Volatility timing in mutual funds: evidence from daily returns

I use daily mutual fund returns to shed new light on the questionof whether or not mutual fund managers are successful markettimers. Previous studies find that funds are unable to timethe market return. I study the funds' ability to time marketvolatility. I show that volatility timing is an important factorin the returns of mutual funds and has led to higher risk-adjustedreturns. The returns of surviving funds are especially sensitiveto market volatility; those of nonsurvivors are not.     

Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management

In this article we take a recent generalized VAR-GARCH approach to examine the extent of volatility transmission between oil and stock markets in Europe and the United States at the sector-level. The empirical model is advantageous in that it typically allows simultaneous shock transmission in the conditional returns and volatilities. Insofar as volatility transmission across oil and stock sector markets is a crucial element for portfolio designs and risk management, we also analyze the optimal weights and hedge ratios for oil-stock portfolio holdings with respect to the results. Our findings point to the existence of significant volatility spillover between oil and sector stock returns. However, the spillover is usually unidirectional from oil markets to stock markets in Europe, but bidirectional in the United States. Our back-testing procedures, finally, suggest that taking the cross-market volatility spillovers estimated from the VAR-GARCH models often leads to diversification benefits and hedging effectiveness better than those of commonly used multivariate volatility models such as the CCC-GARCH of Bollerslev (1990), the diagonal BEKK-GARCH of Engle and Kroner (1995) and the DCC-GARCH of Engle (2002).     

How to make experience your company's best teacher

In our personal life, experience is often the best teacher. Not so in corporate life. After a major event--a product failure, a downsizing crisis, or a merger--many companies stumble along, oblivious to the lessons of the past. Mistakes get repeated, but smart decisions do not. Most important, the old ways of thinking are never discussed, so they are still in place to spawn new mishaps. Individuals will often tell you that they understand what went wrong (or right). Yet their insights are rarely shared openly. And they are analyzed and internalized by the company even less frequently. Why? Because managers have few tools with which to capture institutional experience, disseminate its lessons, and translate them into effective action. In an effort to solve this problem, a group of social scientists, business managers, and journalists at MIT have developed and tested a tool called the learning history. It is a written narrative of a company's recent critical event, nearly all of it presented in two columns. In one column, relevant episodes are described by the people who took part in them, were affected by them, or observed them. In the other, learning historians--trained outsiders and knowledgeable insiders--identify recurrent themes in the narrative, pose questions, and raise "undiscussable" issues. The learning history forms the basis for group discussions, both for those involved in the event and for others who also might learn from it. The authors believe that this tool--based on the ancient practice of community storytelling--can build trust, raise important issues, transfer knowledge from one part of a company to another, and help build a body of generalizable knowledge about management.     

How Crashes Develop: Intradaily Volatility and Crash Evolution

This paper explores whether affine models with volatility jumps estimated on intradaily S&P 500 futures data over 1983 to 2008 can capture major daily outliers such as the 1987 stock market crash. Intradaily jumps in futures prices are typically small; self‐exciting but short‐lived volatility spikes capture intradaily and daily returns better. Multifactor models of the evolution of diffusive variance and jump intensities improve fits substantially, including out‐of‐sample over 2009 to 2016. The models capture reasonably well the conditional distributions of daily returns and realized variance outliers, but underpredict realized variance inliers. I also examine option pricing implications.     

How Markets Process Information: News Releases and Volatility

We examine the impact of scheduled macroeconomic news announcements on interest rate and foreign exchange futures markets. We find these announcements are responsible for most of the observed time-of-day and day-of-the-week volatility patterns in these markets. While the bulk of the price adjustment to a major announcement occurs within the first minute, volatility remains substantially higher than normal for roughly fifteen minutes and slightly elevated for several hours. Nonetheless, these subsequent price adjustments are basically independent of the first minute's return. We identify those announcements with the greatest impact on these markets.     

Testing for market timing ability: A framework for forecast evaluation

In this paper we examine the Henriksson-Merton test of market timing and its potential usefulness in evaluating investment advice. The paper proposes a natural extension of the test that is valid under more general assumptions about the distribution of asset returns. We show that the Henriksson-Merton test and its more general counterpart are special cases of standard tests of market rationality and efficiency. Both tests are applied to a group of foreign exchange advisory services.     

How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes

We propose a discrete-time stochastic volatility model in whichregime switching serves three purposes. First, changes in regimescapture low-frequency variations. Second, they specify intermediate-frequencydynamics usually assigned to smooth autoregressive transitions.Finally, high-frequency switches generate substantial outliers.Thus a single mechanism captures three features that are typicallyviewed as distinct in the literature. Maximum-likelihood estimationis developed and performs well in finite samples. Using exchangerates, we estimate a version of the process with four parametersand more than a thousand states. The multifractal outperformsGARCH, MS-GARCH, and FIGARCH in- and out-of-sample. Considerablegains in forecasting accuracy are obtained at horizons of 10to 50 days.     

Market timing for the individual investor: Using the predictability of long-horizon stock returns to enhance portfolio performance

Recent research indicates that dividend yield and earnings-price ratio can partially predict long-horizon stock returns. We examine whether individual investors can successfully construct timing portfolios based on either of these variables or a measure of the expected market risk premium. The out-of-sample tests in this study require that investors rely only on information that was available at the time of the market-timing decision. Timing portfolios based on the market risk premium show the strongest ability to time the market. We present an economic rationale for the results that is consistent with efficient markets.     

How prior realized outcomes affect portfolio decisions

Investors have been shown to have particular preferences when it comes to the characteristics of stock they hold in their portfolios, while prior gains and losses have been shown to impact on individuals’ economic decisions, both in an investment context and more widely. This paper is the first to investigate how prior gains and losses affect investors’ preferences for particular stock characteristics and so shape their portfolio compositions. Using a rich dataset combination from China, we conclude that prior realized outcomes play an important role in shaping portfolio composition through their impact on the characteristics of stocks that investors choose to hold. We find that positive prior realized outcomes encourage investors to select stocks with a variety of characteristics broadly consistent with higher risk taking (for example, higher betas and higher levels of idiosyncratic risk), though there are some differences across investor classes. While our empirical results are in line with what one would expect from the existing literature on the disposition and house money effects, we also consider other possible interpretations of the results.     

How the best of the best get better and better

What is the real key to elite performance? According to sports psychologist turned executive coach Graham Jones, star athletes and businesspeople share one defining trait: mental toughness. People who become champions aren't necessarily more gifted than others; they're just masters at managing pressure, meticulously tackling goals, and driving themselves to stay ahead of the competition. Jones, who has advised Olympic medalists and Fortune 500 executives, sees many parallels between the arenas of business and sports, especially in the behavior of people who rise to the very top. These stars have learned to love pressure because it spurs them to achieve. Inner-focused and self-directed, they concentrate on their own excellence and forget the rest. They don't get distracted by others' victories or failures--or even by a personal tragedy off the field of competition. Like Darren Clarke, the golfer who inspired his team to a Ryder Cup victory shortly after the death of his beloved wife, elite performers are masters of compartmentalization. Superstars rebound from defeats more easily, Jones observes, because they don't engage in self-flagellation. One of the keys to their success is a relentless focus on the long-term and the careful planning of short-term goals that will help them attain major milestones. Competition doesn't daunt elite performers; they just use it to challenge themselves--and they never stop striving. Even after becoming benchmarks in their fields, stars keep their edge by reinventing themselves. Star business people and athletes also recognize the importance of celebrating their wins. It's not just the emotional reward that's important, however: The very best performers also analyze the factors underpinning their success. That helps them build their expertise and their confidence.     

How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models

Abstract:  We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed.     

Conditional risk and performance evaluation: Volatility timing, overconditioning, and new estimates of momentum alphas

Unconditional alphas are biased when conditional beta covaries with the market risk premium (market timing) or volatility (volatility timing). We demonstrate an additional bias (overconditioning) that can occur any time an empiricist estimates risk using information, such as a realized beta, that is not available to investors ex ante. Calibrating to U.S. equity returns, volatility timing and overconditioning can plausibly impact alphas more than market timing, which has been the focus of prior literature. To correct market- and volatility-timing biases without overconditioning, we show that incorporating realized betas into instrumental variables estimators is effective. Empirically, instrumentation reduces momentum alphas by 20-40%. Overconditioned alphas overstate performance by up to 2.5 times. We explain the sources of both the volatility-timing and overconditioning biases in momentum portfolios.     

Analyst characteristics, timing of forecast revisions, and analyst forecasting ability

We first examine whether analysts with certain characteristics that prior research has identified are related to superior forecasting ability systematically time their forecast revisions later in the fiscal quarter. We then examine whether this superior ability persists after controlling for the timing advantage by using relative forecast error, a measure that largely eliminates the timing advantage of recent forecasts. Using a sample of quarterly earnings forecast revisions over the 20-year period from 1990 to 2009, we find that analysts with more firm-specific and general experience and more accurate prior-period forecasts, analysts employed by larger brokerage firms, and analysts who follow fewer industries and companies tend to revise forecasts later in the quarter. We also find that analyst characteristics that are positively correlated with revision timing are negatively related to relative forecast errors. These results are consistent with analyst characteristics being useful proxies for analyst forecasting ability and analysts with greater ability revising forecasts later in the quarter.     

Volatility timing,sentiment, and the short-term profitability of VIX-based cross-sectional trading strategies

This paper explores the profitability of simple short-term cross-sectional trading strategies based on the implied volatility index (VIX), often referred to as an “investor fear gauge” in the stock market. These strategies involve holding sentiment-prone stocks when VIX is low and sentiment-immune stocks when VIX is high and generate significantly higher excess returns than the benchmark long–short portfolios that do not condition on VIX. We show that the profitability of our trading strategies is not subsumed by the well-known risk factors or transaction cost adjustments. Our findings are consistent with the theory of delayed arbitrage and the synchronization problem of Abreu and Brunnermeier (2002).     

Portfolio returns and manager activity: How to decompose tracking error into security selection and market timing

We develop a new method for detecting portfolio manager activity. Our method relies exclusively on portfolio returns and, consequently, avoids the pitfalls associated with disclosed portfolio holdings. We investigate the link between activity and performance of actively managed U.S. equity funds from 2000 to 2007 and document robust evidence that future performance is positively related to past stock picking and negatively associated with past market timing. Finally, we find that portfolio manager activity is highly persistent over time, which supports the conclusion that stock picking increases performance while market timing decreases performance.     

My global fund portfolio is not yours: the effect of home bias on European- and US-managed convertible bond fund exposures

This paper shows that global convertible bond funds (CBFs) and their resulting equity-bond exposures are regionally biased. Global bond fund managers display home bias, resulting in CBFs that are not only tilted towards the home market but also reflect the different bond-equity exposures of European and US convertibles. More specifically we find that global funds managed by a European asset management firm are more bond-like than global funds managed by a US-based asset manager. Hence, investors have to account for the asset management company's origin to avoid that the performance of the fund and its correlation with other assets is not in line with investor's ex ante expectations about globally managed portfolios. Our results also indicate that for investors of European-based CBFs this home bias has resulted in an ex post opportunity cost up to 1.38% per year, depending on the sample period.     

Volatility has to be rough

Under power-law blow-up of the short ATM skew, volatility must be rough in a viable market for the underlying asset