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Time-Varying Predictability of Oil Market Movements Over a Century of Data: The Role of US Financial Stress

Rangan Gupta (), Patrick Kanda (), Aviral Tiwari () and Mark Wohar ()
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Patrick Kanda: THEMA, Université de Cergy-Pontoise

No 201848, Working Papers from University of Pretoria, Department of Economics

Abstract: In this paper we analyze whether a news-based measure of financial stress index (FSI) in the US can predict West Texas Intermediate oil returns and (realized) volatility over the monthly period of 1889:01 to 2016:12, using a dynamic conditional correlation multivariate generalized autoregressive conditional heteroscedasticity (DCC-MGARCH) model. Our results show that, standard linear Granger causality test fails to detect any evidence of predictability. However, the linear model is found to be misspecified due to structural breaks and nonlinearity, and hence, the result of no causality from FSI to oil returns and volatility cannot be considered reliable. When we use the DCC-MGARCH model, which is robust to such misspecifications, in 75 percent and 80 percent of the sample periods, FSI in fact do strongly predict the oil returns and volatility respectively. Overall, our results highlight that FSI is helpful in predicting oil returns and volatility, when one accounts for nonlinearity and regime changes through a robust time-varying model.

Keywords: US Financial Stress Index; DCC-MGARCH; WTI Oil Returns; Realized Volatility (search for similar items in EconPapers)
JEL-codes: C32 Q41 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ene and nep-his
Date: 2018-07
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