On the links between stock and commodity markets' volatility
TL;DR: In this article, the authors investigated the links between price returns for 25 commodities and stocks over the period from January 2001 to November 2011, by paying a particular attention to energy raw materials.
About: This article is published in Energy Economics.The article was published on 2013-05-01 and is currently open access. It has received 460 citations till now. The article focuses on the topics: Speculation & Stock market.
Citations
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TL;DR: In this article, the authors introduce a sequential monitoring procedure to detect changes in the left-quantiles of asset returns, and to assess whether a tail change in the equity index can be offset by introducing a safe-haven asset into a simple mean-variance portfolio.
358 citations
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TL;DR: In this paper, a rolling window analysis is used to construct out-of-sample one-step-ahead forecasts of dynamic conditional correlations and optimal hedge ratios for emerging market stock prices.
350 citations
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TL;DR: In this paper, the authors examined the time and frequency dynamics of connectedness among stock prices of U.S. clean energy companies, crude oil prices and a number of key financial variables using the methodology developed by Barunik and Krehlik (2018).
332 citations
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TL;DR: In this paper, a rolling window analysis is used to construct out-of-sample onestep-ahead forecasts of dynamic conditional correlations and optimal hedge ratios for emerging market stock prices.
Abstract: While much research uses multivariate GARCH to model volatility dynamics and risk measures, one particular type of multivariate GARCH model, GO-GARCH, has been underutilized. This paper uses DCC, ADCC and GO-GARCH to model volatilities and conditional correlations between emerging market stock prices, oil prices, VIX, gold prices and bond prices. A rolling window analysis is used to construct out-of-sample onestep-ahead forecasts of dynamic conditional correlations and optimal hedge ratios. In most of the situations we study, oil is the best asset to hedge emerging market stock prices. Hedge ratios from the ADCC model are preferred (most effective) for hedging emerging market stock prices with oil, VIX, or bonds. Hedge ratios estimated from the GO-GARCH are most effective for hedging emerging market stock prices with gold in some instances. These results are reasonably robust to choice of model refits, forecast length and distributional assumptions.
315 citations
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TL;DR: A CNN-based framework is suggested, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets.
Abstract: Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next day’s direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL based on various sets of initial variables. The evaluations show a significant improvement in prediction’s performance compared to the state of the art baseline algorithms.
282 citations
References
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TL;DR: In this article, a new class of multivariate models called dynamic conditional correlation models is proposed, which have the flexibility of univariate generalized autoregressive conditional heteroskedasticity (GARCH) models coupled with parsimonious parametric models for the correlations.
Abstract: Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two-step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.
5,695 citations
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TL;DR: In this paper, the problem of estimating the break dates and the number of breaks in a linear model with multiple structural changes has been considered and an efficient algorithm based on the principle of dynamic programming has been proposed.
Abstract: In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T2) for any number of breaks. Our method can be applied to both pure and partial structural change models. Second, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program. Copyright © 2002 John Wiley & Sons, Ltd.
4,026 citations
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TL;DR: In this paper, the problem of estimating the number of break dates in a linear model with multiple structural changes has been studied and an efficient algorithm to obtain global minimizers of the sum of squared residuals has been proposed.
Abstract: In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.
3,836 citations
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TL;DR: This article found that after 1986, oil price movements explained a larger fraction of the forecast error variance in real stock returns than do interest rates, and that oil price volatility shocks have asymmetric effects on the economy.
1,782 citations
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TL;DR: In this paper, the authors test whether the reaction of international stock markets to oil shocks can be justified by current and future changes in real cash flows and/or changes in expected returns.
Abstract: We test whether the reaction of international stock markets to oil shocks can be justified by current and future changes in real cash flows and/or changes in expected returns. We find that in the postwar period, the reaction of United States and Canadian stock prices to oil shocks can be completely accounted for by the impact of these shocks on real cash flows alone. In contrast, in both the United Kingdom and Japan, innovations in oil prices appear to cause larger changes in stock prices than can be justified by subsequent changes in real cash flows or by changing expected returns.
1,570 citations