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

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

day of the week effects and asset returns the number of empirical studies using daily stock returns is rapidly increasing researchers generally assume that the distribution of stock returns is identical for all days of the week a convenient statistical assumption but not a necessary condition of market equilibrium nevertheless there are reasons to suspect that the distribution of returns may vary according to the day of the week the most obvious is the impact of weekends on monday s return since monday s return is calculated over three instead of one calender day the mean and variance may well be higher on monday compared with any other daily return perhaps three times as large previous research has examined the daily distribution of stock returns for a monday effect while fama 1965 does not compare daily mean returns he does report that monday s variance is about 20o greater than other daily returns with a traditional distributional assumption regarding the returns on a financial asset specifies that the expected returns are identical for all days of the week contrary to this plausible assumption this paper discovers that the expected returns on common stocks and treasury bills are not constant across days of the week the most notable evidence is for monday s returns where the mean is unusually low or even negative several explanations of the results are investigated but none proves satisfactory aside from documenting significant day of the week effects the implications of the results for tests of market efficiency are examined while market adjusted returns continue to exhibit day of the week effects these effects are no longer concentrated on monday during the last year we have had many conversations with our colleagues throughout the professions we are particularly thankful to the participants at sessions of the western and american finance associations where preliminary versions of this paper were presented special thanks are due to gene fama gailen hite jon ingersoll robert jarrow merton miller clifford smith and rex thompson the expert research assistance of frank rabinovitch and youngsup yun is gratefully acknowledged 1 the list of papers using daily stock returns is too long to name recent examples include charest 1978 dodd 1980 banz 1981 and reinganum 1981

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

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

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

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

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

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

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

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

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

the effect of value line investment survey rank changes on common stock prices abstract the information content of value line investment survey rank changes is investigated the results suggest rank changes affect common stock prices but the effect varies by the type of rank change changes from rank 2 to rank 1 have the most dramatic impact on prices a cross sectional analysis finds small firms have a greater reaction to a rank change than larger firms which supports theories on the frequency of report arrival and precision of information a speed of adjustment test concludes the prices of individual securities adjust to the information in a rank change over a multiple day period

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

testing an aggressive investment strategy using value line ranks a comment in the june 1981 journal of finance clark holloway reviews returns on a value line investments service vl market portfolio over the past fourteen years although he concludes vl does indeed have the ability to choose good stocks he is careful not to present that conclusion explicitly as an indictment of the efficient markets hypothesis holloway s hesitation is appropriate since the results of this and similar articles about value line are not inconsistent with efficient markets in the article holloway observes that the probability of value line s success by mere chance is only 0286 that observation is a misuse of statistics value line is only one of a number of investment advisory services it was not selected ex ante for this experiment it was selected precisely because it has been successful that value line has put together a string of market successes from 1965 to 1978 is no more significant than the fact that there are always a few gamblers ahead of the house in las vegas if in efficient markets chances of success are about one in two the probability of a randomly selected investment advisory service suffering only three bad years out of 14 is indeed 0286 but out of a population of say 20 investment advisory services the probability of finding at least one with three or less bad years out of 14 is 44 in fact it would only take 24 investment services to give you a betterthan even chance of finding at least one as good as value line that the firm would want to advertise that success is certainly no surprise nor is it surprising that a number of businessmen and academics would notice how well value line has performed against the average but holloway and others have only focused on a firm in the tail of a standard binomial distribution and that doesn t contradict the existence of the distribution itself over 14 years an advisory service in the other tail 3 out of 14 losers will probably have gone out of business whatever clark holloway s intention his results are likely to be misunderstood this is not evidence of a capability systematically to beat the market the statistical experiment is wrongly conceived but if value line performs equally well in the future i promise to discard my faith in efficient markets

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

security price reactions to initial reviews of common stock by the value line investment survey this study examines daily price reactions to initial reviews of securities by the value line investment survey the reviews are found to convey information to the market as significant abnormal returns are found over a 3 day period around release of the information furthermore there is no statistically significant subsequent price reation after this 3 day period consistent with market efficiency

caviar conditional autoregressive value at risk by regression quantiles value at risk var has become the standard measure of market risk employed by financial institutions for both internal and regulatory purposes var is defined as the value that a portfolio will lose with a given probability over a certain time horizon usually one or ten days despite its conceptual simplicity its measurement is a very challenging statistical problem and none of the methodologies developed so far give satisfactory solutions interpreting the var as the quantile of future portfolio values conditional on current information we propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies such as normality or i i d returns the conditional autoregressive value at risk or caviar model moves the focus of attention from the distribution of returns directly to the behavior of the quantile we specify the evolution of the quantile over time using a special type of autoregressive process and use the regression quantile framework introduced by koenker and bassett to determine the unknown parameters since the objective function is not differentiable we use a differential evolutionary genetic algorithm for the numerical optimization utilizing the criterion that each period the probability of exceeding the var must be independent of all the past information we introduce a new test of model adequacy the dynamic quantile test applications to simulated and real data provide empirical support to this methodology and illustrate the ability of these algorithms to adapt to new risk environments

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

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

quantifying market risk with value at risk or expected shortfall consequences for capital requirements and model risk the basel committee on banking supervision recently proposed fundamental changes in the regulatory treatment of financial institutions trading book positions among others a replacement of value at risk 0 99 by expected shortfall 0 975 for the quantification of market risk is recommended while this increases capital requirements for heavy tailed risks its consequences for model risk related to the estimation process have not been explored hence the aim of this paper is to analyze how both risk measures react to different sources of model risk in order to better understand the impact of the intended change in risk measures our results show that the expected shortfall 0 975 is more sensitive towards regulatory arbitrage and parameter misspecification we find that this is based on a trade off between a model s ability to better capture the heavy tailed behavior of risks and a higher vulnerability to model risk these new aspects should be taken into account in the regulatory decision for expected shortfall 0 975

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

the value line enigma extended an examination of the performance of option recommendations the authors extend the research on the value line enigma by examining the performance of call recommendations in value line options d galai s hedge decomposition procedure identifies the components of the calls returns abnormal call returns were most pronounced immediately following the purchase which is consistent with studies of value line s stock rankings the largest and most significant abnormal performance was by calls assigned the highest rank written on stocks judged by value line to be correctly priced abnormal call return performance by joint call and stock ranks was consistent with hypothesis that value line identifies underpriced call options copyright 1993 by university of chicago press

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

the informative role of the value line investment survey evidence from stock highlights i examine abnormal stock returns associated with stock highlights published by the value line investment survey at the time of their publication stock highlights elicit strong positive abnormal returns they also have positive abnormal returns at the time of the earnings announcement preceding stock highlight publications post earnings announcement drift is present but is much too small to explain abnormal returns at the time of the publication of stock highlights thus value line stock highlights provide useful information to investors this information is rapidly reflected in stock prices consistent with market efficiency

stock price reactions to the wall street journal s securities recommendations this paper examines the impact of the heard on the street hots column of the wall street journal on common stock prices the results of the study indicate that the hots column appears to have an impact on stock prices on the publication day however we also find a smaller but statistically significant impact on two days preceding the pub lication the significant abnormal returns on these days are associated with higher trading volume the reaction of stock prices is symmetric with respect to the buy and sell recom mendations and the impact of single company recommendations is greater than the im pact of the multi company recommendations i introduction the efficient market hypothesis assumes that security prices fully and instantaneously reflect all available information considerable evidence has been accumulated during the past decades in support of the hypothesis the acceptance of the hypothesis has raised questions about the economic value of professional investment advice if the market is efficient then the management of port folios based on these recommendations should not consistently outperform the market why then are investors still willing to pay for this information examples of the advice include low cost financial publications brokerage house rec ommendations and subscription financial newsletters that sell for hundreds of dollars recent studies argue that investment advisors can provide information to investors at a lower cost than the investors cost of information production millon and thakor 1985 develop a model that explains the existence of investment advisory agencies they argue that if the information of investment advisory agencies permits security analysts to share information about common uncertainties that affect the value of firms as a whole such as the return on the market portfolio then investment advisory agencies can produce information more cheaply than individual investors this model is consistent with the study of

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

market capitalization and value at risk abstract the potential of economic variables for financial risk measurement is an open field for research this article studies the role of market capitalization in the estimation of value at risk var we test the performance of different var methodologies for portfolios with different market capitalization we perform the analysis considering separately financial crisis periods and non crisis periods we find that var methods perform differently for portfolios with different market capitalization for portfolios with stocks of different sizes we obtain better var estimates when taking market capitalization into account we also find that it is important to consider crisis and non crisis periods separately when estimating var across different sizes this study provides evidence that market fundamentals are relevant for risk measurement

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

a note on testing an aggressive investment strategy using value line ranks one of the better known investment advisory services is the value line investment survey an unresolved debate has been in progress in the literature for a decade as to whether the performance of the value line investment service is inconsistent with the efficient market hypothesis for example black 2 states that the vl system can produce extra returns even when transaction costs are considered but he does not report his exact test procedure or any quantitative results of his study on the other hand kaplan and weil 10 11 do not believe that vl can generate abnormal returns we shall show in the following sections that the vl system represents a borderline case when transaction costs are neglected abnormal returns are obtained when realistic transaction costs are used active trading according to vl recommendations does not yield abnormal returns for a buy and hold policy however vl recommendations do yield abnormal returns even when transaction costs are considered

improving daily value at risk forecasts the relevance of short run volatility for regulatory quality assessment abstract in this paper we present a novel perspective on data filtering and present an innovative wavelet based approach that leads to improved value at risk var forecasts a separation of financial conditional volatility into short mid and long run components allows us to study the relevance of these frequency components with respect to a regulatory quality assessment for daily var forecasts a simulation study and an analysis of daily market prices suggest that short and mid run information components cover the relevant information that is necessary for estimating adequate daily var excluding long run information components reduces daily var forecasts by up to 4 and does not impact the quality of regulatory back testing

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

new findings regarding day of the week returns over trading and non trading periods a note this paper decomposes daily close to close returns into trading day and non trading day returns we discover that all of the average negative returns from friday close to monday close documented in the literature for stock market indexes occurs during the non trading period from friday close to monday open in addition average trading day returns open to close are identical for all days of the week january firm size turn of the year anomalies are shown to be interrelated with day of the week returns

on corporate governance a study of proxy contests abstract this paper examines a sample of firms experiencing proxy contests for seats on their board of directors dissident shareholders usually fail to obtain a majority of board seats nevertheless they capture some seats via mechanisms such as cumulative voting in over half of the sample contests regardless of proxy contest outcome positive and statistically significant share price performance is associated with the contest that finding is predicted by the standard economic analysis of proxy contests in which the challenges benefit shareholders by improving corporate performance the finding runs counter to the claim of berle 1962 that the economists view of proxy contests is a wholly imaginary picture a portion of the positive share price changes taking place in the early stages of some proxy contests is not permanent however and as suggested by manne 1962 is at least partially attributable to temporary increases in the market value of the vote attached to corporate shares

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

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

has government any role in money abstract recent interest in monetary reform has been sparked by the emergence of a world irredeemable paper money system in light of this interest we review the current validity of four good reasons friedman advanced in 1960 to rationalize government intervention we conclude that the forces that produced government involvement in the past will persist deregulation of financial intermediaries is desirable on grounds of market efficiency though it is an open question whether government should continue as lender of last resort we expect that the present world fiat money standard will neither degenerate into hyperinflation nor revert to a commodity standard

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

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

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

testing an aggressive investment strategy using value line ranks a comment recently in this journal clark holloway employed a novel and interesting approach to testing for superior performance 1 however there appear to be errors in his methodology and in the inferences he draws from his evidence holloway holds that a buy and hold policy based on value line vl recommendations yields abnormal returns even when transaction costs are considered he calculates that the annual compound rate of return of vl rank i stocks was 11 3 percent for the 1965 78 period and indeed this is the result reported by vl when dividends are not included holloway then applies a 2 percent one way commission up to may 1975 and 1 25 percent thereafter he reports that the annual abnormal return after commissions was found to be 8 6 percent italics added p 711 this abnormal return is presumably the amount the compound rate of return of rank i stocks exceeds the compound rate of the market allvl stocks prior to an adjustment for risk it is obviously too large to be correct and except for his repeating the same figure in his conclusion p 718 one would assume it is a typographical error for if we adjust rank i returns with his own commission adjustment equation p 711 a terminal value tv per dollar originally invested of 2 697 emerges this amounts to a postcommission annual rate of return of 7 34 percent the market tv for the same period before commissions also without dividends is 1 728 as for commissions there are only two commission occasions with respect to this market portfolio for the entire 1965 78 period at entry and at exit fourteen years later the postcommission return of the market is 3 75 percent annually the margin by which the rank i stocks exceeds the market therefore is the difference between 7 34 percent and 3 75 percent namely 3 6 percent prior to risk adjustment not 8 6 percent since the 3 6 percent extra return just derived did not include dividends the differential would be narrowed even more if the yield on the market is greater than on rank i stocks we can get an estimate of the differential yields using holloway s table i p 712 in table i he compares the vl reported returns with his own calculations using those vl stocks included in the crsp file according to table iv p 717 the number of rank i stocks included therein ranged from 86 to 90 out of 100 over the four year period he chooses as representative of the whole 1965 78 period the number of stocks which compose the market of allvl stocks on the file ranged from 1476 to 1482 out of about 1700 holloway s calculations of return include dividends vl s do not holloway uses the table to show how close his results were to vl s if consideration is given to dividends

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

testing and aggressive investment strategy using value line ranks a reply this paper answers the comments of readers of our earlier paper additional insight is gained by adding recent data to show the effect of following value line investment service recommendations in active stock market trading over the years 1974 81 we show that value line makes a statistically significant contribution which cannot be explained by the beta risk factor we offer our results as an unexplained divergence from the efficient market hypothesis but with the tentative hypothesis that investment timing i e the fact that the efficient market hypothesis does not operate instantaneously may explain much of the abnormality our previous paper 5 tested whether the performance of the value line investment service vl was inconsistent with the efficient market hypothesis emh we showed using data through 1977 that the vl system represented a borderline case when transaction costs were neglected abnormal returns were obtained when realistic transaction costs were used active trading according to vl recommendations yielded abnormal returns but not significantly so for a buy and hold policy however vl recommendations did yield abnormal returns even when transaction costs were considered our previous paper drew a number of comments from readers we offer our reply to these comments in section i below further insight into the comments is gained by considering added data and the present paper adds data for the years 1978 81 discussed in sections ii v with the benefit of the added data we show that the active trading returns already positive now become significantly positive we conclude that beta does not offer an explanation for the abnormal yields we offer our results as an unexplained divergence from the emh but with the tentative hypothesis that investment timing i e the fact that the emh does not operate instantaneously may explain much of the abnormality particularly for the active trading results

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

asymmetric information and optimal bank reserves one traditional argument in favor of bank reserve requirements holds that since a b ank and its depositors are asymmetrically informed as to the bank s reserve position and its portfolio the bank will hold too few reserves and too risky a port folio this being the case presumably a central banking authority is equipped t o impose and to monitor a minimal reserve requirement yielding a pareto superior outcome this paper analyzes this position in the context of a formalmodel and shows that at least for some parameters of the model there is such a case for a minimal reserve requirement copyright 1987 by ohio state university press

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

security price reactions around corporate spin off announcements abstract we examine security price reactions around the announcements of 123 voluntary spin offs by 116 firms between 1963 and 1981 involving a pro rata distribution of the common stock of a subsidiary to the stockholders of the parent firm the median spin off in the sample is 6 6 of the original equity value and is associated with an abnormal return of 7 0 from 50 days prior to the announcement through completion of the spin off no evidence is found to indicate the gains to stockholders represent wealth transfers from senior securityholders over the entire event period we find positive gains for firms engaging in spin offs to facilitate mergers or to separate diverse operating units but negative returns to firms responding to legal and or regulatory difficulties in the two day interval surrounding the first press announcement we find positive average excess returns for all groups

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

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

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

weekend effects on stock returns a note some researchers have apparently been surprised to discover that the distribution of stock returns depends on the day of the week kenneth french 3 for example in testing whether daily stock returns are generated by a trading time or calendar time hypothesis provided convincing evidence of a negative market return on mondays as french carefully notes this finding runs counter to both hypotheses since a trading time view would have expected stock returns equal on different days and a calendar time view would have higher expected returns on monday to compensate for the longer holding period in this paper we offer a partial explanation for the apparently puzzling discovery of different daily returns we argue that the expected stock returns as measured for example from closing to closing prices should depend on the day of the week in general we argue that the expected returns on mondays should be lower than would be implied simply by a trading time or calendar time model and the returns on fridays should be higher in addition we anticipate that holidays will have complex effects on stock returns on other days of the week our argument is based on the delay between trading and settlements in stocks and in clearing checks the explanation that we offer for different measured daily returns does not contradict the efficient market hypothesis as correctly adjusted expected returns should not differ according to the day of the week

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