Research Review | 8 Dec 2016 | Volatility & Risk Management

How Should Investors Respond to Increases in Volatility?
Alan Moreira (Yale University) andn Tyler Muir (UCLA)
December 2, 2016
They should reduce their equity position. We study the portfolio problem of a long-horizon investor that allocates between a risk-less and a risky asset in an environment where both volatility and expected returns are time-varying. We find that investors, regardless of their horizon, should substantially decrease risk exposure after an increase in volatility. Ignoring variation in volatility leads to large utility losses (on the order of 35% of lifetime utility). The utility benefits of volatility timing are significantly larger than those coming from expected return timing (i.e., from return predictability) for all investment horizons we consider, particularly when parameter uncertainty is taken into account. We approximate the optimal volatility timing portfolio and find that a simple two fund strategy holds: all investors choose constant weights on a buy-and-hold portfolio and a volatility timing portfolio that scales the risky-asset exposure by the inverse of expected variance. We then show robustness to cases where the degree of mean-reversion in stock returns co-moves with volatility over time.

Shiller’s CAPE: Market Timing and Risk
Valentin Dimitrov (Rutgers University) and Prem C. Jain (Georgetown University)
November 17, 2016
Robert Shiller shows that Cyclically Adjusted Price to Earnings Ratio (CAPE) is strongly associated with future long-term stock returns. This result has often been interpreted as evidence of market inefficiency. We present two findings that are contrary to such an interpretation. First, if markets are efficient, returns on average, even when conditional on CAPE, should be higher than the risk-free rate. We find that even when CAPE is in its ninth decile, future 10-year stock returns, on average, are higher than future returns on 10-year Treasurys. Thus, the results are largely consistent with market efficiency. Only when CAPE is very high, say, CAPE is in the upper half of the tenth decile (CAPE higher than 27.6), future 10-year stock returns, on average, are lower than those on 10-year U.S. Treasurys. Second, we provide a risk-based explanation for the association between CAPE and future stock returns. We find that CAPE and future stock returns are positively associated with future stock market volatility. Overall, CAPE levels do not seem to reflect market inefficiency and do reflect risk (volatility).

Learning from History: Volatility and Financial Crises
Jon Danielsson (London School of Economics), et al.
October 2016
We study the effects of volatility on financial crises by constructing a cross-country database spanning over 200 years. Volatility is not a significant predictor of crises whereas unusually high and low volatilities are. Low volatility is followed by credit build-ups, indicating that agents take more risk in periods of low financial risk consistent with Minsky hypothesis, and increasing the likelihood of a banking crisis. The impact is stronger when financial markets are more prominent and less regulated. Finally, both high and low volatilities make stock market crises more likely, while volatility in any form has no impact on currency crises.

Evolving Differences Among Publicly-Traded Firms in the United States, 1960-2015
Jose Maria Barrero (Stanford University)
August 17, 2016
I use data on all publicly traded firms in the United States to document the evolving differences between large and small firms over the period covering 1960 to 2015. Focusing separately on the financial and non-financial sectors, I document patterns related to the number of active publicly-traded firms; size differences between the firms at the 10th and 90th percentiles; the share of sales and assets held by firms at the top of the distribution; the behavior of entrants; volatility differences among small and large firms; the significance of the technology sector; and the sectoral affiliation of publicly-traded firms. I find evidence that, over the past 55 years, disparity (in size and volatility) between the largest and smallest firms has increased, although the picture is more complicated in the financial sector where large firms, for example, have become relatively volatile in comparison with small firms. New non-financial public companies have also become smaller relative to incumbents, while all entrants have had a smaller chance of surviving during more recent decades. My analysis also uncovers a shift away from manufacturing and towards services, retail and wholesale trade, and finance, along with the increasing prominence of high-technology industries.

Ambiguity, Macro Factors, and Stock Return Volatility
Le (Lexi) Kang and Hwagyun Kim (Texas A&M University)
December 5, 2016
Recent studies find stock returns are negatively related to idiosyncratic volatility (IVOL). We find that aggregate variables known to explain stock market volatility affect the IVOL and portfolio returns sorted by IVOL. Macroeconomic volatilities, yield spreads, dividend yield, trading volume and common factors of earnings forecast dispersions are important drivers of IVOL. Macro factors produce the negative pattern, consistent with theories of intertemporal hedging demand. Teasing out the common IVOL part, the residual IVOL is positively and significantly related to stock returns and the idiosyncratic portions of earnings forecast dispersions. This is consistent with ambiguity aversion and incomplete market hypotheses.

How Important are Inflation Expectations for the Nominal Yield Curve?
Roberto Gomez Cram (University of Pennsylvania)
December 1, 2016
Less than you think. Relative to the data, standard macro-finance term structure models rely too heavily on the volatility of expected inflation news as a source for variations in nominal yield shocks. In this paper, I develop a nonlinear Bayesian state-space model that accounts for several bond market features, without resorting to an expected inflation channel that counterfactually dominates the variation in nominal yield shocks. The estimation of the model suggests that, for the last two decades, inflation-related risk factors have not played an important role in driving either expected excess bond returns or the term premium component of the nominal yield curve.

Man vs. Machine: Comparing Discretionary and Systematic Hedge Fund Performance
Campbell R. Harvey (Duke University), et al.
December 5, 2016
We analyse and contrast the performance of discretionary and systematic hedge funds. Systematic funds use strategies that are rules-based, with little or no daily intervention by humans. In our experience, some large allocators shy away from systematic hedge funds altogether. A possible explanation for this is what the psychology literature calls “algorithm aversion”. We find that, for the period 1996-2014, systematic equity managers underperform their discretionary counterparts in terms of unadjusted (raw) returns, but that after adjusting for exposures to well-known risk factors, the risk-adjusted performance is similar. In the case of macro, systematic funds outperform discretionary funds, both on an unadjusted and risk-adjusted basis. It is sometimes claimed that systematic funds’ returns have a greater exposure to well-known risk factors. We find, however, that for discretionary funds (in the aggregate) more of the average return and the volatility of returns can be explained by risk factors.


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