a semiparametric approach to short term oil price forecasting abstract in this paper it is shown how the garch properties of oil price changes can be employed to forecast the oil price distribution over short term horizons the forecasting methodology is semiparametric and it is based on the bootstrap approach the results of an out of sample forecasting exercise carried out using the brent oil price series suggest that the forecasting approach can be used to obtain a performance measure for the forward price in addition to compute interval forecasts for the oil price

on the impacts of oil price fluctuations on european equity markets volatility spillover and hedging effectiveness the objective of this paper is to investigate the volatility spillovers between oil and stock markets in europe as not all industries are expected to be equally affected by oil price changes we conduct our study at both the aggregate as well as sector levels empirically we make use of a recently developed var garch approach which allows for transmissions in volatilities in addition we analyze the optimal weights and hedge ratios for oil stock portfolio holdings based on our results on the whole our findings show significant volatility spillovers between oil price and sector stock returns and suggest that a better understanding of those links is crucial for portfolio management in the presence of oil price risk

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

chaos in oil prices evidence from futures markets abstract we test for the presence of low dimensional chaotic structure in crude oil heating oil and unleaded gasoline futures prices from the early 1980s evidence on chaos will have important implications for regulators and short term trading strategies while we find strong evidence of non linear dependencies the evidence is not consistent with chaos our test results indicate that arch type processes with controls for seasonal variation in prices generally explain the non linearities in the data we also demonstrate that employing seasonally adjusted price series contributes to obtaining robust results via the existing tests for chaotic structure maximum likelihood methodologies that are robust to the non linear dynamics lend support for samuelson s hypothesis on contract maturity effects in futures price changes however the tests for chaos are not found to be sensitive to the maturity effects in the futures contracts the results are robust to controls for the oil shocks of 1986 and 1991

causality in variance and the type of traders in crude oil futures abstract this article examines the causal relationship and in particular informational dependence between crude oil futures return and the trading volume using daily data over a ten year period using a recent econometric methodology the two step procedure developed by cheung and ng 1996 cheung y w ng l k 1996 a causality in variance test and its applications to financial market prices journal of econometrics 72 33 48 is robust to distributional assumption and does not depend on simultaneous modeling of the two variables we find only causality at higher order lags running from return to volume in the mean as well as in conditional variance our result is not in complete agreement with several earlier studies in this area however the result does indicate mild support for noise traders hypothesis in the crude oil futures market

international evidence on crude oil price dynamics applications of arima garch models abstract we examine the usefulness of several arima garch models for modeling and forecasting the conditional mean and volatility of weekly crude oil spot prices in eleven international markets over the 1 2 1997 10 3 2009 period in particular we investigate the out of sample forecasting performance of four volatility models garch egarch and aparch and figarch over january 2009 to october 2009 forecasting results are somewhat mixed but in most cases the aparch model outperforms the others also conditional standard deviation captures the volatility in oil returns better than the traditional conditional variance finally shocks to conditional volatility dissipate at an exponential rate which is consistent with the covariance stationary garch models than the slow hyperbolic rate implied by the figarch alternative

non stationarities in financial time series the long range dependence and the igarch effects we give the theoretical basis of a possible explanation for two stylized facts observed in long log return series the long range dependence lrd in volatility and the integrated garch igarch both these effects can be explained theoretically if one assumes that the data are nonstationary

the relationship between spot and futures oil prices do structural breaks matter abstract this paper examines the effect of structural breaks on the spot futures oil prices relationship we explore the impact of structural breaks on four critical issues including cointegrating relationships market efficiency under the expectation hypothesis and the no arbitrage rule causalities and forecasting performance of futures oil volatility as far as our empirical results exhibit the structural break we detect endogenously causes some influences on these issues which is in sharp contrast to the conclusions of existing studies our findings offer some implications and suggestions to researchers investors and policymakers

dynamic cyclical comovements of oil prices with industrial production consumer prices unemployment and stock prices abstract this paper examines the empirical relationship between oil prices and several key macroeconomic variables in particular we investigate the cyclical comovements of crude oil prices with output consumer prices unemployment and stock prices the methodology involves the use of the hodrick prescott hodrick r j prescott e c 1980 post war us business cycles an empirical investigation working paper carnegie mellon university and baxter king baxter m king r g 1999 measuring business cycles approximate band pass filters for economic time series review of economics and statistics 81 575 593 filters as well as the recently developed full sample asymmetric christiano fitzgerald christiano l j fitzgerald t j 2003 the band pass filter international economic review 44 435 465 band pass filter contemporaneous and cross correlation estimates are made using the stationary cyclical components of the time series to make inference about the degree to which oil prices move with the cycle besides documenting a number of important cyclical relationships using three different time series filtering methods the results suggest that crude oil prices are procyclical and lag industrial production additionally we find that oil prices lead consumer prices

common persistence in conditional variances since the introduction of the autoregressive conditional heteroskedastic arch model in engle 1982 numerous applications of this modeling strategy have already appeared a common finding in many of these studies with high frequency financial or monetary data concerns the presence of an approximate unit root in the autoregressive polynomial in the univariate time series representation for the conditional second order moments of the process as in the so called integrated generalized arch igarch class of models proposed in engle and bollerslev 1986 in the igarch models shocks to the conditional variance are persistent in the sense that they remain important for forecasts of all horizons this idea is readily extended to a multivariate framework even though many time series may exhibit persistence in variance it is likely that several different variables share the same common long run component in that situation the variables are naturally defined to be co persistent in variance and the co persistent linear combination is interpretable as a long run relationship conditions for co persistence to occur in the multivariate linear garch model are presented these conditions parallel the conditions for linear co integration in the mean as developed by engle and granger 1987 the presence of co persistence has important implications for asset pricing relationships and in optimal portfolio allocation decisions an empirical example relating to the time series properties of nominal u s dollar exchange rates for the deutschemark and the british pound provides a simple illustration of the ideas

volatility spillovers between oil prices and stock sector returns implications for portfolio management abstract in this article we take a recent generalized var garch approach to examine the extent of volatility transmission between oil and stock markets in europe and the united states at the sector level the empirical model is advantageous in that it typically allows simultaneous shock transmission in the conditional returns and volatilities insofar as volatility transmission across oil and stock sector markets is a crucial element for portfolio designs and risk management we also analyze the optimal weights and hedge ratios for oil stock portfolio holdings with respect to the results our findings point to the existence of significant volatility spillover between oil and sector stock returns however the spillover is usually unidirectional from oil markets to stock markets in europe but bidirectional in the united states our back testing procedures finally suggest that taking the cross market volatility spillovers estimated from the var garch models often leads to diversification benefits and hedging effectiveness better than those of commonly used multivariate volatility models such as the ccc garch of bollerslev 1990 the diagonal bekk garch of engle and kroner 1995 and the dcc garch of engle 2002

does high frequency crude oil futures data contain useful information for predicting volatility in the us stock market new evidence abstract this study examines whether high frequency crude oil futures data contain useful information to forecast the realized volatility rv of the us stock market from both in and out of sample perspectives there are several significant findings first from the in sample analysis crude oil futures rv exhibits a significant positive impact on the future s p 500 volatility second the out of sample results reveal that the prediction models including crude oil futures rv outperform the related competing models implying that crude oil rv is an important predictive factor for the us stock market third we further find that the primary forecasting ability of crude oil rv is reflected in high frequency information negative crude oil rv and high volatility level finally the out of sample empirical results based on different forecasting windows alternative forecast evaluation approaches subsample analysis different prediction models alternative midas lags and controlling the leverage effect are robust to our conclusions

long term forecasts for energy commodities price what the experts think the ability to forecast energy prices in the long term is important for a wide range of reasons from the formulation of countries energy and transportation policies to the defensive strategies of nations to investment decisions within the private sector despite the importance of these predictions however forecasters and market pundits face a difficult challenge when trying to forecast over the long term while statistical models can credibly rely on assumptions about the relationship between variables in the short term they are frequently less reliable in the long term as political and technological transformations profoundly change how the economy works over time towards improving long term predictions for energy commodities this paper uses the elicitation and aggregation of experts beliefs to put forward forecasts for crude oil and natural gas prices by incentivizing experts to tell the truth and minimising their own biases through the application of the bayesian truth serum with this approach we generated both short term and long term forecasts and used the short term forecast to validate the quality of the experts predictions

modelling oil price volatility with structural breaks in this paper we provide two main innovations i we analyze oil prices of two prominent markets namely west texas intermediate wti and brent using the two recently developed tests by narayan and popp 2010 and liu and narayan 2010 both of which allow for two structural breaks in the data series and ii the latter method is modified to include both symmetric and asymmetric volatility models we identify two structural breaks that occur in 1990 and 2008 which coincidentally correspond to the iraqi kuwait conflict and the global financial crisis respectively we find evidence of persistence and leverage effects in the oil price volatility while further extensions can be pursued the consideration of asymmetric effects as well as structural breaks should not be jettisoned when modelling oil price volatility

a new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle abstract this paper introduces a general procedure for decomposition of non stationary time series into a permanent and transitory component allowing both components to be stochastic the permanent component is shown to be a random walk with drift and the transitory or cyclical component is a stationary process with mean zero the decomposition methodology which depends only on past data and therefore is computable in real time is applied to the problem of measuring and dating business cycles in the portwar u s economy we find that measured expansions and contractions are of roughly equivalent duration and that our dating of cyclical episodes tends to lead the traditional nber dating and to a lesser extent the growth cycle chronology of zarnowitz and boschan 1977

a novel hybrid method for crude oil price forecasting abstract forecasting crude oil price is a challenging task given the nonlinear and time varying characteristics of international crude oil prices we propose a novel hybrid method to forecast crude oil prices first we use the ensemble empirical mode decomposition eemd method to decompose international crude oil price into a series of independent intrinsic mode functions imfs and the residual term then the least square support vector machine together with the particle swarm optimization lssvm pso method and the generalized autoregressive conditional heteroskedasticity garch model are developed to forecast the nonlinear and time varying components of crude oil prices respectively next the forecasted crude oil prices of each component are summed as the final forecasted results of crude oil prices the results show that the newly proposed hybrid method has a strong forecasting capability for crude oil prices due to its excellent performance in adaptation to the random sample selection data frequency and structural breaks in samples furthermore the comparison results also indicate that the new method proves superior in forecasting accuracy to those well recognized methods for crude oil price forecasting

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 analysis of oil production by opec countries persistence breaks and outliers this study examines the time series behaviour of oil production for opec member countries within a fractional integration modelling framework recognizing the potential for structural breaks and outliers the analysis is undertaken using monthly data from january 1973 to october 2008 for 13 opec member countries the results indicate there is mean reverting persistence in oil production with breaks identified in 10 out of the 13 countries examined thus shocks affecting the structure of opec oil production will have persistent effects in the long run for all countries and in some cases the effects are expected to be permanent

oil price fluctuations and singapore economy this study finds that the impact of an oil price shock on the singapore economy is marginal both impulse response and variance decomposition analysis provide reasonable grounds to believe that the impact only had an insignificant adverse effect on singapore s gross domestic product gdp inflation and unemployment rates further analysis on two oil vulnerability measures supports the finding the declining trend of oil intensity in singapore since 1989 and the declining shares of the singapore s expenditure on oil consumption as a percentage of its nominal gdp this study identifies however that the impact of an oil price shock on the singapore economy should not be considered negligible even though it is small

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

are bond financed deficits inflationary a ricardian analysis this paper considers the possible theoretical validity of the following monetarist hypothesis that a constant positive government budget deficit can be maintained permanently and without inflation if it is financed by the issue of bonds rather than money the question is studied in a discrete time perfect foresight version of the competitive equilibrium model of sidrauski modified by the inclusion of government bonds as a third asset it is shown that the monetarist hypothesis is invalid if the deficit is defined exclusive of interest payments but that it is valid under the conventional definition it is also shown that the stock of bonds can grow indefinitely at a rate in excess of the rate of output growth provided that the difference is less than the rate of time preference these formal conclusions do not take account of possible limitations on taxing capacity arising from default incentives that tend to grow with the stock of bonds

forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models this paper extends previous studies by investigating the relevance of structural breaks and long memory in modeling and forecasting the conditional volatility of oil spot and futures prices using a variety of garch type models our results can be summarized as follows first we provide evidence of parameter instability in five out of nine garch based conditional volatility processes for energy prices second long memory is effectively present in all the series considered and a figarch model seems to better fit the data but the degree of volatility persistence diminishes significantly after adjusting for structural breaks finally the out of sample analysis shows that volatility models accommodating instability and long memory characteristics of the data provide the best volatility forecasts for most cases

a nonparametric garch model of crude oil price return volatility the use of parametric garch models to characterise crude oil price volatility is widely observed in the empirical literature in this paper we consider an alternative approach involving nonparametric method to model and forecast oil price return volatility focusing on two crude oil markets brent and west texas intermediate wti we show that the out of sample volatility forecast of the nonparametric garch model yields superior performance relative to an extensive class of parametric garch models these results are supported by the use of robust loss functions and the hansen s 2005 superior predictive ability test the improvement in forecasting accuracy of oil price return volatility based on the nonparametric garch model suggests that this method offers an attractive and viable alternative to the commonly used parametric garch models

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

modeling and explaining the dynamics of european union allowance prices at high frequency in this paper we model the adjustment process of european union allowance eua prices to the releases of announcements at high frequency controlling for intraday periodicity volatility clustering and volatility persistence we find that the high frequency eua price dynamics are very well captured by a fractionally integrated asymmetric power garch process the decisions of the european commission on second national allocation plans have a strong and immediate impact on eua prices on the other hand our results suggest that eua prices are only weakly connected to indicators about the future economic development as well as the current economic activity

a data envelopment analysis based framework for the relative performance evaluation of competing crude oil prices volatility forecasting models abstract forecasts of crude oil prices volatility are important inputs to many decision making processes in application areas such as macroeconomic policy making risk management options pricing and portfolio management despite the fact that a large number of forecasting models have been designed to forecast crude oil prices volatility so far the relative performance evaluation of competing forecasting models remains an exercise that is unidimensional in nature to be more specific most studies tend to use several criteria and their measures to assess the relative performance of these models but competing models are always ranked by performance measure thus leading in general to different rankings for different criteria and to a situation where one cannot make an informed decision as to which model performs best with respect to all criteria under consideration the purpose of this paper is to propose a single ranking that takes account of several criteria using a data envelopment analysis framework our empirical results reveal that the unidimensional rankings for different criteria might differ significantly and that the multidimensional ranking of some models could be substantially different from their unidimensional rankings which highlights the importance of the proposed performance evaluation tool

the efficiency of the crude oil markets evidence from variance ratio tests this study examines the random walk hypothesis for the crude oil markets using daily data over the period 1982 2008 the weak form efficient market hypothesis for two crude oil markets uk brent and us west texas intermediate is tested with non parametric variance ratio tests developed by wright j h 2000 alternative variance ratio tests using ranks and signs journal of business and economic statistics 18 1 9 and belaire franch j and contreras d 2004 ranks and signs based multiple variance ratio tests working paper department of economic analysis university of valencia as well as the wild bootstrap variance ratio tests suggested by kim j h 2006 wild bootstrapping variance ratio tests economics letters 92 38 43 we find that the brent crude oil market is weak form efficiency while the wti crude oil market seems to be inefficiency on the 1994 2008 sub period suggesting that the deregulation have not improved the efficiency on the wti crude oil market in the sense of making returns less predictable

modelling oil price volatility in this paper we examine the volatility of crude oil price using daily data for the period 1991 2006 our main innovation is that we examine volatility in various sub samples in order to judge the robustness of our results our main findings can be summarised as follows 1 across the various sub samples there is inconsistent evidence of asymmetry and persistence of shocks and 2 over the full sample period evidence suggests that shocks have permanent effects and asymmetric effects on volatility these findings imply that the behaviour of oil prices tends to change over short periods of time

the linkage between oil price shocks and economic growth with inflation in the presence of technological advances a cge model abstract this study examines whether oil price shocks are inflationary in the us we increase the price of oil in the year 2000 in a manner consistent with the oil price shock of 1973 74 and let the economy experience a hicksian technological change then using a dynamic computable general equilibrium cge model we conduct our analyses under two separate cases 1 regular economic growth and 2 low economic growth we also run three technological scenarios 1 no technology change 2 technological advances in the manufacturing and refining sectors and 3 technological advances in the manufacturing refining chemical and service sectors the effects of these changes are analyzed over the next 20 years until the year 2020 our results suggest that while a shock of the magnitude experienced in the 1970s will have a fairly severe effect on such things as gasoline and refinery prices the aggregate price changes will be largely dissipated over time at the aggregate level furthermore the aggregate level of prices cpi and ppi will fall over time as the level of technological advances rise under both growth scenarios there are several reasons why we would obtain such results first of all the structure of the us economy has changed remarkably since the early 1970s rather than being a manufacturing based economy the us is largely a service based economy today and hence it is more protected form raw materials shortages second the economy has had a steady history of strong growth and the faster an economy grows the quicker disruptions to that economy are dissipated finally our economy is experiencing rapid technological advances in information systems which have served to reduce costs and maintain output in a wide number of economic sectors

an information diffusion based model of oil futures price abstract inspired by the increasing evidence of financialization speculation in commodity pricing this paper constitutes a first attempt to build an information diffusion based asset pricing framework for the oil futures market with gradual information dissemination slowly decaying uncertainty about the asset s future fundamentals generates persistent conditional volatility and a drift in asset return volatility based proxies for information flows are proposed to examine empirically the asset pricing implications the results confirm a significant intertemporal relationship between return on the price of oil futures information diffusion and volatility components an important implication of our study is that the slow diffusion of information generates predictability in price dynamics a forecasting model is then constructed and tested in relation to our theory it is found that the lagged series of the pricing factors possess significant predicting power for returns

estimating oil price value at risk using the historical simulation approach abstract in this paper we propose using value at risk var for oil price risk quantification var provides an estimation for the maximum oil price change associated with a likelihood level and can be used for designing risk management strategies we analyse three var calculation methods the historical simulation standard approach the historical simulation with arma forecasts hsaf approach developed in this paper and the variance covariance method based on autoregressive conditional heteroskedasticity models forecasts the results obtained indicate that hsaf methodology provides a flexible var quantification which fits the continuous oil price movements well and provides an efficient risk quantification

volatility spillovers in commodity markets a large t vector autoregressive approach abstract prices of commodities have shown large fluctuations a high volatility of one commodity today may impact the volatility of another commodity tomorrow as such agricultural and energy commodities are closely dependent due to the expansion of the biofuel industry we study volatility spillovers among a large number of energy agriculture and biofuel commodities using the vector auto regressive var model to account for the possible fat tailed distribution of the model errors we propose the t lasso method for obtaining a large var the t lasso is shown to have excellent properties and a forecast analysis shows that the t lasso attains better forecast accuracy than standard estimators our empirical analysis shows the existence of volatility spillovers between energy and biofuel and between energy and agricultural commodities

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

the asymmetry of the impact of oil price shocks on economic activities an application of the multivariate threshold model this paper applies the multivariate threshold model to investigate the impacts of an oil price change and its volatility on economic activities changes in industrial production and real stock returns the statistical test on the existence of a threshold effect indicates that a threshold value does exist using monthly data of the us canada and japan during the period from 1970 to 2002 we conclude i the optimal threshold level seems to vary according to how an economy depends on imported oil and the attitude towards adopting energy saving technology ii an oil price change or its volatility has a limited impact on the economies if the change is below the threshold levels iii if the change is above threshold levels it appears that the change in oil price better explains macroeconomic variables than the volatility of the oil price and iv if the change is above threshold levels a change in oil price or its volatility explains the model better than the real interest rate

daily seasonality in crude oil returns and volatilities abstract in this article we test for the existence of daily seasonality in returns and volatilities of crude oil using a dummy augmented garch specification for the period from may 1987 to october 2013 our key findings are as follows i volatilities on mondays are significantly higher than on all other weekdays providing the important insight that seasonal effects should be considered when forecasting crude oil volatility ii returns on the other hand tend to be lower on mondays than on other weekdays suggesting profitable investment strategies based on this seasonal pattern in fact the analysis of a simple long short trading rule based on the monday effect provides some evidence that it can outperform a passive buy and hold approach however it cannot do so to an extent that is statistically significant iii our seasonality results are fairly robust to the choice of other frequently used garch model variants like garch m tgarch and cgarch

the impact of oil prices on gdp in european countries an empirical investigation based on asymmetric cointegration this paper investigates the existence of a long term relationship between oil prices and gdp in 12 european countries to account for the fact that economic activity responds asymmetrically to oil price shocks we propose an approach based on asymmetric cointegration our results show that while standard cointegration is rejected there is evidence for asymmetric cointegration between oil prices and gdp in the majority of the considered european countries

are the crude oil markets becoming weakly efficient over time a test for time varying long range dependence in prices and volatility this paper analyzes the efficiency of crude oil markets brent and west texas intermediate by means of estimating the fractal structure of these time series we test for time varying degrees of long range dependence using the rescaled range hurst analysis and find evidence that this market has become more efficient over time these results are robust for controlling for short term autocorrelation by means of a shuffling procedure

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

forecasting volatility in the biofuel feedstock markets in the presence of structural breaks a comparison of alternative distribution functions abstract the need for research on commodity volatility has grown considerably due to the important role and financialization of commodities in global asset markets this paper examines the volatility forecasting performance of a wide variety of garch based models in the context of biofuel feedstock markets in the presence of structural breaks our sample is also extended to several non renewable energy commodities to evaluate comparatively the volatility forecasting performance across various commodity markets the model specifications allow for different conditional distribution functions in the rolling window estimations a break detection algorithm finds significant evidence of structural breaks in the unconditional variance of all commodity returns under study the out of sample analysis which is based on an up to date model comparison testing procedure reveals that volatility models accommodating structural breaks in the data provide the best volatility forecasts for most cases regarding the relevance of distribution functions the skewed normal distribution dominates in the model confidence sets nevertheless the complex distribution functions do not always outperform simpler ones although true return distribution is asymmetric and heavy tailed

central bank intervention and foreign exchange rates new evidence from figarch estimations abstract in this paper we investigate the effects of official interventions on the short run evolution and volatility of exchange rates to this aim we rely on a new measure of volatility implied by the figarch model that outperforms the traditionally used garch one it is found that central bank interventions exert an incorrectly signed effect on the levels of exchange rates and tend to increase their volatility in the short run in general our results also show that the traditional garch estimations tend to underestimate the effects in terms of volatility

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

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

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

the empirical relationship between energy futures prices and exchange rates abstract this paper investigates the interaction between energy futures prices and exchange rates results are presented to show that futures prices for crude oil heating oil and unleaded gasoline are co integrated with a trade weighted index of exchange rates this is important because it means that there exists a long run equilibrium relationship between these four variables granger causality results for both the long and short run are presented evidence is also presented that suggests exchange rates transmit exogenous shocks to energy futures prices

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

forecasting crude oil market volatility further evidence with jumps this paper analyzes volatility models and their forecasting abilities in the presence of jumps in two crude oil markets brent and west texas intermediate wti between january 6th 1992 and december 31st 2014 we compare a number of garch type models that capture short memory as well as asymmetry garch gjr garch and egarch estimated on raw returns to three competing approaches that deal with the presence of jumps garch type models estimated on jump filtered returns and two new classes of volatility models called generalized autoregressive score gas and markov switching multifractal msm models estimated using raw returns the forecasting performance of these volatility models is evaluated using the model confidence set approach which allows us to identify a subset of models that outperform all the other competing models we find that asymmetric models estimated on filtered returns provide better out of sample forecasts than do garch gas type and msm models estimated on raw return series for brent and wti returns

are the crude oil markets really becoming more efficient over time some new evidence abstract we replicate the study of tabak cajueiro 2007 are the crude oil markets becoming weakly efficient over time a test for time varying long range dependence in prices and volatility published in energy economics 29 pp 28 36 the results have been mostly confirmed specifically we have confirmed that the crude oil markets efficiency could be rejected up till approximately 1994 and this holds for both studied crude oil commodities brent and wti analyzing an extended dataset up till june 2017 we find that the markets remained efficient at least with respect to long range dependence until the outbreak of the global financial crisis in 2008 this is confirmed by all three applied methods the rescaled range analysis used in the original study and the detrended fluctuation analysis and the geweke porter hudak estimator which were added to strengthen validity of the results the markets returned back to efficiency around 2012 and remained there until 2015 when the hurst exponent started another rally and stayed rather high until the end of the examined sample comparing the two markets the brent crude oil shows stronger signs of inefficiency during the inefficient periods compared to the wti crude oil this is in hand with the results reported in the original study apart from rerunning the analysis on an extended dataset and using two additional methods we also provide a firmer validity check using the moving block bootstrap procedure which outperforms the original shuffling procedure in the provided forecasting study

energy risk management and value at risk modeling abstract the value of energy trades can change over time with market conditions and underlying price variables the rise of competition and deregulation in energy markets has led to relatively free energy markets that are characterized by high price shifts within oil markets the volatile oil price environment after opec agreements in the 1970s requires a risk quantification value at risk has become an essential tool for this end when quantifying market risk there are various methods for calculating value at risk the methods we introduced in this paper are historical simulation arma forecasting and variance covariance based on garch modeling approaches the results show that among various approaches the hsaf methodology presents more efficient results so that if the level of confidence is 99 the value at risk calculated through hsaf methodology is greater than actual price changes in almost 97 6 percent of the forecasting period

oil price fluctuation volatility spillover and the ghanaian equity market implication for portfolio management and hedging effectiveness abstract this study attempts to contribute to the literature on stock markets and energy prices by examining the dynamic volatility and volatility transmission between oil and ghanaian stock market returns in a multivariate setting using the recently developed var garch var agarch and dcc garch frameworks in turn the models results are used to compute and analyze the optimal weights and hedge ratios for oil stock portfolio holdings for comparison purposes and to put the paper more in the perspective of west africa the nigerian stock market is also included in the analysis our findings point to the existence of significant volatility spillover and interdependence between oil and the two stock market returns while spillover effects are stronger for nigeria the transmission of volatility is much more apparent from oil to stock than from stock to oil in the case of ghana also the study demonstrates evidence of short term predictability in oil and stock price changes through time and reveals that conditional volatility changes more rapidly as result of substantial effects of past volatility rather than past news shocks for all market returns moreover we show that there is a slightly more effective hedge in the two stock markets under the dcc garch framework our preferred model compared to the other two models although hedging effectiveness is much greater for ghana on the whole our findings for optimal hedge ratios are consistent with other studies and particularly the view that oil assets should be an integral part of a diversified portfolio of stocks and suggest that a better understanding of volatility links is crucial for portfolio management in the presence of oil price risk finally the existence of multivariate asymmetric effects and dynamic conditional correlations as revealed by the var agarch and dcc garch models make it clear that the assumptions of symmetric effects and constant conditional correlations are not supported empirically

more power to the pill the impact of contraceptive freedom on women s life cycle labor supply the release of enovid in 1960 the first birth control pill afforded u s women unprecedented freedom to plan childbearing and their careers this paper uses plausibly exogenous variation in state consent laws to evaluate the causal impact of the pill on the timing of first births and extent and intensity of women s labor force participation the results suggest that legal access to the pill before age 21 significantly reduced the likelihood of a first birth before age 22 increased the number of women in the paid labor force and raised the number of annual hours worked

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

volatility persistence in crude oil markets financial market participants and policy makers can benefit from a better understanding of how shocks can affect volatility over time this study assesses the impact of structural changes and outliers on volatility persistence of three crude oil markets brent west texas intermediate wti and organization of petroleum exporting countries opec between january 2 1985 and june 17 2011 we identify outliers using a new semi parametric test based on conditional heteroscedasticity models these large shocks can be associated with particular event patterns such as the invasion of kuwait by iraq the operation desert storm the operation desert fox and the global financial crisis as well as opec announcements on production reduction or us announcements on crude inventories we show that outliers can bias i the estimates of the parameters of the equation governing volatility dynamics ii the regularity and non negativity conditions of garch type models garch igarch figarch and hygarch and iii the detection of structural breaks in volatility and thus the estimation of the persistence of the volatility therefore taking into account the outliers on the volatility modelling process may improve the understanding of volatility in crude oil markets

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

modeling and forecasting the volatility of petroleum futures prices we investigate volatility models and their forecasting abilities for three types of petroleum futures contracts traded on the new york mercantile exchange west texas intermediate crude oil heating oil 2 and unleaded gasoline and suggest some stylized facts about the volatility of these futures markets particularly in regard to volatility persistence or long memory properties in this context we examine the persistence of market returns and volatility simultaneously using the following arfima garch class models arima garch arfima garch arfima igarch and arfima figarch although the arfima figarch model better captures long memory properties of returns and volatility the out of sample analysis indicates no unique model for all three types of petroleum futures contracts suggesting that investors should be careful when measuring and forecasting the volatility risk of petroleum futures markets

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

market risk in commodity markets a var approach we put forward value at risk models relevant for commodity traders who have long and short trading positions in commodity markets in a 5 year out of sample study on aluminium copper nickel brent crude oil and wti crude oil daily cash prices and cocoa nearby futures contracts we assess the performance of the riskmetrics skewed student aparch and skewed student arch models while the skewed student aparch model performs best in all cases the skewed student arch model delivers good results and its estimation does not require non linear optimization procedures as such this new model could be relatively easily integrated in a spreadsheet like environment and used by market practitioners

the backing of government bonds and monetarism abstract this paper examines the implications of the fiscal backing of government bonds for the effects of conventional macroeconomic policies and in particular for certain monetarist propositions it shows that the validity of some basic monetarist hypotheses requires a considerable degree of accommodation by the fiscal authority relative to the central bank otherwise government bonds may matter in a manner as described in the traditional literature e g mundell 1971 patinkin 1965 though some differences arise of perhaps independent interest the framework developed in the analysis is an intertemporal general equilibrium model with all the descriptive features of the conventional flexible price is lm model

superstitious seasonality in precious metals markets evidence from garch models with time varying skewness and kurtosis in this article we analyse whether the friday the 13th effect documented by kolb and rodriguez 1987 can be observed in precious metals markets specifically we use dummy augmented garch models to investigate the impact of this specific calendar day on the conditional means of gold silver palladium and platinum returns the specification of the garch model follows a flexible class recently proposed by leon et al 2005 that incorporates time varying skewness and kurtosis by applying a gram charlier series expansion of the normal density function our results for the period from july 1996 to august 2013 provide three important insights first there is no evidence that human superstition regarding bad luck fridays affects precious metals markets in a negative way i e returns on fridays the 13th are not significantly lower than on regular fridays second besides showing robustness in a variety of settings we can confirm this main result in a sensitivity check where we replace the dummy variables

beyond demand and supply curves in macroeconomics this paper surveys recent issues in macroeconomics from the viewpoint of dynamic economic theory the need to look beyond demand and supply curves and the insights that come from doing so are emphasized examples of issues in debt management and fiscal policy are analyzed

which determinant is the most informative in forecasting crude oil market volatility fundamental speculation or uncertainty abstract this paper aims to identify the most informative determinant in forecasting crude oil market volatility we use a new garch class model based on mixed data sampling regression and the dynamic model averaging combination method to examine the predicting power of the determinants we integrate both the global economic policy uncertainty gepu indices and several national economic policy uncertainty epu indices with traditional determinants such as global oil demand supply and speculation our analysis suggests that the epu indices comprehensively integrate the information contained in other determinants specifically gepu indices and the u s s epu index have superior predictive powers for west texas intermediate spot oil volatility this finding highlights the importance of epu indices implying that they are key factors to consider when determining crude oil market volatility

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

volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory abstract this paper explores the relevance of asymmetry and long memory in modeling and forecasting the conditional volatility and market risk of four widely traded commodities crude oil natural gas gold and silver a broad set of the most popular linear and nonlinear garch type models is used to investigate this relevancy our in sample and out of sample results show that volatility of commodity returns can be better described by nonlinear volatility models accommodating the long memory and asymmetry features in particular the fiaparch model is found to be the best suited for estimating the var forecasts for both short and long trading positions this model also gives for all four commodities the lowest number of violations under the basel ii accord rule given a risk exposure at the 99 confidence level several implications for commodity market risks policy regulations and hedging strategies can be drawn from the obtained results