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

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

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

investigating diesel market integration in france evidence from micro data abstract we use station level daily price observations collected over a period of eight years to study the extent of diesel market integration in france the empirical analysis starts off by examining the time series properties of diesel price ratios and then assesses how geographical separation and petrol station characteristics affect the speed of adjustment of prices to exogenous shocks our findings reveal that the great majority of diesel price ratios maintain stable long run relationships we also find that while price ratios take longer to adjust for pairs of petrol stations that are farther apart adjustment is quicker when pairs of petrol stations provide the same range of services and when they belong to the same owner

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

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

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

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

excess capacity as a barrier to entry an empirical appraisal this paper examines excess capacity barriers to entry and investment dynamics in a sample of thirty eight chemical product industries logit and log linear models of investment behavior are estimated and specific case examples are considered the results show that incumbents rarely built excess capacity pre emptively in an effort to deter entry in general entrants and incumbents exhibited similar investment behavior

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

contestable markets under uncertainty in this article we present a model of a market which is ex post contestable we show that in a market characterized by uncertainty a firm will face a tradeoff between efficiency and flexibility and generally will make some precommitments to take advantage of ex ante technologies we show that in the face of potential entry the incumbent will increase his precommitments and in so doing will affect the probability of entry the degree of market contestability is therefore endogenously determined by the choice of precommitments the extent to which precommitments will be used to affect entry probabilities is shown to depend on the efficiency of ex ante production adjustment costs and the degree of uncertainty in particular we show that the market becomes more contestable as the relative efficiency of ex post production increases and as market conditions become more uncertain

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

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

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

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

capital as a commitment strategic investment to deter mobility abstract this paper analyzes how an early entrant in a market can exploit its head start by strategic investment the analysis is based on spence s paper investment strategy and growth in a new market bell j econ 10 1979 1 19 we frist study the investment game in the no discounting case which embodies the key features of mobility deterrence we establish the existence of a set of perfect equilibria and suggest that one particular equilibrium is most reasonable this equilibrium also valid with discounting involves the follower firm being forever deterred from investing to its steady state reaction curve in contrast to spence s proposed solution

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

do gasoline prices respond asymmetrically to crude oil price changes the authors test and confirm that retail gasoline prices respond more quickly to increases than to decreases in crude oil prices among the possible sources of this asymmetry are production inventory adjustment lags and market power of some sellers by analyzing price transmission at different points in the distribution chain the authors attempt to shed light on these theories spot prices for generic gasoline show asymmetry in responding to crude oil price changes which may reflect inventory adjustment effects asymmetry also appears in the response of retail prices to wholesale price changes possibly indicating short run market power among retailers

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

forecasting the oil gasoline price relationship do asymmetries help abstract according to the rockets and feathers hypothesis rfh the transmission mechanism of positive and negative changes in the price of crude oil to the price of gasoline is asymmetric although there have been many contributions documenting that downstream prices are more reactive to increases than to decreases in upstream prices little is known about the forecasting performance of econometric models incorporating asymmetric price transmission from crude oil to gasoline in this paper we fill this gap by comparing point sign and probability forecasts from a variety of asymmetric ecm a ecm and threshold autoregressive ecm tar ecm specifications against a standard ecm forecasts from a ecm and tar ecm subsume the rfh while the ecm implies symmetric price transmission from crude oil to gasoline we quantify the forecast accuracy gains due to incorporating the rfh in predictive models for the prices of gasoline and diesel we show that as far as point forecasts are involved the rfh does not lead to significant improvements while it can be exploited to produce more accurate sign and probability forecasts finally we highlight that the forecasting performance of the estimated models is time varying

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

why are the 2000s so different from the 1970s a structural interpretation of changes in the macroeconomic effects of oil prices in the 1970s large increases in the price of oil were associated with sharp decreases in output and large increases in inflation in the 2000s even larger increases in the price of oil were associated with much milder movements in output and inflation using a structural var approach blanchard and gali in j gali and m gertler eds 2009 international dimensions of monetary policy university of chicago press pp 373 428 argued that this reflected a change in the causal relation from the price of oil to output and inflation they then argued that this change could be due to a combination of three factors a smaller share of oil in production and consumption lower real wage rigidity and better monetary policy their argument based on simulations of a simple new keynesian model was informal our purpose in this paper is to take the next step and to estimate the explanatory power and contribution of each of these factors to do so we use a minimum distance estimator that minimizes over the set of structural parameters and for each of two samples pre and post 1984 the distance between the empirical svar based impulse response functions and those implied by a new keynesian model our empirical results point to an important role for all three factors

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

biofuel related price transmission using renewable identification number prices to signal mandate regime this paper improves on existing methods of testing price links between biofuels petroleum petroleum products and agricultural commodity feedstocks by incorporating information contained in the prices of renewable identification numbers rins that are used for biofuel mandate compliance theoretical equations are developed to show the role rin prices can play in the relationships among energy biofuel and agricultural commodity prices rin price data are used a to infer values of fuels where market data are unavailable and b to define binding and non binding mandate regimes a specific empirical exercise exploits the rin price data to conduct cointegration tests on high frequency data representing spot and futures prices relating to ethanol and biodiesel from february 2009 through march 2015 finding little evidence of short term biofuel and petroleum product substitution during this period

from oil to consumer energy prices how much asymmetry along the way abstract a longstanding question in macroeconomics is whether fuel prices react more to increases than to decreases of the price of oil this paper analyzes the response of weekly gasoline and gasoil prices to oil prices in the u s the euro area and the four largest euro area countries germany france italy and spain using nonlinear impulse response functions and forecast accuracy tests while for the u s both approaches point to the presence of asymmetries in the adjustment of retail prices for the euro area the evidence is mixed

does the volatility of commodity prices reflect macroeconomic uncertainty abstract while there exists numerous studies on the macroeconomic effects of oil and commodity shocks the literature is quite silent on the impact of macroeconomic uncertainty on oil and commodity prices and especially on their volatility this paper tackles this issue through the estimation of a structural threshold vector autoregressive tvar model on a sample of 19 commodity markets we aim at i assessing whether the effect of macroeconomic uncertainty shocks on commodity price returns depends on the degree of uncertainty and ii investigating the transfer from macroeconomic uncertainty to price uncertainty using a newly developed measure of commodity price uncertainty our findings show that both agricultural and industrial markets are highly sensitive to the variability and the level of macroeconomic uncertainty while the impact on precious metals is more parsimonious given their well identified safe haven role in time of economic turmoil in addition we find evidence that the recent 2007 09 recession has generated an unprecedented episode of high uncertainty in numerous commodity prices interestingly our analysis further reveals that volatility and uncertainty in prices can be disconnected this is especially true for the oil market as most important shocks in the 1990s and the beginning of the 2000s that lead to price volatility do not generate price uncertainty highlighting the relevance of our uncertainty measure in linking uncertainty to predictability rather than to volatility

econometric models of asymmetric price transmission abstract in this paper we review the existing empirical literature on price asymmetries in commodities providing a way to classify and compare different studies that are highly heterogeneous in terms of econometric models type of asymmetries and empirical findings relative to the previous literature this paper is novel in several respects first it presents a detailed and updated survey of the existing empirical contributions on price asymmetries in the transmission mechanism linking input prices to output prices second this paper presents an extension of the traditional distinction between long run and short run asymmetries to new categories of asymmetries such as contemporaneous impact distributed lag effect cumulated impact reaction time equilibrium and momentum equilibrium adjustment path regime effect regime equilibrium adjustment path each empirical study is then critically discussed in the light of this new classification of asymmetries third this paper evaluates the relative merits of the most popular econometric models for price asymmetries namely autoregressive distributed lags partial adjustments error correction models regime switching and vector autoregressive models finally we use the meta regression analysis to investigate whether the results of asymmetry tests are not model invariant and find which additional factors systematically influence the rejection of the null hypothesis of symmetric price adjustment the main results of our survey can be summarized as follows i each econometric model is specialized to capture a subset of asymmetries ii each asymmetry is better investigated by a subset of econometric models iii the general significance of the f test for asymmetric price transmission depends mainly on characteristics of the data dynamic specification of the econometric model and market characteristics overall our empirical findings confirm that asymmetry in all its forms is very likely to occur in a wide range of markets and econometric models

testing for price response asymmetries in the spanish fuel market new evidence from daily data abstract in this work we use daily data to examine pattern asymmetries in the speed of transmission of international wholesale oil prices to spanish retail fuel prices results are robust to two alternative specifications of an asymmetric error correction model for which the presence of autoregressive conditional heteroskedasticity for disturbances is modeled by a garch 1 1 process evidence indicates that the short term transmission of wholesale prices to retail prices is quite symmetric for both gasoline and diesel fuel nevertheless in contrast to some of the results provided for an earlier period we did not find asymmetries in the speed of retail price responses toward long run equilibrium our evidence also suggests that the use of weekly or lower frequency data is one of the possible explanations for some of the seemingly contradictory results concerning this issue

precommitment and financial structure an analysis of the effects of taxes while modern industrial organization theory predicts the use of sunk costs to deter entry this paper examines some factors that might act to moderate the commitment value of capital and hence the entry barring impact of these sunk costs to the point perhaps that empirical detection might be difficult the authors show that the tax system and financial contracts of the incumbent firm may affect its optimal precommitment decision and in fact may attenuate the strategic incentives for precommitment copyright 1992 by the london school of economics and political science

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

lags in the response of gasoline prices to changes in crude oil prices the role of short term and long term shocks abstract using weekly data for the period march 1991 to august 2002 we estimate the response of retail gasoline prices to changes in crude oil and spot gasoline prices in the us allowing for a possibility of two types of cost shocks to the gasoline market long term and short term shocks our conclusion is that theoretical models should be developed that allow more than one type of input price changes and the different effect of input price changes on output prices the empirical results support the conjecture of two types of cost shocks as such we find that lags in the response of retail gasoline prices to changes in crude oil prices may be due to the fact that approximately 97 of changes in crude oil prices are viewed as short term by the market participants when two types of shocks are considered there is large difference between the cumulative response function of gasoline prices to long term and short term shocks to crude oil prices as such this paper adds to our understanding of the price stickiness of gasoline prices

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

the pass through of oil prices into euro area consumer liquid fuel prices in an environment of high and volatile oil prices abstract crude and refined oil prices have been relatively high and volatile on a sustained basis since 1999 this paper considers the pass through of oil prices into consumer liquid i e petrol diesel and heating fuel prices in such an environment the pass through of oil prices into consumer liquid fuel prices has already been addressed extensively in the literature nonetheless much of this literature has either focused on the united states or on a time period when oil prices were relatively stable or has used monthly data the main contribution of this paper is a comprehensive combination of many features that have been considered before but rarely jointly these features include 1 the analysis of the euro area as an aggregate and a large number of countries the initial 12 member states 2 the consideration of different time periods 3 the modelling of the data in raw levels rather than in log levels this turns out to have important implications for our findings 4 the use of high frequency weekly data which as results will suggest are the lowest frequency one should consider 5 the investigation of the different stages of the production chain from crude oil prices to retail distribution refining costs and margins distribution and retailing costs and margins 6 the examination of prices including and excluding taxes excise and value added 7 the modelling of prices for three fuel types passenger car petrol and diesel separately and home heating fuel oil 8 lastly we also address the issue of possible asymmetries allowing for the pass through to vary according to a whether price are increasing or decreasing and b whether price levels are above or below their equilibrium level the main findings are as follows first as distribution and retailing costs and margins have been broadly stable on average the modelling of the relationship between consumer prices excluding taxes and upstream prices in raw levels rather than in logarithms has important implications for the stability of estimates of pass through when oil price levels rise significantly second considering spot prices for refined prices improves significantly the fit of the estimated models relative to using crude oil prices it also results in more economically meaningful results concerning the extent of pass through third oil price pass through occurs quickly with 90 occurring within three to five weeks fourth using a relatively broad specification allowing for asymmetry in the pass through from upstream to downstream prices there is little evidence of statistically significant asymmetries furthermore even where asymmetry is found to be statistically significant it is generally not economically significant lastly these results generally hold across most euro area countries with few exceptions

retail price asymmetries in local gasoline markets the experience of the salt lake city retail gasoline market during the 1989 to 1993 period suggests that there was asymmetric adjustment wholesale and retail price data unique to the salt lake market are used to test for the existence of three types of price asymmetry during this period with respect to the magnitude of the pass through of wholesale price changes evidence of price asymmetry during periods of average weekly wholesale price changes is found however during market shocks the evidence points in the direction of retail price symmetry evidence of asymmetry with respect to the timing and pattern of the retail price response is found for periods of both average and market shock changes since gasoline is purchased by the consumer in local markets conclusions reached by studies of market pricing behaviour that rely on national data may not be relevant for local policy decisions this appears to be the case for the salt lake city market

do oil price increases cause higher food prices us retail food price increases in recent years may seem large in nominal terms but after adjusting for inflation have been quite modest even after the change in us biofuel policies in 2006 in contrast increases in the real prices of corn soybeans wheat and rice received by us farmers have been more substantial and can be linked in part to increases in the real price of oil that link however appears largely driven by common macroeconomic determinants of the prices of oil and of agricultural commodities rather than the pass through from higher oil prices we show that there is no evidence that corn ethanol mandates have created a tight link between oil and agricultural markets moreover increases in agricultural commodity prices have contributed little to us retail food price increases because of the small cost share of agricultural products in food prices in short there is no evidence that oil price shocks have been associated with more than a negligible increase in us retail food prices in recent years nor is there evidence for the prevailing wisdom that oil price driven increases in the cost of food processing packaging transportation and distribution have been responsible for higher retail food prices similar results hold for other industrialized countries there is reason however to expect food commodity prices to be more tightly linked to retail food prices in developing countries christiane baumeister and lutz kilian

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

asymmetric pass through of oil prices to gasoline prices with interval time series modelling abstract modelling and analyzing the pass through of crude oil prices to retail gasoline prices has received an increasing attention in existing literature however most of the ongoing efforts focus on point valued data which may suffer from the informational loss this paper first employs the threshold autoregressive interval valued tari models developed recently by sun et al 2018a to investigate this transmission and proposes a novel consistent interval based test to detect threshold effects and derives its asymptotic properties these interval based tari model and test superior to traditional point based methods can produce more efficient parameter estimation and more powerful inference due to the informational gain of interval data that is interval data simultaneously contain more information e g trend and volatility than point valued data during the same period empirical results suggest that retail gasoline prices are adjusted upwards faster than downwards and this short run asymmetry is statistically significant during the whole sample our findings reveal that both the level and volatility of oil prices have a positive impact on the price of gasoline which contributes to the asymmetries in the transmission of oil price shocks these results are robust before and after financial crisis furthermore it is interesting to find that after financial crisis both the error correction adjustment speeds and the asymmetries decrease

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

dynamic effects of rising oil prices on consumer energy prices in canada and the united states evidence from the last half a century abstract this paper examines the dynamic relationship between the price of crude oil and the cpi energy price sub index in canada and the u s using a markov regime switching model and the bai perron sequential method since these two series are cointegrated during the sample period january 1961 june 2013 a short run dynamic model is thus estimated for each country in which all coefficients and the error variance terms can freely switch over time between two values prevailing in regimes 0 and 1 previous studies indicate that the price of crude oil does not currently affect the aggregate cpi as much as it did in the 1970s this finding is not disputed in this paper however the sequentially determined break date as well as the time varying regime switching probabilities point to two new findings first the marginal effects of changes in oil price on consumer energy prices not the aggregate cpi have consistently increased and become more instantaneous for both countries after the western u s energy crisis of 2000 second the speed of adjustment proxied by different error correction coefficients has also risen particularly for the u s therefore oil prices exert far more positive and immediate impacts on energy costs in the post rather than pre 1999 periods