What drives dynamic connectedness of the U.S equity sectors during different business cycles?

https://doi.org/10.1016/j.najef.2021.101493Get rights and content

Highlights

  • Asymmetric and time-varying volatility spillover among nine sector equities.

  • Cyclical sectors net volatility shock transmitters (dominate defensive sectors)

  • Default spread, VIX and TED spreads key state variables driving spillovers.

  • Imprecise and uninformative policy signals during economic growth upsurge spillover.

  • Evidence supports cross-hedging and financial contagion across business cycles.

Abstract

The study investigates (i) the time-varying and directional connectedness of nine equity sectors through intra- and inter-sector volatility spillover periods and (ii) assesses the impact of state variables on aggregate volatility spillovers. The study finds about 76% of volatility linkage is associated with cross-sector volatility transmissions. Aggressive sectors, which are sensitive to macroeconomic risk, play the net volatility transmission role. Defensive sectors that are largely immune to macroeconomic risk play the net volatility receiving role. The intensity and direction of volatility transmissions among the sectors vary with economic expansion and recession periods. Over time, some sectors switching from net transmitting to net receiving role and vice versa. Macro and financial market uncertainty variables significantly impact volatility spillover at lower volatility spillover (economic expansion period) and higher volatility (economic recession periods) volatility spillover quantiles. Political signals are seemingly more imprecise and uninformative during economic expansion or low quantiles, intensifying volatility spillover. Overall, the causal effects of macro, financial, and policy uncertainty variables on aggregate volatility spillover are asymmetric, nonlinear, and time-varying. The study's result supports the cross-hedging and financial contagion views of volatility transmission across nine US equity sectors.

Introduction

The domestic sector equities have become increasingly important to investors for multiples reasons. First, the emergence and growth of sector-based exchange-traded funds (ETFs) have enabled investors to diversify their portfolios at minimal cost instantly. Second, Phylaktis and Xia (2006) show that in the long term, the industry-level portfolio diversification dominates the country-level diversification effects in Europe and North America since 1999. Third, there is ample evidence supporting the home-bias puzzle. Specifically, domestic investors hold sub-optimal portfolios since they overweight (underweight) domestic (international) stocks in their portfolios, thereby forgoing the improved risk-adjusted returns associated with exposure to international stocks. Therefore, domestic sector equities require additional empirical analysis.

Fourth, the theory of sector rotation states that the performance of different sectors relative to the broad market will vary as the economic cycles shift from the full recession, early recovery, late recovery, and finally, early recession. Indeed, the sector rotation strategy, when coupled with value investing (Graham & Dodd, 1934), momentum strategy (Jegadeesh & Titman, 1993), or time-series momentum, has been known to outperform the market significantly. For example, during the early recession cycle, interest rates are highest, while the yield curve is almost inverting, and consumer confidence is rapidly waning. Investors reallocate their capital between defensive and aggressive sectors across economic expansion and recession periods to improve the risk-adjusted returns.

Defensive sectors consist of firms that deal with necessity goods and services with inelastic demand throughout different economic cycles. Examples include utilities (electric, water, gas), staples (personal effects, food, beverage, etc.), and healthcare services (Pharmaceuticals, hospitals, long-term care services, etc.). According to Novy-Marx (2014), defensive sectors are low beta, low volatility assets with relatively stable earnings and minimal vulnerability to economic recessions and expansions. The sectors offer long-term job security in the labor market and low volatility of returns. They are considered safe havens during economic recessions. Aggressive sectors, however, are characterized by high beta and high return volatility, especially during economic recessions. Their earnings and performance are sensitive to macroeconomic fluctuations. However, aggressive stocks also offer higher returns than defensive stocks during economic expansion. Examples include consumer durables, industrial and financial sectors. Therefore, different economic shocks affect sectors heterogeneously. Lastly, specific sectors such as banking, manufacturing, and housing market have been used as conduits of policy interventions by the national governments to redirect capital resources in the economy. For example, banks remain the central conduits of effecting monetary policies.

Most past studies presume that sudden macroeconomic events such as tightening monetary policy or an increase in expected inflation or decline in economic output, or fiscal policy initiatives such as tax cuts will systematically impact the entire market portfolio (Ewing, 2002). However, some economic and financial events, coupled with policy initiatives, directly impact specific sectors within the market. A shock to a particular sector may potentially be transmitted to other sectors in the economy. For example, central banks generally use the banking sector as a conduit of shifting monetary policy shocks to both the real and financial sectors of the economy. Therefore, it is apparent that the policymakers may target different sectors to stabilize the economy1. A shock to one sector is expected to ripple out to the rest of the sectors in the economy2.

The success of portfolio diversification strategies to eliminate idiosyncratic risk in a single sector or across different sectors is predicated on a comprehensive understanding of how, and to what degree, the stocks within each sector interact with each other (intra-sector volatility spillover) as well as inter-sector interactions.

The current study attempts to provide insights on how and to what extent volatility shocks to one sector can explain its fluctuations and instability in other sectors during economic contraction (recession) and economic expansion (growth) cycles. Understanding the volatility linkages among disparate equity sectors is vital to portfolio managers, investors, and regulators. For example, portfolio managers will reallocate capital from high to low volatility sectors to reduce portfolio risk. However, if volatility changes across different sectors are highly correlated, Fleming et al., 1998, Garcia and Tsafack, 2011, and Aboura and Chevallier (2014) note that risk reduction becomes a daunting undertaking since none of the sectors can be a safe haven for portfolio managers. Indeed, the study of the cross-sector interdependence via volatility linkages during periods of economic expansion and economic recessions offers exciting insights on how different sectors respond to volatility shocks to other sectors and what macro and financial factors contribute to cross-sector volatility transmissions.

In asset pricing, volatility of the underlying asset is an essential input in the pricing of derivatives and hedging strategies (James et al., 2012, Jayasinghe and Tsui, 2008). Since investors hold multiples sector-based ETFs and may hedge risk with multiple derivatives such as put and call options, their overall exposure and payoff from the use of derivatives may largely depend on cross-market volatility linkages among the sector ETFs. From a regulatory and policy standpoint, banking regulators, for example, need to understand the direction and intensity of volatility in the design of suitable capital adequacy. Regulators can also identify the sectors generating the most destabilizing effects on the system by transmitting volatility shocks to other sectors. Policymakers and stock market regulators need to understand the source, direction, and intensity of volatility spillover in setting margin requirements, which subsequently influences investors' risk-taking behavior, volatility, and trading activity (Chance, 1990).

In the first stage analysis, the study employs the newly developed, ordering-invariant generalized forecast error variance decomposition (GFEVD) based on the time-varying parameter vector autoregression (TVP-VAR) model to investigate the aggregate and directional volatility linkages among nine sector equities. In the second stage analysis, the study utilizes Quantile Regression (QR) to assess the asymmetric and nonlinear impact of the macro, financial, and policy variables on overall volatility spillover among different sectors.

There is extensive and rich literature on shock and volatility transmissions among national stock markets. Only a few studies have focused on return and volatility interdependence among the US sectors. For example, Ewing (2002) uses monthly data and employs the GFEVD method to investigate volatility transmission among capital goods, financial, industrial, transportation and utility sectors of the S&P index. The author documents evidence of cross-sector volatility spillover. Hassan and Malik (2007) find volatility spillover among six US sectors using a tri-variate Baba, Engle, Kraft, and Kroner (1990) Generalized Autoregressive Conditional Heteroskedasticity (BEKK-GARCH) model. Ben Sita (2013) employs Granger causality tests to investigate volatility transmission among thirty US industries. He documents feedback and feedforward volatility spillover structure. Specifically, volatility of the leading industries ripples to the lagging industries, and the entire system and lagging industries propagate volatility to the leading industries. Elyasiani et al. (2007) find return and volatility interdependence among large and small firms in the commercial banking, securities, and life insurance industries, which comprise the financial services sector. Tsuji (2020) investigates return transmission and volatility spillovers between the US banking sector stocks and eight other international banking sectors. The author finds largely unidirectional (bidirectional) stock return (volatility) transmission from (between) the US banking sector to (and) all the other eight international banking sectors. Majumder and Nag (2018) provide evidence of shock and volatility spillover among India's sector equities, while Kumar, 2017, Wang and Wang, 2019 document evidence of return and/or volatility spillovers between the energy sector and sectoral equity markets of the US and China, respectively.

This study expands the existing literature by tackling several unanswered questions regarding how sectors interact to destabilize the entire financial system. Some questions of interest are as follows. What proportion of volatility shocks does each sector transmit to itself (intra-sector transmission) and to other sectors? (inter-sector transmission)? Does shock transmission intensity vary between economic expansion and recession periods? Which sectors (aggressive or defensive) dominate the network of sectors by being net volatility shock transmitters? What economic, financial, and policy uncertainty factors drive the sectors' dynamic connectedness during economic expansion and recession periods? The answers to these questions are relevant to policymakers and regulators to stabilize the financial system and avoid the harmful impact on the real sector. Investors would gain insights on continuously changing volatility shock transmission in making portfolio rebalancing, hedging strategy, and optimal portfolio allocation decisions.

Theoretically, Ross (1989) intimates that the volatility of an asset's price represents the rate of information flow to the market. The rate of information flow and the information processing speed noticeably varies among different financial assets such as sector indices and ETFs. Expectedly, different sectors should exhibit different volatility patterns (See Fig. 1). As the equity sectors become increasingly integrated, understanding volatility spillover patterns and intensity across different sectors becomes ever more critical to the policymakers, regulators, and investors. The theoretical exposition by Fleming et al. (1998) conjectures cross-markets volatility transmissions over time is originated by cross-market hedging and sharing of common information, which may concurrently modify investors' expectations and beliefs across the markets. An alternative view of how volatility spillover across markets is originated is financial contagion, which Kodres and Pritsker (2002) define as a shock to, say, country (sector) A's financial market that causes changes in asset prices in the country (sector) B's financial market. Financial market contagion arises from the rational expectations of investors on multiple assets. Through cross-market balancing, investors will transmit shocks across sectors by adjusting their portfolios in response to exposure to macroeconomic risks. Therefore, the degree of financial market contagion is chiefly dependent on the sectors' sensitivities to common macroeconomic risk factors and the degree of information asymmetry among the markets. Grobys (2015) concludes that idiosyncratic factors drive financial market uncertainties during calm economic periods. However, during periods of economic turmoil, financial markets are primarily driven by common factors that propagate volatilities and volatility spillover.

The study discusses the key variables, econometric modeling employed in the study, the empirical evidence, and concluding remarks in the subsequent sections.

Section snippets

Data sources

I gather USD-denominated equity indices from Morgan Stanley Capital International (MSCI) website. The sector indices include Consumer Discretionary (CDI), Consumer Staples (CSI), Energy (ENG), Financial (FIN), Health Care (HCI), Industrial (IND), Materials (MAT), Technology (TEC), and Utilities (UTL). The sample period, 06/01/1994 to 01/15/2021, is dictated by data availability and accounts for 1392 weekly observations. I use weekly data to avoid the usual market microstructure problems such as

Preliminary results

According to Table 1, the TEC (ENG) sector returns yielded the highest (lowest) average weekly returns. At the same time, TEC (CSI) had the highest (lowest) risk or standard deviation during the sample period. According to the Sharpe ratio (Mean/Std. Dev), CSI (ENG) is the best (worst) performing sector. The unconditional standard deviations of returns vary across the sectors. The excess kurtosis coefficient is significantly higher than zero signifying leptokurtic returns distribution (presence

Conclusion

The paper seeks to (i) investigate the time-varying dynamic connectedness of nine equity sectors through intra- and inter-sector volatility spillover; (ii) identify directional spillovers during three business cycles and (iii) identify the asymmetric and nonlinear impact of the macro, financial market uncertainty, and policy uncertainty factors that drive aggregate volatility spillovers. The study finds that aggressive sectors, whose performance largely depends on economic cycles, play the net

CRediT authorship contribution statement

Geoffrey M. Ngene: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing.

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