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 message in weekly exchange rates in the european monetary system mean reversion conditional heteroscedasticity and jumps weekly rates of the european monetary system ems vis a vis the deutsche mark from april 1979 to march 1991 are modeled as a combined ma 1 garch 1 1 jump process the moving average ma part accounts for mean reversion required for the rates to stay inside the target zone the generalized autoregressive conditional heteroscedasticity garch part accounts for changing volatility whereas the jump process models parity changes and other erratic movements using an adjusted pearson chi squared goodness of fit test we find similar results for the bernoulli and the poisson jump processes in those cases in which the bernoulli normal distribution does not pass the goodness of fit test a mixture of three normals does finally the ma 1 garch 1 1 bernoulli jump models are jointly estimated assuming a constant contemporaneous correlation matrix for the disturbances and a common jump probability for all the currencies

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

estimating value at risk of crude oil price and its spillover effect using the ged garch approach estimation has been carried out using garch type models based on the generalized error distribution ged for both the extreme downside and upside value at risks var of returns in the wti and brent crude oil spot markets furthermore according to a new concept of granger causality in risk a kernel based test is proposed to detect extreme risk spillover effect between the two oil markets results of an empirical study indicate that the ged garch based var approach appears more effective than the well recognized hsaf i e historical simulation with arma forecasts moreover this approach is also more realistic and comprehensive than the standard normal distribution based var model that is commonly used results reveal that there is significant two way risk spillover effect between wti and brent markets supplementary study indicates that at the 99 confidence level when negative market news arises that brings about a slump in oil price return historical information on risk in the wti market helps to forecast the brent market conversely it is not the case when positive news occurs and returns rise historical information on risk in the two markets can facilitate forecasts of future extreme market risks for each other these results are valuable for anyone who needs evaluation and forecasts of the risk situation in international crude oil markets

a longitudinal analysis of white women s wages according to human capital theory women s work participation decisions will strongly affect their wages and wage growth we test human capital predictions about how labor force withdrawals both past and prospective part time work experience and working in male rather than in female jobs affect wages and wage growth for white women we do this by estimating a wage change model for the years 1967 1979 for a national sample of white women we find that wages drop immediately following withdrawals but that this is followed by a rapid wage growth so that the net loss in wages from dropping out of the labor force is small we further find that wage growth is not significantly lower in female than in male jobs but that part time work experience does not lead to significant wage growth in either male or female jobs

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

can the markov switching model forecast exchange rates abstract a markov swirching model is fit for 18 exchange rates at quarterly frequencies the model fits well in sample for many exchange rates by the mean squared error criterion the markov model does not generate superior forecasts to a random walk or the forward rate there appears to ne some evidence that the forecasts of the markov model are superior at predicting the direction of change of the exchange rate

forward curves scarcity and price volatility in oil and natural gas markets the role of inventory in explaining the shape of the forward curve and spot price volatility in commodity markets is central in the theory of storage developed by kaldor kaldor n 1939 speculation and economic stability the review of economic studies 7 1 27 and working working h 1949 the theory of the price of storage american economic review 39 1254 1262 and has since been documented in a vast body of financial literature including the reference paper by fama and french fama e f and k r french 1987 commodity futures prices some evidence on forecast power premiums and the theory of storage journal of business 60 55 73 on metals the goal of this paper is twofold i validate in the case of oil and natural gas the use of the slope of the forward curve as a proxy for inventory the slope being defined in a way that filters out seasonality ii analyze directly for these two major commodities the relationship between inventory and price volatility in agreement with the theory of storage we find that i the negative correlation between price volatility and inventory is globally significant for crude oil ii this negative correlation prevails only during those periods of scarcity when the inventory is below the historical average and increases importantly during the winter periods for natural gas our results are illustrated by the analysis of a 15 year database of us oil and natural gas prices and inventory

hedging crude oil using refined product a regime switching asymmetric dcc approach abstract in this paper we explore the strategy on hedging crude oil using refined product we develop a regime switching asymmetric dcc rs adcc model by taking into account both of regime switching and asymmetry in correlations our out of sample findings indicate that rs adcc displays greater hedging effectiveness than some conventional multivariate garch heating oil can better hedge crude oil than gasoline

forecasting volatility in the petroleum futures markets a re examination and extension abstract this paper examines the volatility models and their forecasting abilities for four types of petroleum futures contracts traded on the new york mercantile exchange the aim of this paper is twofold firstly it replicates and carries out the robustness checks using the rigorous model confidence set test on the out of sample volatility forecast analysis undertaken by sadorsky energy economics 2006 28 467 488 through the same statistical models but with the extended data on daily prices of petroleum futures our test results largely confirm the findings obtained in the replicated paper secondly our paper also explores the relevance of some statistical complexities e g model optimality regime switches and alternative distribution functions in volatility forecasting through a large number of moving windows our results in general show that accounting for the model optimality structural breaks and using the asymmetric heavy tailed distribution functions in the estimations lead to significant forecasting accuracy gains

forecasting volatility of crude oil markets abstract this article investigates the efficacy of a volatility model for three crude oil markets brent dubai and west texas intermediate wti with regard to its ability to forecast and identify volatility stylized facts in particular volatility persistence or long memory in this context we assess persistence in the volatility of the three crude oil prices using conditional volatility models the cgarch and figarch models are better equipped to capture persistence than are the garch and igarch models the cgarch and figarch models also provide superior performance in out of sample volatility forecasts we conclude that the cgarch and figarch models are useful for modeling and forecasting persistence in the volatility of crude oil prices

an evaluation of volatility forecasting techniques the existing literature contains conflicting evidence regarding the relative quality of stock market volatility forecasts evidence can be found supporting the superiority of relatively complex models including arch class models while there is also evidence supporting the superiority of more simple alternatives these inconsistencies are of particular concern because of the use of and reliance on volatility forecasts in key economic decision making and analysis and in asset option pricing this paper employs daily australian data to examine this issue the results suggest that the arch class of models and a simple regression model provide superior forecasts of volatility however the various model rankings are shown to be sensitive to the error statistic used to assess the accuracy of the forecasts nevertheless a clear message is that volatility forecasting is a notoriously difficult task

the link between ability and specialization an explanation for observed correlations between wages and mobility rates wage levels and turnover rates are negatively correlated across types of employment and this fact is often interpreted as evidence that high wage jobs are rationed a simple training model illustrates however that this correlation may arise because able workers have an incentive to choose highly specialized jobs in any job the most able workers possess the most valuable stocks of specific skills and therefore face the highest mobility costs thus able workers may have a comparative advantage in specialized employments data from the national longitudinal survey of youth provide an opportunity to evaluate the merits of the training model developed here data on worker training and mobility provide support for several implications of the model the model also provides new ways to interpret existing results in the literature on interindustry wage differentials

trends and contagion in wti and brent crude oil spot and futures markets the role of opec in the last decade abstract this article examines the interconnectedness of wti and brent prices on different resolutions of price movements firstly within a multivariate bekk framework we identify high but volatile correlations with recurring highs around 0 8 and multiple periods of decoupling opec meetings increase the correlation in the short run secondly linear l1 trends reveal that long term movements of wti and brent are driven by the same dynamics confirming the one great pool hypothesis opec meetings have only little impact on long term price trends thirdly we find leading effects of wti over brent by short term trends of several days especially in a negative direction these trends have an asymmetrical effect on volatility negative trends cause a stronger increase than positive trends these findings are of interest to policy makers as well as hedging strategies of crude oil portfolios and provide insight into long term movements of crude prices

oil prices and real exchange rates in this paper we investigate the long run relationship between real oil prices and real exchange rates by using a monthly panel of g7 countries from 1972 1 to 2005 10 we first test whether exchange rates are cointegrated with real oil prices it is shown that real oil prices may have been the dominant source of real exchange rate movements and that there is a link between real oil prices and real exchange rates we then examine the ability of real oil prices to forecast future real exchange returns panel predictive regression estimates suggest that real oil prices have significant forecasting power the out of sample prediction performances demonstrate greater predictability over longer horizons

volatility regimes asymmetric basis effects and forecasting performance an empirical investigation of the wti crude oil futures market abstract this study employs a flexible regime switching egarch model with student t distributed error terms to investigate whether volatility regimes and basis affect the behavior of crude oil futures returns including the conditional mean variance skewness kurtosis as well as the extent of heavy tailedness the study also examines whether volatility regimes and asymmetric basis effects can improve the forecasting accuracy the main merit of the empirical model is that the basis effect is allowed to be asymmetric and to vary across volatility regimes empirical results suggest that the conditional mean and variance respond to the basis asymmetrically and nonlinearly and that the responses of transition probabilities to the basis are symmetric furthermore the conditional higher moments are sensitive to the absolute value of basis and the heavy tailed characteristic can be greatly alleviated by taking into account the asymmetric basis effects and regime switches finally the regime switches and asymmetric basis effects play decisive roles in forecasting return volatility and tail distribution

good bad cojumps and volatility forecasting new evidence from crude oil and the u s stock markets abstract in this article we investigate the impacts of jumps cojumps and their signed components on forecasting oil futures price volatility in the framework of the heterogeneous autoregressive realized volatility model our empirical results reveal several noteworthy findings first the effects of signed jumps and cojumps based on the daily and intraday jump tests on future volatility are asymmetric and the negative components are much more powerful in forecasting volatility moreover our proposed models including the signed jump and cojump components are able to generate higher forecasting accuracy and we find that disentangling the effects of positive and negative jumps and cojumps can significantly improve forecasts of future volatility lastly our findings are reliable for various robustness checks and our study provides some new insights into forecasting oil price realized volatility which are useful for researchers market participants and policymakers

extreme value theory and value at risk application to oil market recent increases in energy prices especially oil prices have become a principal concern for consumers corporations and governments most analysts believe that oil price fluctuations have considerable consequences on economic activity oil markets have become relatively free resulting in a high degree of oil price volatility and generating radical changes to world energy and oil industries as a result oil markets are naturally vulnerable to significant negative volatility an example of such a case is the oil embargo crisis of 1973 in this newly created climate protection against market risk has become a necessity value at risk var measures risk exposure at a given probability level and is very important for risk management appealing aspects of extreme value theory evt have made convincing arguments for its use in managing energy price risks in this paper we apply both unconditional and conditional evt models to forecast value at risk these models are compared to the performances of other well known modelling techniques such as garch historical simulation and filtered historical simulation both conditional evt and filtered historical simulation procedures offer a major improvement over the parametric methods furthermore garch 1 1 t model may provide equally good results as well as the combining of the two procedures

energy markets volatility modelling using garch abstract this paper investigates the empirical properties of oil natural gas and electricity price volatilities using a range of univariate and multivariate garch models and daily data from wholesale markets in the united states for the period from 2001 to 2013 the key contribution to the literature is the estimation of trivariate bekk and dcc models that allow us to observe spillovers and interactions among energy markets we evaluate and compare the performance of univariate and multivariate models with a range of diagnostic and forecast performance tests and assess forecasting performance and conditional correlation dynamics

linking the gas and oil markets with the stock market investigating the u s relationship energy markets can represent a strategic advantage when they are supporting each other and specifically when energy segments are complementary enough to support economic development and growth in this light a high and strategic interest relies on the possible interactions between energy market segments as well as their impact on a given country s financial market the proposed research focuses on the interaction between the u s natural gas and u s crude oil markets on one side and their dependencies with the u s stock market on the other side after controlling for structural changes or breaks we characterize previous dependencies with the multivariate copula methodology first we assess the joint link prevailing between the natural gas and crude oil markets then we characterize the joint risk structure prevailing between previous energy markets and the u s stock market finally we assess the joint dependence structure between the natural gas crude oil and stock markets

volatility transmission and volatility impulse response functions in crude oil markets abstract using daily data from july 2005 to february 2011 for wti dubai and brent futures contracts we employ a var bekk model to investigate crude oil markets integration on the second moment we also quantify the size and persistence of these connections through the analysis of volatility impulse response functions virf for two historical shocks namely the 2008 financial crisis and the bp deepwater horizon oil spill we observe that brent and dubai crude are highly responsive to market shocks whereas wti crude shows the least responsiveness of the three benchmarks which creates questions about its predominance as a benchmark crude oil furthermore we fit the density of the virf at different forecast horizons these fitted distributions are asymmetric showing that the probability of observing a large impact of a shock is lower while the probability of a relatively smaller impact is much higher finally we simulate the virf for a given probability of a random shock the virf shows that only a large shock derived from a smaller probability will result in an increase in expected conditional volatilities these results provide useful insights into the volatility transmission mechanism in crude oil markets and their associated risk estimation and may have significant implications for various market participants and regulators

the effects of crude oil shocks on stock market shifts behaviour a regime switching approach abstract in this paper we develop a two regime markov switching egarch model introduced by henry henry o 2009 regime switching in the relationship between equity returns and short term interest rates journal of banking and finance 33 405 414 to examine the relationship between crude oil shocks and stock markets an application to stock markets of uk france and japan over the sample period january 1989 to december 2007 illustrates plausible results we detect two episodes of series behaviour one relative to low mean high variance regime and the other to high mean low variance regime furthermore there is evidence that common recessions coincide with the low mean high variance regime in addition we allow both real stock returns and probability of transitions from one regime to another to depend on the net oil price increase variable the findings show that rises in oil price has a significant role in determining both the volatility of stock returns and the probability of transition across regimes

modeling and forecasting crude oil markets using arch type models this study investigates the time varying volatility of two major crude oil markets the west texas intermediate wti and europe brent a flexible autoregressive conditional heteroskedasticity arch model is used to take into account the stylized volatility facts such as clustering volatility asymmetric news impact and long memory volatility among others the empirical results indicate that the intensity of long persistence volatility in the wti is greater than in the brent it is also found that for the wti the appreciation and depreciation shocks of the wti have similar impact on the resulting volatility however a leverage effect is found in brent although both the estimation and diagnostic evaluations are in favor of an asymmetric long memory arch model only the wti models provide superior in the out of sample forecasts on the other hand from the empirical out of sample forecasts it appears that the simplest parsimonious generalized arch provides the best forecasted evaluations for the brent crude oil data

volatility in crude oil futures a comparison of the predictive ability of garch and implied volatility models the wti future contract quoted at the nymex is the most actively traded instrument in the energy sector this paper compares the predictive ability of two approaches which can be used to forecast volatility garch type models where forecasts are obtained after estimating time series models and an implied volatility model where forecasts are obtained by inverting one of the models used to price options although the main scope of the research discussed here is to evaluate which model produces the best forecast of volatility for the wti future contract evaluated according to statistical and regression based criteria we also investigate whether volatility of the oil futures are affected by asymmetric effects whether parameters of the garch models are influenced by the distribution of the errors and whether allowing for a time varying long run mean in the volatility produces any improvement on the forecast obtained from garch models

forecasting the volatility of crude oil futures using har type models with structural breaks abstract we introduce sixteen har type volatility models with structural breaks and estimate their parameters by applying 5 min high frequency transaction data for wti crude oil futures we find significant structural breaks in the volatility of crude oil futures additionally the historical realized volatility continuous sample path variation negative realized semivariance signed jump signed semi jump and leverage components contain substantial and salient information for forecasting the volatility of crude oil futures then we use loss functions to assess the forecasting performance of these sixteen new models and finally rank these models using the promethee ii method our results indicate that different models exhibit different predictive power in forecasting the 1 day 1 week and 1 month volatility of crude oil futures of the new har type models the new har rsv model performs best at forecasting the 1 day and 1 month volatilities whereas the new har cj best forecasts the 1 week volatility

regime switching stochastic volatility evidence from the crude oil market this paper incorporates regime switching into the stochastic volatility sv framework in an attempt to explain the behavior of crude oil prices in order to forecast their volatility more specifically it models the volatility of oil return as a stochastic volatility process whose mean is subject to shifts in regime the shift is governed by a two state first order markov process the bayesian markov chain monte carlo method is used to estimate the models the main findings are first there is clear evidence of regime switching in the oil market ignoring it will lead to a false impression that the volatility is highly persistent and therefore highly predictable second incorporating regime switching into the sv framework significantly enhances the forecasting power of the sv model third the regime switching stochastic volatility model does a good job in capturing major events affecting the oil market

risk assessment of oil price from static and dynamic modelling approaches the price gap between west texas intermediate wti and brent crude oil markets has been completely changed in the past several years the price of wti was always a little larger than that of brent for a long time however the price of wti has been surpassed by that of brent since 2011 the new market circumstances and volatility of oil price require a comprehensive re estimation of risk therefore this study aims to explore an integrated approach to assess the price risk in the two crude oil markets through the value at risk var model the var is estimated by the extreme value theory evt and garch model on the basis of generalized error distribution ged the results show that evt is a powerful approach to capture the risk in the oil markets on the contrary the traditional variance covariance vc and monte carlo mc approaches tend to overestimate risk when the confidence level is 95 but underestimate risk at the confidence level of 99 the var of wti returns is larger than that of brent returns at identical confidence levels moreover the ged garch model can estimate the downside dynamic var accurately for wti and brent oil returns

a markov regime switching approach for hedging energy commodities abstract this paper estimates constant and dynamic hedge ratios in the new york mercantile exchange oil futures markets and examines their hedging performance we also introduce a markov regime switching vector error correction model with garch error structure this specification links the concept of disequilibrium with that of uncertainty as measured by the conditional second moments across high and low volatility regimes overall in and out of sample tests indicate that state dependent hedge ratios are able to provide significant reduction in portfolio risk

forecasting the realized volatility of the oil futures market a regime switching approach considering nonlinear and highly persistent dynamics of realized volatility we introduce markov regime switching models to the heterogeneous autoregressive model of the realized volatility har rv models to forecast the realized volatility of the crude oil futures market in sample results demonstrate that the high volatility regime is short lived out of sample results suggest that har rv models with regime switching increase the forecasting ability significantly than those without regime switching moreover these findings are robust for different actual volatility benchmarks forecasting windows and model settings

futures based forecasts how useful are they for oil price volatility forecasting oil price volatility forecasts have recently attracted the attention of many studies in the energy finance field the literature mainly concentrates its attention on the use of daily data using garch type models it is only recently that efforts to use more informative intraday data to forecast oil price realized volatility have been made despite all these previous efforts no study has examined the usefulness of futures based models for oil price realized volatility forecasting although the use of such models is extensive for oil price predictions this study fills this void and shows that futures based forecasts based on intra day data provide informative forecasts for horizons that span between 1 day and 66 days ahead more importantly these results hold true even during turbulent times for the oil market such as the global financial crisis of 2007 09 and the oil collapse period of 2014 15

a markov switching model of the conditional volatility of crude oil futures prices abstract this paper examines the temporal behaviour of volatility of daily returns on crude oil futures using a generalised regime switching model that allows for abrupt changes in mean and variance garch dynamics basis driven time varying transition probabilities and conditional leptokurtosis this flexible model enables us to capture many complex features of conditional volatility within a relatively parsimonious set up we show that regime shifts are clearly present in the data and dominate garch effects within the high volatility state a negative basis is more likely to increase regime persistence than a positive basis a finding which is consistent with previous empirical research on the theory of storage e g fama and french 1988a fama and french 1988b and ng and pirrong 1994 the volatility regimes identified by our model correlate well with major events affecting supply and demand for oil out of sample tests indicate that the regime switching model performs noticeably better than non switching models regardless of evaluation criteria we conclude that regime switching models provide a useful framework for the financial historian interested in studying factors behind the evolution of volatility and to oil futures traders interested short term volatility forecasts

forecasting crude oil market volatility further evidence using garch class models abstract this paper extends the work of kang et al 2009 we use a greater number of linear and nonlinear generalized autoregressive conditional heteroskedasticity garch class models to capture the volatility features of two crude oil markets brent and west texas intermediate wti the one five and twenty day out of sample volatility forecasts of the garch class models are evaluated using the superior predictive ability test and with more loss functions unlike kang et al 2009 we find that no model can outperform all of the other models for either the brent or the wti market across different loss functions however in general the nonlinear garch class models which are capable of capturing long memory and or asymmetric volatility exhibit greater forecasting accuracy than the linear ones especially in volatility forecasting over longer time horizons such as five or twenty days

estimation of value at risk for energy commodities via fat tailed garch models the choice of an appropriate distribution for return innovations is important in var applications owing to its ability to directly affect the estimation quality of the required quantiles this study investigates the influence of fat tailed innovation process on the performance of one day ahead var estimates using three garch models garch n garch t and garch ht daily spot prices of five energy commodities wti crude oil brent crude oil heating oil 2 propane and new york harbor conventional gasoline regular are used to compare the accuracy and efficiency of the var models empirical results suggest that for asset returns that exhibit leptokurtic and fat tailed features the var estimates generated by the garch ht models have good accuracy at both low and high confidence levels additionally mrsb indicates that the garch ht model is more efficient than alternatives for most cases at high confidence levels these findings suggest that the heavy tailed distribution is more suitable for energy commodities particularly var calculation

on the relationship between the prices of oil and the precious metals revisiting with a multivariate regime switching decision tree this study examines the volatility and correlation and their relationships among the euro us dollar exchange rates the s p500 equity indices and the prices of wti crude oil and the precious metals gold silver and platinum over the period 2005 to 2012 our model links the univariate volatilities with the correlations via a hidden stochastic decision tree the ensuing hidden markov decision tree hmdt model is in fact an extension of the hidden markov model hmm introduced by jordan et al 1997 the architecture of this model is the opposite that of the classical deterministic approach based on a binary decision tree and it allows a probabilistic vision of the relationship between univariate volatility and correlation our results are categorized into three groups namely 1 exchange rates and oil 2 s p500 indices and 3 precious metals a switching dynamics is seen to characterize the volatilities while in the case of the correlations the series switch from one regime to another this movement touching a peak during the period of the subprime crisis in the us and again during the days following the tohoku earthquake in japan our findings show that the relationships between volatility and correlation are dependent upon the nature of the series considered sometimes corresponding to those found in econometric studies according to which correlation increases in bear markets at other times differing from them

forecasting the volatility of crude oil futures using intraday data abstract we use the information in intraday data to forecast the volatility of crude oil at a horizon of 1 66 days using a variety of models relying on the decomposition of realized variance in its positive or negative semivariances part and its continuous or discontinuous part jumps we show the importance of these decompositions in predictive in sample regressions using a number of specifications nevertheless an important empirical finding comes from an out of sample analysis which unambiguously shows the limited interest of considering these components overall our results indicates that a simple autoregressive specification mimicking long memory and using past realized variances as predictors does not perform significantly worse than more sophisticated models which include the various components of realized variance

spread and volatility in spot and forward exchange rates abstract this paper is concerned with modeling the conditional heteroscedasticity of the prediction error of foreign exchange rates as spot and forward rates are cointegrated we use a system of error correction models for mean prediction to predict the variance we use a vibariate generalized autoregressive conditional heteroscedasticity garch model as a function of the spread using daily series for seven currencies we find that unmodeled conditional heteroscedasticity by garch can generally be explained by the squared spread this indicates that as the spread is bigger the exchange rates are more volatile jel f31 c32

exploiting the heteroskedasticity in measurement error to improve volatility predictions in oil and biofuel feedstock markets abstract we examine whether heteroskedasticity in measurement errors improves the volatility forecasting ability of the heterogenous autoregressive har model in crude oil and biofuel feedstock markets we also examine the incremental explanatory power of jumps and the investor fear gauge ifg over heteroskedasticity in measurement errors in improving the volatility forecasting ability of the har model in each of these markets for the in sample evaluation we find that exploiting the heteroskedasticity of measurement errors in the har model improves the model s goodness of fit measured by adjusted r2 by up to 10 depending on the market ifg has a significant incremental role over heteroskedasticity in measurement errors in improving the fit of the har model in both the crude oil and biofuel feedstock markets while jumps have a significant incremental role in improving the fit of the har model in the crude oil market but not the biofuel feedstock markets for the out of sample forecasts including regime switching improves volatility predictions in the corn and wheat markets across all forecasting horizons while for the soybean market including regime switching improves the performance of multi step volatility forecasts in the out of sample forecasts the best ranked models almost always include heteroskedasticity of measurement error and ifg

forecasting energy market volatility using garch models can multivariate models beat univariate models abstract in this paper we forecast energy market volatility using both univariate and multivariate garch class models first we forecast volatilities of individual assets and find that multivariate models display better performance than univariate models second we forecast crack spread volatility and contrast the performance of multivariate models for two underlyings with the alternative of univariate ones for crack spreads directly our evidence shows that univariate models allowing for asymmetric effects display the greatest accuracy we also discuss the hedging strategy based on multivariate models and its implications for market participants

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

a markov model of switching regime arch in this article i present a new approach to model more realistically the variability of financial time series i develop a markov arch model that incorporates the features of both hamilton s switching regime model and engle s autoregressive conditional heteroscedasticity arch model to examine the issue of volatility persistence in the monthly excess returns of the three month treasury bill the issue can be resolved by taking into account occasional shifts in the asymptotic variance of the markov arch process that cause the spurious persistence of the volatility process i identify two periods during which there is a regime shift the 1974 2 1974 8 period associated with the oil shock and the 1979 9 1982 8 period associated with the federal reserve s policy change the variance approached asymptotically in these two episodes is more than 10 times as high as the asymptotic variance for the remainder of the sample i conclude that regime shifts have a greater impact on the properties of the data and i ca

forecasting oil price realized volatility using information channels from other asset classes motivated from ross 1989 who maintains that asset volatilities are synonymous to the information flow we claim that cross market volatility transmission effects are synonymous to cross market information flows or information channels from one market to another based on this assertion we assess whether cross market volatility flows contain important information that can improve the accuracy of oil price realized volatility forecasting we concentrate on realized volatilities derived from the intra day prices of the brent crude oil and four different asset classes stocks forex commodities and macro which represent the different information channels by which oil price volatility is impacted from we employ a har framework and estimate forecasts for 1 day to 66 days ahead our findings provide strong evidence that the use of the different information channels enhances the predictive accuracy of oil price realized volatility at all forecasting horizons numerous forecasting evaluation tests and alternative model specifications confirm the robustness of our results

temporal analysis of income earned by former agriculture students earnings profiles of students formerly enrolled in a college of agriculture were analyzed in the context of labor market information analysis of covariance was used to estimate models explaining how salary determinants changed during the alumni s careers gender education year of job market entry profit sharing and personal emphasis on earnings were found to be significant determinants of starting incomes starting incomes were found to be a prime determinant of later year salaries along with experience on the immediate job profit sharing oral and technical skills marital status and place of residence labor market information was found to vary in quality across participants and have a limited shelf life

does the jack of all trades hold the winning hand comparing the role of specialized versus general skills in the returns to an agricultural degree this paper examines the roles of specialized versus general skills in explaining variation in the returns to an agriculture degree among graduates working both inside and outside the agricultural industry the focus on returns by sector of employment is motivated by our finding that most agricultural graduates are employed in non agricultural jobs in that study a sample of alumni graduating from a large midwestern public university between 1982 and 2006 shows that alumni with majors more specialized in agriculture earned a premium from working in the agriculture industry but this advantage has diminished over time agricultural graduates with more general training earn more outside than inside agriculture higher ability graduates in more industry focused curricula tend to sort into the agricultural industry while higher ability graduates in broader curricula tend to choose jobs outside of agriculture all graduates are more likely to accept agricultural employment when the farm economy is strong but agricultural graduates who enter agricultural jobs when the farm economy is weak suffer lifetime earnings reductions these findings suggest that greater levels of specialization may limit a graduate s ability to adjust to changing economic circumstances agriculture degree programs could benefit from curriculum innovations that focus on developing more generalized skills

a note on the continuity of incentive schedules abstract this note develops a simple geometric argument suggesting that for every tax schedule in mirrlees 1971 seminal model of optimal income taxation there exists a continuous and nondecreasing schedule that achieves the same welfare distribution granted the original schedule leaves every agent with at least one optimal choice if the original schedule is also upper semi continuous then continuity of the schedule can be obtained without affecting any agent s set of optimal choices alternatively these results can be interpreted in terms of a principal s incentive schedule for a single agent with unknown ability and preferences the results are derived under more general conditions than those in mirrlees 1971

persistence in variance structural change and the garch model this article examines the persistence of the variance as measured by the generalized autoregressive conditional heteroskedasticity garch model in stock return data in particular we investigate the extent to which persistence in variance may be overstated because of the existence of and failure to take account of deterministic structural shifts in the model both an analysis of daily stock return data and a monte carlo simulation experiment confirm the hypothesis that garch measures of persistence in variance are sensitive to this type of model misspecification

forecasting petroleum futures markets volatility the role of regimes and market conditions in this paper we employ regime volatility models to describe time dependency in petroleum markets using a sample of nymex and ice futures contracts we establish the existence of a regime process and link this process to market fundamentals this formulation results in two distinct states a highly persistent conditional volatility process characterised by long memory and low sensitivity to market shocks and a relatively short lived nonstationary process with less memory but higher sensitivity to shocks moreover to investigate the relationship between disequilibrium and volatility of oil futures across high and low volatility regimes we use augmented regime garch models to address in a realistic way the potential diverse response of volatility to forward curve shocks the performance of these models is compared to benchmarks using both statistical tests and risk management loss functions to test the robustness of the forecasting strategies we also perform a reality check employing the stationary bootstrap approach the findings of this paper have important implications for decision making concerning trading and risk management as well as energy market operations such as refining and budget planning by providing valuable information on the oil price volatility dynamics and the ability to predict risk