Oil Prices and the Stock Markets: Evidence from High Frequency Data.

• Author: Rahman, Sajjadur

1. INTRODUCTION

   There is an ongoing debate in the macroeconomics literature about the macroeconomic effects of oil price shocks. See, for example, Hamilton (1983, 1996, 2011), Hooker (1996), Kilian (2008), Edelstein and Kilian (2009), Herrera et al. (2011), and Kilian and Vigfusson (2011a,b). As Serletis and Mehmandosti (2018) recently put it, "those of the view that positive oil price shocks have been the major cause of recessions in the United States (and other oil-importing countries) appeal to models that imply asymmetric responses of real output to oil price increases and decreases. These models are able to explain larger recessions is response to positive oil price shocks as well as smaller expansions in response to negative ones. On the other hand, those of the view that positive oil price shocks do not cause recessions appeal to theoretical models of the transmission of exogenous oil price shocks that imply symmetric responses of real output to oil price increases and decreases. These models cannot explain large declines in the level of economic activity in response to unexpected increases in the price of oil."

   In this regard, Elder and Serletis (2010, 2011), Bredin et al. (2011), Rahman and Serletis (2011, 2012), Pinno and Serletis (2013), Jo (2014), and Elder (2018) reinvestigate the relationship between the price of oil and the level of economic activity, focusing on the role of uncertainty about oil prices. They appeal to the real options theory--see, for example, Bernanke (1983), Brennan and Schwartz (1985), Madj and Pindyck (1987), Brennan (1990), Gibson and Schwartz (1990), and Dixit and Pindyck (1994)--and use internally consistent simultaneous equations empirical models that accommodate an independent role for the effects of oil price volatility. They find that oil price uncertainty has had a negative and statistically significant effect on several measures of investment, durables consumption, and aggregate output, and that accounting for the effects of oil price uncertainty tends to exacerbate the negative dynamic response of economic activity to a negative oil price shock, while dampening the response to a positive oil price shock.

   The link between oil prices and stock prices has also been investigated in the literature. See, for example, Jones and Kaul (1996), Park and Ratti (2008), Sadorsky (1999, 2001, 2012), and Alsalman and Herrera (2015). Also, Kilian and Park (2009) estimate the global crude oil market model of Kilian (2009), augmented with a stock market variable. They treat the price of crude oil as endogenous and model changes in the real price of crude oil as arising from three different sources: shocks to the global supply of crude oil, shocks to the global demand for all industrial commodities (including crude oil) that are driven by the global business cycle, and oil-market specific demand shocks (also referred to as precautionary demand shocks). They report that the response of stock prices to oil price shocks depends on the nature of the oil price shocks. In particular, they show that demand and supply shocks driving the global crude oil market jointly account for 22% of the long-run variation in U.S. real stock returns.

   In this paper, we contribute to the literature by using the highest frequency data that have ever been studied before to investigate the relationship between the price of oil and the stock market. In investigating this relationship, we use an empirical approach that is widely different from those of others in the related literature, as we follow Rigobon and Sack (2003) and Wright (2012) to identify oil price shocks through heteroscedasticity of the high frequency dataset. We prefer to work with such dataset, as investors' decision continuously changes due to wealth of information that is frequently available to them. Our modelling framework allows us to investigate the effects of oil price shocks on stock market, after incorporating rapidly changing information content of the high frequency dataset.

   We estimate a bivariate structural VAR using daily data on the price of oil and stock returns to identify oil price shocks and investigate the effects of these shocks on different types of stock market returns that include aggregate and disaggregate U.S. market returns, aggregate and disaggregate U.S. excess returns, the returns of the energy sector based on the Global Industry Classification Standard, and the returns of the major oil and gas companies following Kang et al. (2017). Although most of the previous studies primarily focus on U.S. market returns, we extend our analysis to include global, eurozone, and some country specific stock market returns. We find additional empirical evidence in support of the view that positive oil price shocks have negative and statistically significant effects on the stock market.

   The outline of the paper is as follows. In section 2 we discuss the data and in section 3 we briefly present the empirical model. In section 4 we present the empirical results while in section 5 we check for robustness. The final section concludes.

2. THE DATA

   We use daily data on two variables: changes in the price of oil ([x.sub.t]) and stock returns ([y.sub.t]). They are based on the spot price of West Texas Intermediate (WTI) crude oil and different forms of returns that include aggregate and disaggregate U.S. stock market returns; aggregate and disaggregate U.S. excess returns; the returns of the energy sector based on the Global Industry Classification Standard (GICS); the returns of the major oil and gas companies following Kang et al. (2017)); and global, eurozone, and some country specific stock market returns that include emerging and developed economies. In selecting these countries, we consider data availability, maturity of the stock market, as well as its relations with the rest of the world. However, we did not take into account the nature of their economies (whether these countries are exporting or importing crude oil), as we mainly focus on the movement of the stock market at a higher frequency due to sharp changes in the prices of oil.

   The data on the U.S. and international stock market returns, the returns of the GICS energy sector, and the returns on major oil and gas companies have been collected from NetAdvantage, a subscription only database hosted by Standard and Poor's, and some publicly available finance databases such as FRED, Google, and Yahoo. The data on excess returns are available on the data library that is published and maintained by Kenneth R. French (at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/index.html). We have compiled a complete description of the data in Appendix Tables A1-A3.

3. ECONOMETRIC METHODOLOGY

   We...