forecasting crude oil price volatility and value at risk evidence from historical and recent data abstract this paper adopts the markov switching multifractal msm model and a battery of generalized autoregressive conditional heteroscedasticity garch type models to model and forecast oil price volatility extending previous work by wei et al 2010 and wang et al 2016 we evaluate the forecasting performance of all these models via a superior predictive ability spa test we go beyond previous research by i considering oil price volatility in the nineteenth century along with recent data ii applying different types of msm models and iii considering value at risk predictions besides our forecasting of volatility confirming its successful performance in other studies the new msm model comes out as the model that most often across forecasting horizons and subsamples cannot be outperformed by other models this superiority also applies to forecasting of value at risk

tail dependence and diversification benefits in emerging market stocks an extreme value theory approach this article examines tail dependence the benefits of diversification and the relation between the two for emerging stock markets we find most emerging equity markets are independent in limiting joint extremes however the dependence in finite levels of extremes is still much stronger than the dependence implied by multivariate normality therefore simple correlation analysis can lead to gross underestimation of the chances of joint crashes in multiple markets assuming risk averse investors guarding against extreme losses diversification benefits are measured for each two country optimal portfolio by the reduction in quantile risk measures such as value at risk and expected shortfall relative to an undiversified portfolio it is shown that tail dependence measures developed from multivariate extreme value theory are negatively related to diversification benefits and more importantly can explain diversification benefits better than the correlation coefficient at the most extreme quantiles

the drivers of international migration to the uk a panel based bayesian model averaging approach a heterogeneous panel based bayesian model averaging approach is used to identify the robust economic and policy drivers of international migration to the uk from 14 different source regions over 1980 2007 around three quarters of the increase in net immigration over the last decade which is particularly pronounced for selected source regions is associated with structural change which may be attributed to uk and foreign immigration policy the remainder of the rise is largely explained by friends and family effects there are considerable uncertainties associated both with the effects of particular economic drivers of immigration and with forecasts of immigration

occupational self selection a human capital approach to sex differences in occupational structure a rich and diverse literature exists concerning the distribution of labor incomes one approach namely that of human capital concentrates on lifetime accumulation paths of earnings capacity units human capital individual variations in human capital imply differences in earnings power thereby yielding strong implications concerning earnings distribution within a population despite its explanatory power the human capital model has been widely criticized one criticism centers on its inability to obtain inferences concerning occupational distribution the purpose of this paper is to alleviate at least some such criticism by applying the hedonic price approach so as to embed occupational choice into the human capital framework the significance is that neoclassical economic theory can be used to obtain implications concerning the determinants of occupational structure excerpt

answering the skeptics yes standard volatility models do provide accurate forecasts a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility using arch and stochastic volatility models while most of these studies have documented highly significant in sample parameter estimates and pronounced intertemporal volatility persistence traditional ex post forecast evaluation criteria suggest that the models provide seemingly poor volatility forecasts contrary to this contention we show that volatility models produce strikingly accurate interdaily forecasts for the latent volatility factor that would be of interest in most financial applications new methods for improved ex post interdaily volatility measurements based on high frequency intradaily data are also discussed

interrupted work careers depreciation and restoration of human capital the quantitative effects and even the existence of a human capital depreciation phenomenon have been a subject of controversy in the recent literature prior work however was largely cross sectional and the longitudinal dimension if any was retrospective using longitudinal panel data on married women in nls we have now established that real wages at reentry are indeed lower than at the point of labor force withdrawal and the decline in wages is greater the longer the interruption another striking finding is a relatively rapid growth in wages after the return to work this rapid growth appears to reflect the restoration or repair of previously eroded human capital the phenomenon of depreciation and restoration is also visible in data for immigrants to the united states however while immigrants eventually catch up with and often surpass natives returnees from the nonmarket do not fully restore their earnings potential

developing a stress testing framework based on market risk models the basel 2 accord requires regulatory capital to cover stress tests yet no coherent and objective framework for stress testing portfolios exists we propose a new methodology for stress testing in the context of market risk models that can incorporate both volatility clustering and heavy tails empirical results compare the performance of eight risk models with four possible conditional and unconditional return distributions over different rolling estimation periods when applied to major currency pairs using daily data spanning more than 20 years we find that stress test results should have little impact on current levels of foreign exchange regulatory capital

is volatility clustering of asset returns asymmetric abstract volatility clustering is a well known stylized feature of financial asset returns this paper investigates asymmetric pattern in volatility clustering by employing a univariate copula approach of chen and fan 2006 using daily realized kernel volatilities constructed from high frequency data from stock and foreign exchange markets we find evidence that volatility clustering is highly nonlinear and strongly asymmetric in that clusters of high volatility occur more often than clusters of low volatility to the best of our knowledge this paper is the first one to address and uncover this phenomenon in particular the asymmetry in volatility clustering is found to be more pronounced in the stock markets than in the foreign exchange markets further the volatility clusters are shown to remain persistent for over a month and asymmetric across different time periods our findings have important implications for risk management a simulation study indicates that models which accommodate asymmetric volatility clustering can significantly improve the out of sample forecasts of value at risk

value at risk estimation with the optimal dynamic biofuel portfolio abstract in the past petroleum companies only paid attention to hedging the variation in the crude oil price and volatility however they have now expanded their analysis to encompass renewable sources such as corn and soybeans under the current low carbon biofuel obligations this paper employs garch 1 1 and arji models to estimate the one day ahead value at risk var of the optimal dynamic biofuel portfolio which consists of crude oil corn and soybeans the optimal blended standard is subject to the dual limitations of minimum production costs and the lowest biofuel using requirements our empirical findings confirm that the arji model is more suitable than the garch 1 1 model and further captures the discontinuous jump behavior from the in the sample data the results of out of sample forecasts also are represented that our models play important roles in var estimation and risk management for biofuel portfolio we therefore suggest that the petroleum companies should simultaneously pay attention to jump risk in hedging material costs in the prices of energy related crops

are interest rate options important for the assessment of interest rate risk abstract fixed income options contain substantial information on the price of interest rate volatility risk in this paper we ask if those options will also provide information related to other moments of the objective distribution of interest rates based on dynamic term structure models within the class of affine models we find that interest rate options are useful for the identification of interest rate quantiles two three factor models are adopted and their adequacy to estimate value at risk of zero coupon bonds is tested we find significant difference on the quantitative assessment of risk when options are or not included in the estimation process of each of these dynamic models statistical backtests indicate that bond estimated risk is clearly more adequate when options are adopted although not yet completely satisfactory

evaluating interval forecasts a complete theory for evaluating interval forecasts has not been worked out to date most of the literature implicitly assumes homoskedastic errors even when this is clearly violated and proceed by merely testing for correct unconditional coverage consequently i set out to build a consistent framework for conditional interval forecast evaluation which is crucial when higher order moment dynamics are present the new methodology is demonstrated in an application to the exchange rate forecasting procedures advocated in risk management

semiparametric estimation of multi asset portfolio tail risk abstract when correlations between assets turn positive multi asset portfolios can become riskier than single assets this article presents the estimation of tail risk at very high quantiles using a semiparametric estimator which is particularly suitable for portfolios with a large number of assets the estimator captures simultaneously the information contained in each individual asset return that composes the portfolio and the interrelation between assets noticeably the accuracy of the estimates does not deteriorate when the number of assets in the portfolio increases the implementation is as easy for a large number of assets as it is for a small number we estimate the probability distribution of large losses for the american stock market considering portfolios with ten fifty and one hundred assets of stocks with different market capitalization in either case the approximation for the portfolio tail risk is very accurate we compare our results with well known benchmark models

unexpected tails in risk measurement some international evidence risk management critically depends on the assumptions made about the distribution of stock returns this paper applies extreme value methods to investigate the limiting distribution of the extreme returns of the nikkei225 ftse100 and s p500 indices as well as the indices of some of largest sectors in japan uk and us the results indicate that the much celebrated generalised extreme value distribution does not provide the most accurate description of the minima since the generalised logistic distribution performs better due to its ability to better capture the fat tails of returns the time varying nature of extremes is also confirmed while a simulation exercise adds to the robustness of our results it is also shown that the findings may have important implications for risk models such as var and expected shortfall since risk measures which cannot capture the fatness of tails of the empirical distribution function of returns may lead to serious underestimation of downside risk

the effect of outward investment to china on domestic r d a two hurdle model with endogenous odi outward direct investment odi and domestic r d are interrelated but empirical evidence is affected by the nature of a firm s data which are heavily censored firm data contain a firm s yes no decision to invest in china yes no decision of r d and the decision of r d intensity we thus adopt a two hurdle model and allow the china investment decision to be endogenous in an r d model in order to examine the effect of odi in china on domestic r d investment in taiwan s electronics industry in the model a two equation simultaneous subsystem is formed in which three regression equations are specified a decision of r d intensity and a yes no decision of location to conduct r d together with a yes no decision to invest in china our results indicate that china investment and r d intensity are positively related such that odi in china helps to raise significantly a firm s r d intensity as compared to the estimate if the endogeneity of china investment and the nature of data were not properly accounted

forecasting portfolio value at risk with nonparametric lower tail dependence estimates abstract we propose to forecast the value at risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coefficient of lower tail dependence we compare our proposed method to a conventional copula garch model where the parameter of a clayton copula is estimated via canonical maximum likelihood the superiority of our proposed model is exemplified by analyzing a data sample of nine different bivariate and one nine dimensional financial portfolio a comparison of the out of sample forecasting accuracy of both models confirms that our model yields economically significantly better value at risk forecasts than the competing parametric calibration strategy

liquidity adjusted intraday value at risk modeling and risk management an application to data from deutsche borse abstract this paper develops a high frequency risk measure the liquidity adjusted intraday value at risk livar our objective is to explicitly consider the endogenous liquidity dimension associated with order size by reconstructing the open limit order book of deutsche borse changes in the tick by tick ex ante frictionless return and actual return are modeled jointly the risk related to the ex ante liquidity premium is then quantified our model can be used to identify the impact of ex ante liquidity risk on total risk and to provide an estimation of the var for the actual return at a point in time in our sample liquidity risk can account for up to 32 of total risk depending on order size

real time density forecasts from bayesian vector autoregressions with stochastic volatility central banks and other forecasters are increasingly interested in various aspects of density forecasts however recent sharp changes in macroeconomic volatility including the great moderation and the more recent sharp rise in volatility associated with increased variation in energy prices and the deep global recession pose significant challenges to density forecasting accordingly this paper examines with real time data density forecasts of u s gdp growth unemployment inflation and the federal funds rate from bayesian vector autoregression bvar models with stochastic volatility the results indicate that adding stochastic volatility to bvars materially improves the real time accuracy of density forecasts this article has supplementary material online

rolling sampled parameters of arch and levy stable models in this paper an asymmetric autoregressive conditional heteroskedasticity arch model and a levy stable distribution are applied to some well known financial indices dax30 ftse20 ftse100 and sp500 using a rolling sample of constant size in order to investigate whether the values of the estimated parameters of the models change over time although there are changes in the estimated parameters reflecting that structural properties and trading behaviour alter over time the arch model adequately forecasts the one day ahead volatility a simulation study is run to investigate whether the time variant attitude holds in the case of a generated arch data process revealing that even in that case the rolling sampled parameters are time varying

forecasting quantiles of day ahead electricity load accurate load forecasting plays a crucial role in the decision making process of many market participants but probably is most important for the dispatch planning of an electricity market operator despite the competitive forecast accuracy achieved by existing point forecast models point forecasts can only provide limited information relating to the expected level of future load to account for the uncertainty of future load and provide a more complete picture of the future load conditions for dispatch planning purposes quantile forecasts can be useful this paper proposes a computationally efficient approach to forecasting the quantiles of electricity load which is then applied to forecasting in the national electricity market of australia the proposed model performs competitively in comparison with one industry standard and two recently proposed quantile forecasting methods one of the main advantages of the proposed approach is the ease with the number of covariates can be expanded this is a particularly important feature in the context of load forecasting where large numbers of important drivers are usually necessary to provide accurate load forecasts

comparison of historically simulated var evidence from oil prices abstract cabedo and moya cabedo j d moya i 2003 estimating oil price value at risk using the historical simulation approach energy economics 25 239 253 find that arma with historical simulation delivers var forecasts that are superior to those from garch we compare the arma with historical simulation to the semi parametric garch model proposed by barone adesi et al barone adesi g giannopoulos k vosper l 1999 var without correlations for portfolios of derivative securities journal of futures markets 19 5 583 602 the results suggest that the semi parametric garch model generates var forecasts that are superior to the var forecasts from the arma with historical simulation this is due to the fact that garch captures volatility clustering our findings suggest that cabedo and moya s conclusion is mainly driven by the normal distributional assumption imposed on the future risk structure in the garch model

an empirical model of labor supply in a life cycle setting this paper formulates and estimates a structural intertemporal model of labor supply using theoretical characterizations derived from an economic model of lifetime behavior a two step empirical analysis yields estimates of intertemporal and uncompensated substitution effects which provide the information needed to predict the response of hours of work to life cycle wage growth and shifts in the lifetime wage path

dynamics of credit spread moments of european corporate bond indexes abstract traditional quantitative credit risk models assume that changes in credit spreads are normally distributed but empirical evidence shows that they are likely to be skewed fat tailed and change behaviour over time not taking into account such characteristics can compromise calculation of loss probabilities pricing of credit derivatives and profitability of trading strategies therefore the aim of this study is to investigate the dynamics of higher moments of changes in credit spreads of european corporate bond indexes using extensions of garch type models that allow for time varying volatility skewness and kurtosis of changes in credit spreads as well as a regime switching garch model which allows for regime shifts in the volatility of changes in credit spreads performance evaluation methods are used to assess which model captures the dynamics of observed distribution of the changes in credit spreads produces superior volatility forecasts and value at risk estimates and yields profitable trading strategies the results presented can have significant implications for risk management trading activities and pricing of credit derivatives

do banks overstate their value at risk abstract this paper is the first empirical study of banks risk management systems based on non anonymous daily value at risk var and profit and loss data using actual data from the six largest canadian commercial banks we uncover evidence that banks exhibit a systematic excess of conservatism in their var estimates the data used in this paper have been extracted from the banks annual reports using an innovative matlab based data extraction method out of the 7354 trading days analyzed in this study there are only two exceptions i e days when the actual loss exceeds the disclosed var whereas the expected number of exceptions with a 99 var is 74 for each sample bank we extract from historical vars a risk overstatement coefficient ranging between 19 and 79 we attribute var overstatement to several factors including extreme cautiousness and underestimation of diversification effects when aggregating vars across business lines and or risk categories we also discuss the economic and social cost of reporting inflated vars

modelling asset returns under price limits with mixture of truncated gaussian distribution in this paper we propose to use the mixture of truncated gaussian distribution in modelling the financial asset return distribution under price limits theoretically while retaining many convenie

modeling credit card borrowing a comparison of type i and type ii tobit approaches this study focuses on credit cards as a borrowing medium using 1998 survey of consumer finance data the type ii tobit approach used here overcomes a shortcoming of the conventional type i tobit model which restricts the coefficients of the binary borrowing decision the first step and balance level decision the second step to have the same sign by separately estimating the first and second steps as reduced form functions in which the overall effects of supply and demand are incorporated the type ii tobit procedure yields more consistent results with other studies and quantifies the marginal effect elasticity of a particular determinant for practical uses more than 11 000 bank and nonbank holding companies issued credit cards in 1997 among these firms the top 50 issuers market share is 90 4 and the 10 largest credit card issuers hold 57 3 of total outstanding balances bank holding company revenue from interest on outstanding balances is 57 5 billion which is 77 8 of the total revenue of 73 9 billion moreover about 80 of 102 million u s households own at least one credit card demand for credit cards consists of two components one being the demand as a transaction medium and the other being the demand as a borrowing medium over 68 of bank type credit card holders use their credit cards only as a transaction medium 1 which means that they pay off their balances in full each month cargill and wendel 1996 however other credit card holders use their cards as a source of short term lowamount borrowing or as an instrument for consumption smoothing this study focuses on credit cards as a borrowing medium total household debts have been investigated in other studies relating household debt levels to credit or liquidity constraints using a two step selection approach duca and rosenthal 1993 examine the extent to which borrowing constraints restrict household access to debt and the manner in which lenders vary debt limits across borrowers crook 2001 investigates the determinants of credit constraints and the factors in the amount of household debt with a multistage model however it is necessary to separate the credit card debt from other household debts in the sense that every credit card user could borrow without incurring a transaction cost brito and hartley 1995 provide a theoretical model in which the equilibrium interest rate can be very high and inflexible under the

a new pearson type qmle for conditionally heteroscedastic models this article proposes a novel pearson type quasi maximum likelihood estimator qmle of garch p q models unlike the existing gaussian qmle laplacian qmle generalized non gaussian qmle or lad estimator our pearsonian qmle pqmle captures not just the heavy tailed but also the skewed innovations under strict stationarity and some weak moment conditions the strong consistency and asymptotic normality of the pqmle are obtained with no further efforts the pqmle can be applied to other conditionally heteroscedastic models a simulation study is carried out to assess the performance of the pqmle two applications to four major stock indexes and two exchange rates further highlight the importance of our new method heavy tailed and skewed innovations are often observed together in practice and the pqmle now gives us a systematic way to capture these two coexisting features

roughing it up including jump components in the measurement modeling and forecasting of return volatility a rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high frequency returns coupled with simple modeling procedures building on recent theoretical results in barndorff nielsen and shephard 2004a 2005 for related bi power variation measures the present paper provides a practical and robust framework for non parametrically measuring the jump component in asset return volatility in an application to the dm exchange rate the s p500 market index and the 30 year u s treasury bond yield we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process moreover many jumps appear directly associated with specific macroeconomic news announcements separating jump from non jump movements in a simple but sophisticated volatility forecasting model we find that almost all of the predictability in daily weekly and monthly return volatilities comes from the non jump component our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling forecasting and pricing the continuous and jump components of the total return variation process

a note on the importance of overnight information in risk management models this paper examines the economic value of overnight information to users of risk management models in addition to the information revealed by overseas markets that trade during the domestic overnight period this paper exploits information generated via recent innovations in the structure of financial markets in particular certain securities and associated derivative products can now be traded at any time over a 24 h period as such it is now possible to make use of information generated by trading in almost identical securities during the overnight period of the securities that are available over such time periods s p 500 related products are by far the most actively traded and are therefore the subject of this paper using a variety of conditional volatility models that allow time dependent information flow within and across three different s p 500 markets the results show that overnight information flow has a significant impact on the conditional volatility of daytime traded s p 500 securities moreover time consistent forecasts from models that incorporate overnight information are shown to have economic value to risk managers in particular value at risk var models based on these conditional volatility models are shown to be more accurate than var models that ignore overnight information

time series copulas for heteroskedastic data we propose parametric copulas that capture serial dependence in stationary heteroskedastic time series we suggest copulas for first order markov series and then extend them to higher orders and multivariate series we derive the copula of a volatility proxy based on which we propose new measures of volatility dependence including co movement and spillover in multivariate series in general these depend upon the marginal distributions of the series using exchange rate returns we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models and produce more accurate value at risk forecasts

forecast encompassing tests and probability forecasts we consider tests of forecast encompassing for probability forecasts for both quadratic and logarithmic scoring rules we propose test statistics for the null of forecast encompassing present the limiting distributions of the test statistics and investigate the impact of estimating the forecasting models parameters on these distributions the small sample performance is investigated in terms of small numbers of forecasts and model estimation sample sizes we show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables and for evaluating survey based probability forecasts copyright c 2010 john wiley sons ltd

modeling and forecasting extreme commodity prices a markov switching based extreme value model abstract we propose a markov switching multifractal peaks over threshold msm pot model to capture the dynamic behavior of the random occurrences of extreme events exceeding a high threshold in time series of returns this approach allows introducing changes of regimes in the conditional mean function of the inter exceedance times i e the time between two consecutive extreme events in order to admit the presence of short and long term memory patterns further through its multifractal structure the msm pot approach is able to capture the typical stylized facts of extreme events observed in financial time series such as temporal clustering of the size of exceedances and temporal behavior of tail thickness we compare the performance of the msm pot model with competing self exciting models and a garch evt approach in an in and out of sample var forecasting exercise based on the extreme returns of six daily commodity futures prices i e brent and wti crude oil cocoa cotton copper and gold empirical results suggest that the var estimates generated by the msm pot model for the returns analyzed produce the most accurate forecasts

multivariate density forecast evaluation and calibration in financial risk management high frequency returns on foreign exchange we provide a framework for evaluating and improving multivariate density forecasts among other things the multivariate framework lets us evaluate the adequacy of density forecasts involving cross variable interactions such as time varying conditional correlations we also provide conditions under which a technique of density forecast calibration can be used to improve deficient density forecasts and we show how the calibration method can be used to generate good density forecasts from econometric models even when the conditional density is unknown finally motivated by recent advances in financial risk management we provide a detailed application to multivariate high frequency exchange rate density forecasts

from value at risk to stress testing the extreme value approach this article presents an application of extreme value theory to compute the value at risk of a market position in statistics extremes of a random process refer to the lowest observation the minimum and to the highest observation the maximum over a given time period extreme value theory gives some interesting results about the distribution of extreme returns in particular the limiting distribution of extreme returns observed over a long time period is largely independent of the distribution of returns itself in financial markets extreme price movements correspond to market corrections during ordinary periods and also to stock market crashes bond market collapses or foreign exchange crises during extraordinary periods an approach based on extreme values to compute the var thus covers market conditions ranging from the usual environment considered by the existing var methods to the financial crises which are the focus of stress testing univariate extreme value theory is used to compute the var of a fully aggregated position while multivariate extreme value theory is used to compute the var of a position decomposed on risk factors 2000 elsevier science b v all rights reserved

dynamic return volatility dependence and risk measure of covar in the oil market a time varying mixed copula model abstract this study investigates the risk level in the oil market measured by value at risk var and conditional var covar as well as the dynamic and asymmetric dependence between wti returns and crude oil volatility index ovx by constructing six time varying mixed copula models results show that mixed copula between t copula and the 270 degree rotated clayton copula is the optimal fitting copula to measure dynamic dependence the estimated time varying kendall coefficients indicate that wti returns and ovx present negative dependence most of the time there exists a structural change point of dependence between wti returns and ovx changes on april 17 2009 while the dependence characteristics within the subsamples are similar to that in the whole sample indicating the rationality of our time varying mixed copula models finally the tests show significant risk spillover from ovx to wti returns and also asymmetric effects for covars in response to different upside and downside extreme ovx movements

spot and derivative pricing in the eex power market using spot and futures price data from the german eex power market we test the adequacy of various one factor and two factor models for electricity spot prices the models are compared along two different dimensions 1 we assess their ability to explain the major data characteristics and 2 the forecasting accuracy for expected future spot prices is analyzed we find that the regime switching models clearly outperform its competitors in almost all respects the best results are obtained using a two regime model with a gaussian distribution in the spike regime furthermore for short and medium term periods our results underpin the frequently stated hypothesis that electricity futures quotes are consistently greater than the expected future spot a situation which is denoted as contango

utilization of r d results in the home and foreign plants of multinationals this paper analyzes the utilization of r d results in the home and foreign plants of swedish multinational enterprises mnes the findings indicate that firms r d undertaken in the home country is used as an input in both the home and foreign plants of the mnes this suggests that technology is transferred to the foreign plants around four fifths of the gain in value added attributed to home r d was realized in the mnes home plants while the remaining fifth benefitted the foreign plants r d in foreign affiliates does not appear to be used as an input in home plants

modeling interest rate volatility a realized garch approach abstract we propose using a realized garch rgarch model to estimate the daily volatility of the short term interest rate in the euro yen market the model better fits the data and provides more accurate volatility forecasts by extracting additional information from realized measures in addition we propose using the arma realized garch arma rgarch model to capture the volatility clustering and the mean reversion effects of interest rate behavior we find the arma rgarch model fits the data better than the simple rgarch model does but it does not provide superior volatility forecasts

alternative models of earnings determination and labor market structures there are three distinct research traditions in the analysis of individual earnings determination human capital or earnings function analyses aggregate wage analyses and labor demand analyses an important and incongruous aspect of each is the treatment of geographical differences in labor markets this paper first investigates the magnitude and character of geographical wage differentials the sizable differences discovered there are then related to the existing and highly simplified models of labor market differences while the two major classes of models compensating differentials and labor demand differ significantly in assumptions and implications it is impossible to distinguish adequately between them there appears to be a clear need for more structural analyses of labor market operations

inference on self exciting jumps in prices and volatility using high frequency measures summary dynamic jumps in the price and volatility of an asset are modelled using a joint hawkes process in conjunction with a bivariate jump diffusion a state space representation is used to link observed returns plus nonparametric measures of integrated volatility and price jumps to the specified model components with bayesian inference conducted using a markov chain monte carlo algorithm an evaluation of marginal likelihoods for the proposed model relative to a large number of alternative models including some that have featured in the literature is provided an extensive empirical investigation is undertaken using data on the s p 500 market index over the 1996 2014 period with substantial support for dynamic jump intensities including in terms of predictive accuracy documented copyright 2016 john wiley sons ltd

location and the multinational enterprise a neglected factor this article first traces the changing world economic scenario for international business over the past two decades and then goes on to examine its implications for the location of foreign direct investment and multinational enterprise activity it suggests that many of the explanations of the 1970s and early 1980s need to be modified as firm specific assets have become mobile across natural boundaries a final section of the article examines the dynamic interface between the value added activities of multinational enterprises in different locations

continuous cascade models for asset returns abstract in this paper we make a short overview of continuous cascade models recently introduced to model asset return fluctuations we show that these models account in a very parcimonious manner for most of stylized facts of financial time series we review in more details the simplest continuous cascade namely the log normal multifractal random walk mrw it can simply be considered as a stochastic volatility model where the log volatility memory has a peculiar logarithmic shape this model possesses some appealing stability properties with respect to time aggregation we describe how one can estimate it using a gmm method and we present some applications to volatility and var value at risk forecasting

the interdependence between fdi and r d an application of an endogenous switching model to taiwan s electronics industry foreign direct investment fdi and research development r d are mutually dependent and should be treated as endogenous variables in empirical studies an endogenous switching regression model is used to examine the mutual effect of fdi and r d in taiwan s electronics industry the empirical results show that fdi and r d are positively related and do reinforce each other unbiased coefficients are obtained as they are compared to those estimates if fdi and r d are treated as exogenous variables the results have a strong public policy implication for taiwan s foreign direct investment and can be further used to estimate the difference in r d expenditures between fdi and non fdi firms

model risk and capital reserves abstract we propose a procedure to take model risk into account in the computation of capital reserves this addresses the need to make the allocation of capital reserves to positions in given markets dependent on the extent to which reliable models are available the proposed procedure can be used in combination with any of the standard risk measures such as value at risk and expected shortfall we assume that models are obtained by usual econometric methods which allows us to distinguish between estimation risk and misspecification risk we discuss an additional source of risk which we refer to as identification risk by way of illustration we carry out calculations for equity and fx data sets in both markets estimation risk and misspecification risk together explain about half of the multiplication factors employed by the bank for international settlements bis

tail risk in emerging markets of southeastern europe abstractthis article examines the left tail behaviour of returns on stocks in southeastern europe see we apply conditional extreme value theory evt approach on daily returns of six stock market indices from see between 2004 and 2013 predictive performance of value at risk var and expected shortfall es based on evt is compared against several alternatives such as historical simulation and analytical approach based on garch with a single conditional distribution model backtesting with daily returns shows that evt based models provide more reliable var and es forecasts than the alternative models in all six markets unlike the alternatives the evt based models cannot be rejected as var confidence level is increased this emphasizes the importance of extreme events in see markets and indicates that the ability of a model to capture volatility clustering accurately is not sufficient for a correct assessment of risk in these markets

the economics of schooling production and efficiency in public schools n recent years public and professional interest in schools has been heightened by a spate of reports many of them critical of current school policy these policy documents have added to persistent and long standing concerns about the cost effectiveness and fairness of the current school structure and have made schooling once again a serious public issue as in the past however any renewed interest in education is likely to be short lived doomed to dissipate as frustration over the inability of policy to improve school practice sets in this frustration about school policy relates directly to knowledge about the educational production process and in turn to underlying research on schools although the educational process has been extensively researched clear policy prescriptions flowing from this research have been difficult to derive 2 there exists however a consistency to the research findings that does have an immediate application to school policy schools differ dramatically in quality

forecasting daily supermarket sales using exponentially weighted quantile regression inventory control systems typically require the frequent updating of forecasts for many different products in addition to point predictions interval forecasts are needed to set appropriate levels of safety stock the series considered in this paper are characterised by high volatility and skewness which are both time varying these features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions the widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting in this paper we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression the approach amounts to exponential smoothing of the cumulative distribution function and can be viewed as an extension of generalised exponential smoothing to quantile forecasting empirical results are encouraging with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting

forecasting the joint distribution of australian electricity prices using dynamic vine copulae abstract we consider the problem of modelling and forecasting the distribution of a vector of prices from interconnected electricity markets using a flexible class of drawable vine copula models where we allow the dependence parameters of the constituting bivariate copulae to be time varying we undertake in sample and out of sample tests using daily electricity prices and evidence that our model provides accurate forecasts of the underlying distribution and outperforms a set of competing models in their abilities to forecast one day ahead conditional quantiles of a portfolio of electricity prices our study is conducted in the australian national electricity market nem which is the most efficient power auction in the world electricity prices exhibit highly stylised features such as extreme price spikes price dependency between regional markets correlation asymmetry and non linear dependency the developed approach can be used as a risk management tool in the electricity retail industry which plays an integral role in the apparatus of modern energy markets electricity retailers are responsible for the efficient distribution of electricity while being exposed to market risk with extreme magnitudes

value at risk estimations of energy commodities via long memory asymmetry and fat tailed garch models abstract in this paper we evaluate the value at risk var and the expected shortfalls for some major crude oil and gas commodities for both short and long trading positions classical var estimations as well as riskmetrics and other extensions to cases considering for long range memory asymmetry and fat tail in energy markets volatility were conducted we computed the var for three arch garch type models including figarch fiaparch and hygarch these models were estimated in the presence of three alternative innovation s distributions normal student and skewed student our results show that considering for long range memory fat tails and asymmetry performs better in predicting a one day ahead var for both short and long trading positions moreover the fiaparch model outperforms the other models in the var s prediction these results present several potential implications for energy markets risk quantifications and hedging strategies

time series analysis for financial market meltdowns abstract there appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling more specifically it is alleged that current risk models have failed to properly assess the risks associated with large adverse stock price behavior in this paper we first discuss the limitations of classical time series models for forecasting financial market meltdowns then we set forth a framework capable of forecasting both extreme events and highly volatile markets based on the empirical evidence presented in this paper our framework offers an improvement over prevailing models for evaluating stock market risk exposure during distressed market periods

modelling interregional links in electricity price spikes abnormally high price spikes in spot electricity markets represent a significant risk to market participants as such a literature has developed that focuses on forecasting the probability of such spike events moving beyond simply forecasting the level of price many univariate time series models have been proposed to dealwith spikes within an individual market region this paper is the first to develop a multivariate self exciting point process model for dealing with price spikes across connected regions in the australian national electricity market the importance of the physical infrastructure connecting the regions on the transmission of spikes is examined it is found that spikes are transmitted between the regions and the size of spikes is influenced by the available transmission capacity it is also found that improved risk estimates are obtained when inter regional linkages are taken into account

daily exchange rate behaviour and hedging of currency risk textabstractwe construct models which enable a decision maker to analyze the implications of typical time series patterns of daily exchange rates for currency risk management our approach is bayesian where extensive use is made of markov chain monte carlo methods the effects of several model characteristics unit roots garch stochastic volatility heavy tailed disturbance densities are investigated in relation to the hedging strategies consequently we can make a distinction between statistical relevance of model specifications and the economic consequences from a risk management point of view we compute payoffs and utilities from several alternative hedge strategies the results indicate that modelling time varying features of exchange rate returns may lead to improved hedge behaviour within currency overlay management

innovation foreign direct investment and growth direct foreign investment is incorporated in a dynamic general equilibrium model with endogenous technological change in contrast to recent endogenous growth approaches i allow for geographical separation of the innovation and production of newly developed goods firms acquire specific knowledge through r d investment in the more developed country and use their specific asset to establish a production plant in the low cost country foreign direct investment is accompanied by interregional spillovers of knowledge from the more to the less advanced country i derive a steady state equilibrium with active innovation and production activities in the high technology sector in both countries furthermore the implications of factor flow liberalization as well as of industrial policies are investigated

a unified approach to investigate pure and wake up call contagion evidence from the eurozone s first financial crisis abstract this paper simultaneously analyzes wake up call and pure contagion of sovereign risk in the eurozone during its recent financial crisis pure contagion of sovereign risk means the transmission of negative effects after a shock to a country which are not reflected in the risk pricing of fundamental determinants of sovereign risk of the recipient country wake up call contagion is defined as the change of sovereign risk pricing by market participants after negative events in a single country or a group of countries to examine both types of contagion in a unified framework we apply an extension of the canonical model for contagion proposed by pesaran and pick 2007 and metiu 2012 in that we allow for time varying coefficients controlling for changes in the risk pricing by investors we detect several channels of pure contagion between 2008 and 2012 but with decreasing number over time further we find evidence for a disruption of sovereign risk contagion channels from greece ireland and portugal to spain italy france and belgium after their respective bailouts as was desired by policymakers for all eurozone countries considered we observe an increase in the relevance of general risk aversion towards sovereign debt since may 2010 our model extension yields a device that is suitable to determine whether policy interventions are required and to judge their success ex post

capturing deep tail risk via sequential learning of quantile dynamics abstract this paper develops a conditional quantile model that can learn long term and short term memories of sequential data it builds on sequential neural networks and yet outputs interpretable dynamics we apply the model to asset return time series across eleven asset classes using historical data from the 1960s to 2018 our results reveal that it extracts not only the serial dependence structure in conditional volatility but also the memories buried deep in the tails of historical prices we further evaluate its value at risk forecasts against a wide range of prevailing models our model outperforms the garch family as well as models using filtered historical simulation conditional extreme value theory and dynamic quantile regression these studies indicate that conditional quantiles of asset return have persistent sources of risk that are not coming from those responsible for volatility clustering these findings could have important implications for risk management in general and tail risk forecasts in particular

the relationship between r d and physical investment of firms in science based industries this study is motivated by the one way inducement hypothesis that r d induces granger causes physical investment but physical investment does not induce r d raised by lach and schankerman 1989 and lach and rob 1996 it is demonstrated that their results do not hold after extending the sample that matches their criteria by including more time periods and or more firms namely it is found that the granger causality between r d and physical investment occurs both ways it is also shown that their contemporary relationship derived by dynamic simultaneous expressions is positive in both ways from current r d to current physical investment and from current physical investment to current r d moreover previous r d affects current physical investment and previous physical investment also affects current r d

recent advances in electricity price forecasting a review of probabilistic forecasting abstract since the inception of competitive power markets two decades ago electricity price forecasting epf has gradually become a fundamental process for energy companies decision making mechanisms over the years the bulk of research has concerned point predictions however the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply demand and prices academics and practitioners alike have come to understand that probabilistic electricity price and load forecasting is now more important for energy systems planning and operations than ever before with this paper we offer a tutorial review of probabilistic epf and present much needed guidelines for the rigorous use of methods measures and tests in line with the paradigm of maximizing sharpness subject to reliability the paper can be treated as an update and a further extension of the otherwise comprehensive epf review of weron 1 or as a standalone treatment of a fascinating and underdeveloped topic that has a much broader reach than epf itself

outward direct investment by indonesian firms motivation and effects as part of indonesia s opening to the world economy over the past several years indonesian firms have begun to invest abroad this study compares indonesian firms that have invested abroad and those that have not and with third world multinationals and other mnes it found that the performance of indonesian firms that invested abroad improved dramatically after their investment in terms of management expertise exports quality and costs relative to their past performance and relative to the other firms in the sample these results support a conclusion that indonesian multinationals have gone abroad not only to exploit their ownership advantages but also to access and develop ownership advantages they did not previously possess

technology sourcing versus technology exploitation an analysis of us foreign direct investment flows the traditional paradigm of foreign direct investment fdi suggests that fdi is undertaken principally to exploit some firm specific advantage in a foreign country which provides a locational advantage to the investor however recent theoretical work suggests a model of fdi in which the motivation is not to exploit existing technological advantages in a foreign country but to access such technology and transfer it from the host economy to the investing multinational corporation via spillover effects this paper tests the technology sourcing versus technology exploiting hypotheses for a panel of sectoral fdi flows between the united states and major oecd nations over a 15 year period the research makes use of patel and vega s research policy 28 145 55 1999 taxonomy of sectors which are likely a priori to exhibit technology sourcing and exploiting behaviour respectively while there is evidence that fdi flows into the united states are attracted to r and d intensive sectors very little support is found for the technology sourcing hypothesis either for inward or outward fdi flows the results suggest that in aggregate firm specific ownership effects remain powerful determinants of fdi flows

energy risk management through self exciting marked point process abstract crude oil is a dynamically traded commodity that affects many economies we propose a collection of marked self exciting point processes with dependent arrival rates for extreme events in oil markets and related risk measures the models treat the time among extreme events in oil markets as a stochastic process the main advantage of this approach is its capability to capture the short medium and long term behavior of extremes without involving an arbitrary stochastic volatility model or a prefiltration of the data as is common in extreme value theory applications we make use of the proposed model in order to obtain an improved estimate for the value at risk in oil markets empirical findings suggest that the reliability and stability of value at risk estimates improve as a result of finer modeling approach this is supported by an empirical application in the representative west texas intermediate wti and brent crude oil markets

expected interruptions in labor force participation and sex related differences in earnings growth the paper analyzes the joint determination of wives earnings and labor force participation over the life cycle given the interruptions in wives work careers the interruptions affect the profitability of the investment in human capital which in turn determines earnings the earnings prospects feed back into the participation decision namely the decision whether and for how long to drop out of the labor force the formal analysis compares the age earnings profiles of persons who drop out of the labor force with those who do not during the pre and post interruption period the comparison is carried out where interruptions are assumed to be exogenous and when they are endogenous the effect of productivity at home the initial stock of human capital and its rental value on the length of the interruption is investigated this abstract was borrowed from another version of this item

patterns and strategies of foreign direct investment the case of japanese firms this paper is to study globalization motives and strategies of japanese manufacturing industries by analyzing the causes and patterns of foreign direct investment fdi of japanese manufacturing firms we use regression analysis to determine internally driving out factors and externally inducing factors japanese fdi strategy has gone through three different stages from natural resource seeking investment in the 1950s and 1960s to market expansion investment in the 1970s and 1980s and to a combination of cost reducing low cost labor seeking investment and market penetrating investment in the 1990s our findings show that japanese fdi in asia and other developing countries tends to be in labor intensive sectors where japanese firms are losing their comparative advantages at home the main motive for fdi into these regions is low cost resource seeking on the other hand japanese fdi in the us and europe tends to be knowledge intensive sectors where japanese firms attempt to internalize transaction and i

is there evidence of pessimism and doubt in subjective distributions implications for the equity premium puzzle abel 2002 shows that pessimism and doubt in the subjective distribution of the growth rate of consumption reduce the equity premium puzzle we quantify the amount of pessimism and doubt in survey data on us consumption and income individual forecasters are in fact pessimistic but show marked overconfidence rather than doubt however the implications for abel s model depend on how the empirically heterogeneous beliefs are mapped into beliefs of a representative agent we use an arrow debreu economy to show that disagreement increases the equity premium when incorporating this in our estimation we find little empirical evidence of either overconfidence or doubt

forecasting the var of crude oil market do alternative distributions help abstract accurate modeling of the empirical distribution of crude oil market returns is extremely important in estimating risk measures in addition to several commonly used distributions alternative distributions are explored in this study some of which account for the asymmetry and heavy tails simultaneously found in distributions and contain more tail parameters to separately depict the right and left tails when forecasting the value at risk var of crude oil markets during highly volatile periods seven backtests are also conducted to compare the var forecasting accuracy among different distributions the empirical results indicate that a highly volatile environment challenges the commonly used distributions and the four risk models based on commonly used distributions are rejected about 27 to 38 of the time the alternative distributions i e skewed general error distributions sged generalized hyperbolic skewed student t distributions ghst and generalized asymmetric student t gast distributions generally produce more accurate var measurement and gast gives the best measurement accuracy the empirical results imply that risk managers or policymakers should further consider more flexible distributions such as sged ghst or gast in particular when quantifying or managing the risk in turbulent market times

backtesting trading risk of commercial banks using expected shortfall this paper uses saddlepoint technique to backtest the trading risk of commercial banks using expected shortfall it is found that four out of six us commercial banks have excessive trading risks monte carlo simulation studies show that the proposed backtest is very accurate and powerful even for small test samples more importantly risk managers can carry out the proposed backtest based on any number of exceptions so that incorrect risk models can be promptly detected before any further huge losses are realized

estimating value at risk under a heath jarrow morton framework with jump this article proposes a new methodology for measuring value at risk hereafter var using a model that incorporates both volatility and jumps heath jarrow morton hjm model has been used for the valuation of interest rate derivatives this study extends the use of hjm model to the estimation var this article specifically uses a two factor hjm jump diffusion model for the computation the study models the eurodollar futures prices using its derivatives in addition this article uses a new volatility specification of ze to 2002 to construct the hjm dynamics the result indicates that the var model using hjm jump diffusion framework performs well in capturing the nonnormality and in providing accurate var forecasts in the in sample and out sample tests

caviar based forecast for oil price risk abstract as a benchmark for measuring market risk value at risk var reduces the risk associated with any kind of asset to just a number amount in terms of a currency which can be well understood by regulators board members and other interested parties this paper employs a new var approach due to engle and manganelli engle r f manganelli s 2004 caviar conditional autoregressive value at risk by regression quantiles journal of business and economic statistics 22 367 381 to forecasting oil price risk in doing so we provide two original contributions by introducing a new exponentially weighted moving average caviar model and developing a mixed data regression model for multi period var prediction

further evidence on japanese direct investment in u s manufacturing this paper examines the cross industry determinants of the importance of japanese direct investment activities in u s manufacturing through an extension of the analysis in a recent article by bruce kogut and sea jin chang 1991 the results indicate significant positive roles for japanese technology and marketing assets while u s technology assets are insignificant a significant negative effect for u s marketing assets suggests that entry barriers related to marketing are more important than any use of foreign direct investment by japanese firms to access these assets in addition u s government policies toward defense oriented industries appear to act as a deterrent to japanese direct investment copyright 1996 by mit press

evaluation of usda interval forecasts of corn and soybean prices usda world agricultural supply and demand estimates wasde price forecasts are published as an interval but are typically analyzed as point estimates thus all information about uncertainty imbedded in the forecast is ignored the purpose of this article is to evaluate the accuracy of wasde corn and soybean price forecasts using methodology suitable for testing judgmental interval forecasts accuracy tests suggest that wasde forecasts are not calibrated at the 95 confidence level for both commodities and generally not calibrated for corn but calibrated for soybeans at the implied confidence level elicited from the survey of forecast providers

to invest or not to invest in china this research used two samples of enterprises 123 that invested in china and 213 that did not to examine why many taiwanese enterprises stay home despite others that have invested in china our results found that most taiwanese enterprises are pushed to invest abroad by increasing wages at home and increasing competition in the export market which lowers their profitability the key factors determining to invest or not to invest in china are whether they are satisfied with their profits and whether they are able to upgrade organizational capability if they are not satisfied with the profits however this conclusion does not totally apply to large enterprises for large enterprises their investment in china has little to do with profitability and r d intensity but more to do with export competition and technological capability furthermore investment in southeast asia is complementary to investment in china for large enterprises but a trade off for small and medium ones

the drivers of foreign direct investment into research and development an empirical investigation this paper examines the determinants of foreign direct investment fdi in research and development laboratories by 32 multinational enterprises in the pharmaceutical and electronics industries the paper applies a dichotomous set of motives for fdi results from an econometric analysis of 136 laboratory investments show that relative market size and relative strength of a country s science base determine whether fdi in research and development is carried out in order to exploit existing firm specific advantages or in order to build up new firm specific advantages this holds true in similar form for japanese european and u s firms and across the two industries

improving the value at risk forecasts theory and evidence from the financial crisis abstract the recent financial crisis has raised numerous questions about the accuracy of value at risk var as a tool to quantify extreme losses in this paper we develop data driven var approaches that are based on the principle of optimal combination and that provide robust and precise var forecasts for periods when they are needed most such as the recent financial crisis within a comprehensive comparative study we provide the latest piece of empirical evidence on the performance of a wide range of standard var approaches and highlight the overall outperformance of the newly developed methods

extreme value theory in finance a survey extreme value theory is concerned with the study of the asymptotic distribution of extreme events that is to say events which are rare in frequency and huge in magnitude with respect to the majority of observations statistical methods derived from it have been employed increasingly in finance especially for risk measurement this paper surveys some of those main applications namely for testing different distributional assumptions for the data for value at risk and expected shortfall calculations for asset allocation under safety first type constraints and for the study of contagion and dependence across markets under conditions of stress

point process models for extreme returns harnessing implied volatility forecasting the risk of extreme losses is an important issue in the management of financial risk there has been a great deal of research examining how option implied volatilities iv can be used to forecast asset return volatility however the role of iv in the context of predicting extreme risk has received relatively little attention the potential benefit of iv in forecasting extreme risk is considered within a range of models beginning with the traditional garch based approach along with a number of novel point process models univariate models where iv is included as an exogenous variable are considered along with a novel bivariate approach where extreme movements in iv are treated as another point process it is found that in the context of forecasting value at risk the bivariate models produce the most accurate forecasts across a wide range of scenarios

how relevant is volatility forecasting for financial risk management it depends if volatility fluctuates in a forecastable way volatility forecasts are useful for risk management hence the interest in volatility forecastability in the risk management literature volatility forecastability however varies with horizon and different horizons are relevant in different applications moreover existing assessments of volatility forecastability are plagued by the fact that they are joint assessments of volatility forecastability and an assumed model and the results can vary not only with the horizon but also with the assumed model to address this problem we develop a model free procedure for assessing volatility forecastability across horizons perhaps surprisingly we find that volatility forecastability decays quickly with horizon volatility forecastability although clearly of relevance for risk management at the short horizons relevant for say trading desk management may be much less important at longer horizons

multinomial var backtests a simple implicit approach to backtesting expected shortfall under the fundamental review of the trading book frtb capital charges for the trading book are based on the coherent expected shortfall es risk measure which show greater sensitivity to tail risk in this paper it is argued that backtesting of expected shortfall or the trading book model from which it is calculated can be based on a simultaneous multinomial test of value at risk var exceptions at different levels an idea supported by an approximation of es in terms of multiple quantiles of a distribution proposed in emmer et al 2015 by comparing pearson nass and likelihood ratio tests lrts for different numbers of var levels n it is shown in a series of simulation experiments that multinomial tests with n 4 are much more powerful at detecting misspecifications of trading book loss models than standard bi nomial exception tests corresponding to the case n 1 each test has its merits pearson offers simplicity nass is robust in its size properties to the choice of n the lrt is very powerful though slightly over sized in small samples and more computationally burdensome a traffic light system for trading book models based on the multinomial test is proposed and the recommended procedure is applied to a real data example spanning the 2008 financial crisis