Global Market Report What Do We Know About Increases in Risk? Rachael Smith and Oleg Ruban Implied Volatility as a Gauge of Sentiment Following a benign first quarter of 2012, recent weeks were characterized by increasing uncertainty in the investment climate. In particular, political instability in Greece brought fears of a complete breakdown in the eurozone back to the top of investors concerns. A disorderly breakdown may cause a significant increase in investor risk aversion, similar to that seen in other financial crises. While this is only one of several possible scenarios, an examination of what happens to stock returns during times of rapidly rising financial distress is timely in the current environment. Common implied volatility measures, such as the VIX and VDAX, reflect the expected standard deviation of stock returns over a period of up to three months, as implied in the prices of options on stock price indices. Implied volatility reflects investors consensus views of future expected stock market volatility and is a common gauge of investor risk aversion and investor sentiment. As Figure 1 shows, implied volatility tends to follow a well-defined pattern in periods of financial distress, rising rapidly and then declining gradually. Measures of implied volatility in different countries show substantial correlation. Theory suggests that a decrease or rapid decrease in volatility implies a sudden positive shift in investor sentiment. An increase or rapid increase in volatility implies a sudden shift in sentiment towards risk aversion. Investor Sentiment and the Cross-Section of Stock Returns Rising investor risk aversion may have a larger effect on some stocks relative to others. For example, Baker and Wurgler (2007) link improvement in investor sentiment, possibly due to a decline in risk aversion, to higher demand for more speculative securities. They conjecture that more speculative securities are likely to be stocks that are young, small, unprofitable, experiencing extreme growth, or stocks with a high degree of idiosyncratic variation in their returns. Conversely, bond-like stocks may be less driven by sentiment. The Barra Equity Models decompose the sources of stock returns into common factors that distinguish between stocks on the basis of their fundamental characteristics. These factors distinguish stocks on the basis of their country and industry membership, as well as their characteristics captured by style factors, such as Residual Volatility, Size, Momentum and Value, which are related to past performance or valuations. Common factors provide a natural framework to analyze how the impact of changes in risk perceptions differs between different segments of the equity market. Please refer to the disclaimer at the end of this document 1of 6
To gain a better understanding of the impact of volatility regimes on the stock returns, we look for commonalities in the performance of the factors of the Barra Global Equity Model (GEM3). Figure 1: Implied volatility in Europe (VDAX) and the US (VIX). 80 70 60 VDAX VIX US accounting scandals 2008 global financial crisis European sovereign debt crisis Implied Volatility (%) 50 40 30 20 10 0 2000 2002 2004 2006 2008 2010 2012 Constructing Implied Volatility Regimes We begin by defining regimes in our measure of implied volatility. On a weekly basis, we look at the rolling monthly change in VIX level, grouping this time series into five quintiles that define our regimes from rapidly decreasing to rapidly increasing VIX levels. We focus our study on the period commencing June 9, 1997 and ending May 15, 2012. Each quintile has 156 weeks in total with the upper bounds of the monthly change in VIX used to define the regime (shown in Table 1 below). Table 1: Upper Bounds of the Monthly Change in VIX to define the five volatility regimes. Change in VIX Upper Bound decreasing -3.50 Decreasing -1.11 Stable 0.48 Increasing 2.75 Increasing 33.34 Please refer to the disclaimer at the end of this document 2of 6
Next, we examine the characteristics of factor returns in each regime, by calculating the average daily factor return for the factors of the Barra Global Equity Model (GEM3). We report the returns scaled to a monthly horizon. Patterns in Factor Returns Table 2 shows the average performance of the GEM3 style factors for the five volatility regimes. Several styles have well-defined patterns in their returns, as investor fear and expectations of future uncertainty vary. Momentum, Size and Size Non-Linearity all tended to improve in performance as sentiment shifted towards risk aversion. Leverage, Liquidity and Residual Volatility performed worse as we moved towards rising and rapidly rising implied volatility. Interestingly, though Earnings Yield performed well in all environments, its performance suffered in the rapidly increasing volatility regime. In contrast to that, Size had a positive average return only in periods of rapidly increasing volatility. Beta, Dividend Yield (and to a lesser degree, Growth) appeared to enjoy more extreme climates, performing best in the extremes (both rapidly increasing/decreasing volatility regimes). Book-to-Price had relatively flat performance in decreasing and flat volatility regimes, whereas its performance worsened in periods of increasing and rapidly increasing volatility. To summarize, historically stocks with positive exposures to Size, Dividend Yield, Momentum and negative exposures to Residual Volatility and leverage may have provided a hedge at times of very rapid shifts in investor sentiment towards risk aversion. These are large cap stocks, stocks that tended to outperform recently, stocks with low idiosyncratic risk and companies with low leverage paying dividends. This result is largely consistent with intuition. One surprise here is Momentum, which is often viewed as a more cyclical bet, but historically appears to have performed well as sentiment deteriorated. Table 2: Performance of the GEM3 style factors in the five volatility regimes and past month. avg monthly factor return (%) decrease Decrease Stable Increase increase Last Month Beta 0.63-0.50-0.24 0.14 0.37-2.44 Momentum -0.60 0.43 0.35 0.74 1.04 1.23 Size -0.34-0.29-0.06-0.07 0.30-0.57 Earnings Yield 0.26 0.35 0.29 0.37 0.09-0.04 Residual Volatility 0.46-0.34-0.37-0.48-0.99-2.03 Growth 0.19 0.03 0.02-0.05 0.06 0.01 Dividend Yield 0.12 0.01-0.02 0.06 0.54-0.11 Book-to-price 0.16 0.17 0.21 0.09-0.11-0.50 Leverage 0.02 0.05 0.03-0.04-0.50-0.06 Liquidity 0.34 0.23 0.18 0.27-0.05 0.03 Size Non-Linearity -0.05 0.08-0.02 0.10 0.12-0.16 Please refer to the disclaimer at the end of this document 3of 6
Let us now examine the performance of industry factors. For ease of presentation we group GEM3 industry factor returns by sector, using the average estimation universe weight 1 of each GEM3 industry in its GICS sector. Table 3 illustrates the average performance of the sectors during the five regimes defined earlier. Defensives performed best in rapidly increasing volatility environments, whereas Cyclicals typically underperformed. In addition, we see a clear trend whereby Defensives increasingly outperformed as sentiment moved towards risk aversion. Table 3: Performance of the constructed GEM3 sectors in the five volatility regimes and past month. avg monthly factor return (%) Cyclicals Defensives decrease Decrease Stable Increase increase Last Month Materials -0.25 0.32 0.53-0.19-0.65-1.08 Industrials -0.15-0.06 0.20-0.20-0.50-0.12 Consumer Discretionary 0.11 0.10-0.02-0.03-0.47 0.54 Financials -0.58-0.09-0.36-0.08 0.07 0.05 Information Technology 1.40-0.04 0.26-0.25-0.46-0.72 Energy -0.34 0.34 0.29 0.32-0.29-1.33 Consumer Staples -0.33 0.08-0.03 0.02 1.06 0.80 Health Care 0.31 0.47-0.26 0.41 1.25 1.22 Telecom Services 0.01-0.75 0.09 0.52 0.84 0.23 Utilities -0.95-0.21-0.33-0.18 1.09 0.03 Comparing Recent Return Patterns with Past Regimes According to our regime classification, the end of April and beginning of are classed as an increasing volatility environment. We examine the performance of the styles and sectors over this period in the last columns of Tables 2 and 3. Similar to the approach used when defining regimes, we took average daily factor returns over the past month and scaled them to a monthly horizon. When comparing the average factor return over the past month with that of the average factor return seen in the five volatility regimes for both the sector and styles, we see that the last few weeks resemble a rapidly increasing volatility regime. The sector returns are most consistent, with only one of the previous month s returns not being of the same sign as the returns of increasing/rapidly increasing volatility regime. The factors that behaved least in line with the typical behaviour of an increasing or rapidly increasing regime were industries in the Consumer Discretionary sector, whose behavior resembled a rapidly decreasing volatility regime. 1 Weights were determined within the GEM3 estimation universe using total market capitalization and averages calculated over the period 31/12/1996-30/09/2011. Please refer to the disclaimer at the end of this document 4of 6
Conclusion Global Market Report The recent uncertainty in the investment climate led us to examine the effects of rapidly deteriorating investor sentiment on stock returns. An increase or rapid increase in implied volatility implies a sudden shift in sentiment towards risk aversion. Our analysis of factor returns in different volatility regimes over the period starting in June 1997 and ending revealed that Size, Size Non-Linearity and Momentum tended to outperform in previous instances of rapidly increasing implied stock market volatilities, while Leverage and Liquidity tended toward underperformance. The sector analysis over the same period showed an intuitive and clear message: Defensives increasingly outperformed, and Cyclicals underperformed as uncertainty and risk aversion increased. Moreover, we discovered that while implied volatilities have only risen moderately in April and, the behavior of factor returns tended to mimic the pattern of a rapid shift in investor sentiment towards fear. References Baker, M., and J. Wurgler (2007), Investor Sentiment in the Stock Market, Journal of Economic Perspectives, vol. 21, no. 2 (Spring), pp. 129 151. Please refer to the disclaimer at the end of this document 5of 6
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