Market risk in commodity markets: a VaR approach
TL;DR: In this paper, the authors put forward Value-at-risk models relevant for commodity traders who have long and short trading positions in commodity markets, such as aluminium, copper, nickel, Brent crude oil and WTI crude oil daily cash prices and cocoa nearby futures contracts.
About: This article is published in Energy Economics.The article was published on 2003-09-01. It has received 281 citations till now. The article focuses on the topics: West Texas Intermediate & Brent Crude.
Citations
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TL;DR: This paper used several univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns and evaluated the out-of-sample forecasts using forecast accuracy tests and market timing tests.
460 citations
Journal Article•
360 citations
TL;DR: In this article, the authors used a greater number of linear and nonlinear generalized autoregressive conditional heteroskedasticity (GARCH) class models to capture the volatility features of two crude oil markets.
356 citations
TL;DR: The authors investigated the efficacy of a volatility model for three crude oil markets (Brent, Dubai, and WTI) with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory.
345 citations
Posted Content•
TL;DR: The authors evaluate the performance of an extensive family of ARCH models in modelling daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes.
Abstract: We evaluate the performance of an extensive family of ARCH models in modelling daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce better one-step-ahead VaR forecasts; second, the choice of sample size is important for the accuracy of the forecast, whereas the specification of the conditional mean is indifferent. Finally, the ARCH structure producing the most accurate forecasts is different for every portfolio and specific to each equity index.
242 citations
References
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TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Abstract: Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced in this paper. These are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances. For such processes, the recent past gives information about the one-period forecast variance. A regression model is then introduced with disturbances following an ARCH process. Maximum likelihood estimators are described and a simple scoring iteration formulated. Ordinary least squares maintains its optimality properties in this set-up, but maximum likelihood is more efficient. The relative efficiency is calculated and can be infinite. To test whether the disturbances follow an ARCH process, the Lagrange multiplier procedure is employed. The test is based simply on the autocorrelation of the squared OLS residuals. This model is used to estimate the means and variances of inflation in the U.K. The ARCH effect is found to be significant and the estimated variances increase substantially during the chaotic seventies.
20,728 citations
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
17,555 citations
Book•
01 Jan 1997TL;DR: In this paper, Campbell, Lo, and MacKinlay present an attempt by three well-known and well-respected scholars to fill an acknowledged void in the empirical finance literature, a text covering the burgeoning field of empirical finance.
Abstract: This book is an ambitious effort by three well-known and
well-respected scholars to fill an acknowledged void in the
literature—a text covering the burgeoning field of empirical finance.
As the authors note in the preface, there are several excellent books
covering financial theory at a level suitable for a Ph.D. class or as
a reference for academics and practitioners, but there is little or
nothing similar that covers econometric methods and applications.
Perhaps the closest existing text is the recent addition to the Wiley
Series in Financial and Quantitative Analysis. written by Cuthbertson
(1996). The major difference between the books is that Cuthbertson
focuses exclusively on asset pricing in the stock, bond, and foreign
exchange markets, whereas Campbell, Lo, and MacKinlay (henceforth CLM)
consider empirical applications throughout the field of finance,
including corporate finance, derivatives markets, and market
microstructure. The level of anticipation preceding publication
can be partly measured by the fact that at least three reviews
(including this one) have appeared since the book arrived. Moreover,
in their reviews, both Harvey (1998) and Tiso (1998) comment on the
need for such a text, a sentiment that has been echoed by numerous
finance academics.
7,169 citations
TL;DR: In this article, the authors examined the relation between stock returns and stock market volatility and found that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns.
4,348 citations
TL;DR: In this paper, a Monte-Carlo analysis of stock market returns was conducted and it was found that not only there is substantially more correlation between absolute returns than returns themselves, but the power transformation of the absolute return also has quite high autocorrelation for long lags.
3,462 citations