Volatility Regimes in the US

Transcription

1 Audrey Costabile and Zoltán Nagy Introduction Despite extensive stimulus efforts of central banks, governments, and the IMF, it is still not clear if the global and the US economies are closer to a double-dip recession or to a recovery. In such a disorienting environment, market participants foresight is more blurred than usual, and news headlines have a larger impact on asset prices. As a result, the current economic environment is prone to sudden reversals of investor sentiment. The Chicago Board Options Exchange Market Volatility Index (VIX), which is based on the volatility implied by option prices, provides one view of investor sentiment, and market participants consider it as an indicator of the overall level of risk aversion. During 2012, the VIX fluctuated around its long-run average level, with a peak in early June (see Figure 1). This spike was the result of resurging troubles in the Spanish banking system coupled with the uncertain outcome of the Greek elections. 1 Although this peak formed gradually this year, the deterioration of investor sentiment manifested itself as a sharp increase in the level of VIX. 2 In the past, rapid increases in implied volatility were often coupled with a general decline in equity prices, adversely affecting investors. In the aggregate, this is still widely believed. Using the Barra US Equity Model (USE4), however, investors can benefit from more granular observations. 3 USE4 separates stock-specific sources from systematic, or common factor, sources of risk and return. In this report, we look at the behavior of common factors such as style and industry factors in different volatility regimes, focusing on the ones where implied volatility increased most rapidly. We find that a rapid increase in volatility was not always accompanied by an across the board decline in factor returns. This variety of factor behavior is reassuring for investors, as it indicates that at least historically some factors could provide a hedge against market decline during periods of rapid increase in implied volatility For more details, refer to The Barra US Equity Model (USE4) Methodology Notes. Please refer to the disclaimer at the end of this document 1of 7

2 Figure 1: VIX and USE4 Country Factor Risk Forecast, Short Horizon Model, January 2000 July VIX USE4S Risk Forecast Volatility Regimes and Factor Returns In order to understand the effects of sharp changes in volatility, we apply the same methodology developed for the Global Market to the US Market. 4 As a basis of our analysis, we use data from July 1995 through July We begin by first calculating the monthly change in the value of the VIX on a weekly basis. The 892 weeks ranging from July 1995 to July 2012 are then sorted into five quintiles based on the speed of change of the VIX (see Table 1). To study the behavior of USE4 factors in different environments, we calculate the average daily return of each USE4 factor (scaled to a monthly horizon) in each volatility regime. 4 Global Market Report - What Do We Know About Rapid Increases in Risk? - Rachael Smith and Oleg Ruban - May Please refer to the disclaimer at the end of this document 2of 7

3 Table 1: Upper Bounds of the Monthly Change in VIX in the Five Regimes, July 1995 July Regimes Upper Bound of Change in VIX Rapid decrease Decrease Stable 0.57 Increase 2.53 Rapid increase From an investor s standpoint, the behavior of style factors during regimes with rapid increases in risk aversion is particularly important. As Table 2 indicates, Momentum, Dividend Yield, and Earnings Yield were the best performers in rapidly increasing risk regimes. In other words, in times of increasing implied volatility, stocks with good recent performance, and low valuation with respect to dividends and earnings, offered a hedge. Size and Non-Linear Beta also had a positive performance, but to a lesser degree. In contrast, Residual Volatility, Leverage, Book to Price, and Liquidity performed poorly during periods of rapid increases in implied volatility. The relationship between volatility regimes and factors is particularly strong for the Size, Residual Volatility, and Liquidity factors. 5 The Size factor tended to perform better in increasing volatility regimes, while the Residual Volatility and Liquidity factors tended to perform better in decreasing volatility regimes. 5 As measured by the correlation between a dummy integer variable representing the regimes and the average factor returns. Please refer to the disclaimer at the end of this document 3of 7

4 Table 2: Average Style Factor Returns in Different Volatility Regimes between July 1995 and July 2012, with July 2012 for Comparison. Factor Rapid decrease Decrease Stable Increase Rapid increase July 2012 Beta Non-Linear Beta Book to Price Dividend Yield Earnings Yield Growth Leverage Liquidity Momentum Residual Volatility Size Non-Linear Size Industry factors also reacted differently to the changing volatility environments. We present summary statistics of their behavior in Table 3, where industry factor returns are grouped by GICS sectors. 6 Having grouped sectors further into broader categories, such as cyclical and defensive, we found an intuitive pattern. We observe that defensive industries tended to outperform in increasing or rapidly increasing volatility environments especially industry factors in the Health Care, Utilities and Consumer Staples sectors while cyclicals tended to underperform during those times, especially industries in the Consumer Discretionary, Materials, and Industrials sectors. Finally, we compare last July s developments with the observed historical patterns. According to our methodology, the last week of July is classified as an increasing volatility regime, while the earlier weeks of the month are either decreasing or rapidly decreasing. To compare July 2012 with historical monthly average returns, we include the month s numbers in the last columns of Table 2 and Table 3. The numbers are highlighted with the color of the regime whose corresponding average return is closest to the July 2012 value. Style and industry factors appear to have reacted differently in July versus their respective averages. While style factors exhibited a pattern closest to a rapidly increasing volatility regime, industry factor patterns are closest to the rapidly decreasing volatility regime. 6 The USE4 model contains 60 industry factors. For each sector, we report the average industry factor returns in each sector by weighting the industry returns with their average estimation universe weight between June 1995 May Please refer to the disclaimer at the end of this document 4of 7

5 Table 3: Average Industry Factor Returns by Sectors in Different Volatility Regimes between July 1995 and July 2012, with July 2012 for Comparison. Sectors Rapid decrease Decrease Stable Increase Rapid increase July 2012 Materials Industrials Cyclicals Consumer Discretionary Financials Information Technology Energy Consumer Staples Defensives Health Care Telecom Services Utilities Conclusion After recent sharp changes in US market volatility, investors have to be more vigilant about their portfolio choices. In this paper we analyzed different volatility regimes through the lens of the Barra US Equity Model (USE4) to gain a better understanding of equity price movements at a more granular level. Building on ideas developed in our May 2012 Global Market Report -- What Do We Know About Rapid Increases in Risk? -- we used the same methodology to focus on US equities. Our findings revealed that the average factor returns to the Momentum, Dividend Yield and Earnings Yield factors were positive during rapid increases in risk aversion, and thus could have provided a hedge against deteriorating investor sentiment in US equity markets. Furthermore, industry factor returns in the Health Care, Utilities, and Consumer Staples sectors the typical defensive bets could have performed well during rapidly increasing risk regimes. Please refer to the disclaimer at the end of this document 5of 7

6 Index of s from , A Year for Minimum Volatility, January Normalized Earnings-to-Price Signal, February Does an Apple a Day Make the US Economy Healthier? March omy_healthier.html The Effect of the Bush Dividend Tax Cut, April _april_2012.html Should I Like Facebook s IPO? May Do High Performance REITs Offer Diversification? June Please refer to the disclaimer at the end of this document 6of 7

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