Symmetric and asymmetric US sector return volatilities in presence of oil, financial and economic risks
Introduction
In the last two decades, world financial markets have been experiencing erratic volatility at certain times as witnessed by the stock market crash in 1987, the Asian crisis in 1997, the collapse of dotcom stocks in 2000, and the recent Chinese market spillover in 2007. To address these unpredictable excess risks, both financial institutions and regulatory agencies have developed various risk management techniques to deal with extreme market movements in order to protect investors’ portfolios.
In attempting to provide better explanations of the stock volatile movements and better predictions of the volatility, several approaches have been advanced in the empirical studies. First, conditional variance models have been developed to fit clustering volatility (Bollerslev et al., 1992; Nelson, 1991; Glosten et al., 1993; Ding et al., 1993; Engle, 1995, Engle, 2002). A more recent brand of these models pays particular attention to the asymmetrical impact on stock return volatility.1 Second, a larger set of economic variables and more efficient econometric techniques are employed in modeling stock return series in order to reduce the model uncertainty. For instance, in explaining the stock return, Avramov (2002) and Ludvigson and Ng (2007) construct some risk factors that comprise a large amount of information by using Bayesian approach to gain estimation efficiency. Third, in addition to the conditional volatility that employs the GARCH-type models, attempts have been made to link stock volatility to various economic fundamental risks, including sector, industry or firm risks (Fama and French, 1992, 1995), and macroeconomic volatility (Schwert, 1989; Errunza and Hogan, 1998; Flannery and Protopapadakis, 1999). The fourth approach is to find a better measurement of the risk variables to validate the test equation. Andersen et al. (2004) and Ghysels et al. (2005) suggest the use of high frequency data, while Andersen et al. (2003), Andersen et al. (2004), and Engle et al. (2006) suggest employing alternative definitions to measure volatility.
Motivated by the established literature particularly the approach that links stock volatility to various economic fundamental risks, this paper’s purpose is to extend the research by linking sector stock volatility to a broader scope of information set pertinent to policy analysis and global environment. Particularly, the paper emphasizes the role of the oil risk on return volatility of equity sectors of the US economy, given the recent surge in oil prices. Moreover, in addition to the sector-specific factors, price–book ratio, and liquidity effect (Fama and French, 1996; Lamoureux and Lastrapes, 1990), we add macroeconomic variable (Schwert, 1989), and global market volatility (Engle, 1995; Engle et al., 1990; Hamao et al., 1990) into the model. Thus, the model incorporates sectors’ volatility, country factors (macroeconomic variables), and world factors into a unified framework. Our empirical research is connected to a large body of the literature examining the relationship between the stock return volatility and the underlying economic fundamentals. Thus, this paper is not an exercise that tests new techniques.
In sum, the paper provides empirical evidence on stock return volatility behavior by incorporating the presence of world, country, and sector risks. Specifically, the purpose of the paper is five-fold:
- 1.
to examine the responsiveness of the stock return volatility of twenty seven US sectors to the common variables: oil price, world market index, and short-term interest rate;
- 2.
to measure the impacts of the fundamental (sector-specific) variables, namely book/price ratio and trading volume, on the return volatility of those US sectors;
- 3.
to examine the significance of trading volume and whether including trading volume reduces the volatility persistence rate;
- 4.
to assess whether the volatility–volume relationship is significant for both the transitory and permanent components of volatility; and
- 5.
to assess the asymmetric effects in oil price, federal funds rate, and trading volatility on the transitory component of stock return volatility.
This paper is organized as follows. Following this introduction, Section 2 describes the variables’ selection and related literature, and Section 3 discusses the data. Section 4 presents the methodology. Section 5 presents the empirical findings and analyzes the results. Section 6 concludes.
Section snippets
Variables’ selection and literature review
The rationales for the variable selection are briefly stated as follows. The price–book ratio (P/B) provides a measure to assess the value of a stock.2 A high P/B ratio reflects that
The data
In this paper, we use daily DataStream ‘total return’ indices for twenty seven US sectors as classified by the Industrial Classification Benchmark (ICB) which is based on a 4-tier hierarchy4
The models
As described in the introductory section, the purpose of the paper is to examine the characteristics of the return volatility behavior of the US domestic equity sectors in response to the sector financial fundamentals, interest rate, oil shocks, and world stock return based on GARCH-type specifications5
Empirical results
We present in this section the estimation results of the variance equation in each of the two GARCH type models for the twenty seven US sectors. In the standard GARCH model, we focus on the general behavior of the sector aggregate volatility relative to multiple risks and on the econometric implications of adding the trading volume to the variance equation in terms of MLE convergence, predictive power and volatility persistence. In the CGARCH model, we distinguish between the fundamental
Conclusions
The results of the impacts of the different sector-specific fundamentals and global and domestic variables on conditional volatilities, defined in a family of GARCH models, for 27 US equity sectors can be used to construct a mosaic of diversified portfolios to fit investors’ diverse needs. The results are given for the whole sample period 1/2/1989–10/3/2006 and for two subperiods, with the breaking point defined by the surge in commodity prices after the start of the 2003 Iraq war. The results
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