Volatility Timing Strategies Research Articles

The disappearing profitability of volatility-managed equity factors

Our evidence suggests that the profitability of volatility timing strategies applied to equity factor portfolios disappeared when changes in the trading and information environment in the U.S. in the early 2000s made arbitrage less costly. The reduction of volatility timing alphas is greater for factor portfolios based on small-capitalization stocks, which are less liquid, more costly to trade, and more expensive to short than portfolios based on large-capitalization stocks. The evidence holds for 11 factor portfolios and a broader sample of 110 anomaly portfolios in the U.S. Our research highlights the importance of frictions in the profitability of investment strategies.

The Determinants of Volatility Timing Performance

Abstract The exact conditions under which volatility timing strategies yield value are documented. These conditions include: the ability to correctly forecast next period stochastic variance, and violation of a strict version of Merton’s intertemporal capital asset pricing model (ICAPM). While the empirical evidence supports the first of these conditions, the latter remains open to debate. Our empirical results confirm the former, but demonstrate a significant violation of the (strict) ICAPM. It follows that volatility timing strategies appear to have value. However, using reasonable parameter values plugged into the derived formulae, the results also show that extreme leverage is often required for success. A method of tempering leverage is proposed, which is somewhat able to loosen the requirement of high leverage while still maintaining a good performance level. Given the likely variation in (strict) ICAPM violations across time and assets, it follows that volatility timing success (or failure) is very much sample dependent.

Asymmetric multivariate HAR models for realized covariance matrix: A study based on volatility timing strategies

Forecasting high-dimensional covariance matrices is crucial for portfolio optimization and risk management. Recent studies focus on modelling the realized covariance matrices constructed with intraday prices using multivariate heterogeneous autoregressive (MHAR) models. Considering the mounting evidence of the asymmetric volatility phenomenon, this paper extends the literature by investigating whether volatility timing investors could achieve economic gains through incorporating asymmetry in the MHAR models. Empirical results in China's stock markets show that our proposed method of incorporating the asymmetric logistic smooth transition structure in the MHAR model achieves the highest economic values out-of-sample under various market conditions. Furthermore, we uncover that the most diversified portfolio strategy is more applicable when the market is in tranquil stage, while the global minimum variance strategy is more applicable during market turbulence.

Discovering the Drivers of Market Volatility: Asset Allocation Applications

This study investigates the economic and financial drivers of volatility changes and integrates them into stock market volatility forecasting. We first collect a diverse set of predictor variables and analyze them within a unified framework. We discover that only a small number of variables contain significant predictive information, and that the Chinese stock market return significantly predicts U.S. stock market volatility. Using the HAR-LASSO procedure, we integrate the drivers’ predictive information and forecast short-term, medium-term, and long-term market volatilities. Through various volatility timing strategies, we verify that HAR-LASSO-based portfolios lead to outstanding investment performance, regardless of the strategies and forecasting horizons. These results not only economically justify our procedure, but also provide meaningful financial implications of accurate volatility forecasting.

Who Wants to Be a Volatility Timer?

The exact conditions under which volatility timing strategies yield value are documented. These conditions include: the ability to correctly forecast next period stochastic variance, and violation of Merton's ICAPM. Using reasonable parameter values plugged into the derived formulae, the results show that extreme leverage is often required for success. A method of tempering leverage is proposed, which is somewhat able to loosen the requirement of high leverage while still maintaining a good performance level.

Dynamic factor long memory volatility

In this paper, we develop a long memory orthogonal factor (LMOF) multivariate volatility model for forecasting the covariance matrix of financial asset returns. We evaluate the LMOF model using the volatility timing framework of Fleming et al. [J. Finance, 2001, 56, 329–352] and compare its performance with that of both a static investment strategy based on the unconditional covariance matrix and a range of dynamic investment strategies based on existing short memory and long memory multivariate conditional volatility models. We show that investors should be willing to pay to switch from the static strategy to a dynamic volatility timing strategy and that, among the dynamic strategies, the LMOF model consistently produces forecasts of the covariance matrix that are economically more useful than those produced by the other multivariate conditional volatility models, both short memory and long memory. Moreover, we show that combining long memory volatility with the factor structure yields better results than employing either long memory volatility or the factor structure alone. The factor structure also significantly reduces transaction costs, thus increasing the feasibility of dynamic volatility timing strategies in practice. Our results are robust to estimation error in expected returns, the choice of risk aversion coefficient, the estimation window length and sub-period analysis.

Risk-Adjusted Cross-Sectional Momentum

This study proposes a new ranking criterion for constructing momentum portfolios, namely, risk-adjusted cross-sectional momentum. We propose the combination of traditional cross-sectional momentum strategies with different volatility timing strategies in the form of the Sharpe ratio. Then, we show that the traditional momentum trading is inferior to the risk-adjusted cross-sectional momentum strategy and employing the conditioning information can be beneficial with 3% annual returns. This finding is particularly pronounced in the presence of momentum crashes during the global financial crisis. In addition, we highlight the role of penny stocks and find that they significantly affect momentum crashes. Our findings have important implications for market practitioners.

Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects

This paper presents two classes of tick-by-tick covariance estimators adapted to the case of rounding in the price time stamps to a frequency lower than the typical arrival rate of tick prices. Through Monte Carlo simulations, we investigate the behavior of such estimators under realistic market microstructure conditions analogous to those of the financial data examined in this paper's empirical section, that is, nonsynchronous trading, general ARMA structure for microstructure noise, and true lead–lag cross-covariance. Simulation results show the robustness of the proposed tick-by-tick covariance estimators to time stamp rounding, and their overall performance is superior to competing covariance estimators under empirically realistic microstructure conditions. These results are confirmed in the empirical application where the economic benefits of the proposed estimators are evaluated with volatility timing strategies applied to a bivariate portfolio of S&P 500 futures and 30-year U.S. treasury bond futures.

The behavior of green exchange‐traded funds

PurposeThe purpose of this paper is to investigate green exchange‐traded funds (ETFs) and propose a market‐wide proxy for green returns and a green volatility factor.Design/methodology/approachIdentifying a unique sample of green funds, this paper investigates the time‐series behavior of returns for these investment vehicles and their associated conditional volatility dynamics via GARCH methodology. In this study, green ETFs are defined as index funds replicating market indices that invest in stocks exhibiting positive environmental, social, and governance characteristics.FindingsCumulative market‐wide green returns are found to be positive from inception year 2005 through 2008. Estimating a t‐GARCH (1,1) specification, the author finds strong evidence in favor of volatility persistence for the 15 green ETFs identified in this study. Additionally, the results suggest that a 1 percent volatility‐based value‐at‐risk forecast ranges from $24,150 through $26,000 on a daily basis. Finally, the empirical evidence provides support for weak‐form market efficiency when examining the green universe of stocks.Research limitations/implicationsGreen ETFs are relatively recent financial instruments and exhibit important implications for volatility timing strategies and the cross‐section of green stock returns.Practical implicationsKnowledge of green price behavior is important in constructing optimal hedging and risk management strategies.Social implicationsGreen ETFs provide a natural channel for sustainable investing.Originality/valueGreen ETFs exhibit the advantage of including many companies that have undertaken positive measures towards the global environment in their respective business models. This paper provides the first investigation of green ETFs.

The economic value of volatility timing using a range-based volatility model

There is growing interest in utilizing the range data of asset prices to study the role of volatility in financial markets. In this paper, a new range-based volatility model was used to examine the economic value of volatility timing in a mean–variance framework. We compared its performance with a return-based dynamic volatility model in both in-sample and out-of-sample volatility timing strategies. For a risk-averse investor, it was shown that the predictable ability captured by the dynamic volatility models is economically significant, and that a range-based volatility model performs better than a return-based one.

Mutual fund volatility timing and management fees

This paper shows that compensation incentives partly drive fund managers’ market volatility timing strategies. Larger incentive management fees lead to less counter-cyclical or more pro-cyclical volatility timing. But fund styles or aggregate fund flows could also account for this relation; therefore, we control for them and find that the relation between fees and volatility timing still holds. Results show that less aggressive fund styles are associated with pro-cyclical volatility timing, and that volatility timing and flow timing are negatively related. We also find that pro-cyclical timing mostly improves funds’ average excess returns, Sharpe ratios, and alphas.

The Economic Value of Volatility Timing using a Range-Based Volatility Model

There is growing interest in utilizing the range data of asset prices to study the role of volatility in financial markets. In this paper, a new range-based volatility model is used to examine the economic value of volatility timing in a mean-variance framework. We compare its performance with a return-based dynamic volatility model in both in-sample and out-of-sample volatility timing strategies. For a risk-averse investor, it is shown that the predictable ability captured by the dynamic volatility models is economically significant, and that the range-based volatility model performs better than the return-based one.

The Economic Value of Using Realized Volatility in the Index Options Market

We examine the economic benefits of using high frequency volatility measures for pricing, trading and hedging in the S&P 500 index options market. Using the encompassing regression framework, we generate volatility forecasts combining information from long memory high-frequency volatility specifications and option-based implied volatilities. We conduct out-of-sample tests of the volatility forecasts by examining option pricing performance, trading performance based on volatility timing strategies, and the performance of covered options positions for index option writers. Our results support combining forecasts of implied volatility and realized volatility and illustrate that the realized volatility approach has economic value in the context of option pricing and risk management.

Incentive Fees and Mutual Fund Volatility Timing

This paper shows that compensation incentives partly drive fund managers' market volatility timing strategies. Larger management fees are associated with less counter-cyclical or more pro-cyclical volatility timing. Fund investment objectives and styles also partly determine volatility timing. Funds with more aggressive styles time volatility more counter-cyclically. Thus, managers may try to outperform the general market by adopting aggressive styles, while dynamically hedging portfolio volatility using counter-cyclical volatility timing. We also find that fund managers systematically change their portfolio betas in response to aggregate equity fund cash flows. The average effects of volatility timing and fund flow timing on fund performance are mostly positive for funds that increase their betas when conditional volatility and fund flows increase (i.e., pro-cyclical timers).

Global European portfolio construction: Does a changing volatility structure matter?

AbstractWe propose a multivariate time series model to forecast the returns and volatilities of 15 European financial markets. Using the approach of mean‐variance portfolios we develop several strategies which are based on the predictions of high‐dimensional VAR‐GARCH models for future volatilities. We explore the value of volatility timing strategies by simplifying the forecasting model. One approach for information blocking is based on factor analysis for the returns. Finally we discuss if multivariate volatility timing strategies are successful for beating the benchmark index (the MSCI Europe index). Copyright © 2004 John Wiley & Sons, Ltd.

Modeling the Conditional Covariance Between Stock and Bond Returns: A Multivariate Garch Approach

To analyze the intertemporal interaction between the stock and bond market returns, we allow the conditional covariance matrix to vary over time according to a multivariate GARCH model similar to Bollerslev, Engle and Wooldridge (1988). We extend the model such that it allows for asymmetric effects on conditional variances and covariances. Using weekly U.S. stock and bond market data, we find strong evidence of conditional heteroskedasticity in the covariance between stock and bond market returns. The results indicate that not only variances, but also covariances respond asymmetrically to return shocks. Regardless of the bond market shocks, bad news in the stock market is typically followed by a higher conditional covariance than good news. We find that volatility timing strategies for dynamic asset allocation significantly outperform passive strategies. Even when short-sale restrictions are present and transaction costs are high, the economic value of dynamic trading strategies is larger than that of a passive strategy. Moreover, the symmetric volatility timing strategy is outperformed by its asymmetric counterpart.

The Economic Value of Volatility Timing

ABSTRACTNumerous studies report that standard volatility models have low explanatory power, leading some researchers to question whether these models have economic value. We examine this question by using conditional meanm‐variance analysis to assess the value of volatility timing to short‐horizon investors. We find that the volatility timing strategies outperform the unconditionally efficient static portfolios that have the same target expected return and volatility. This finding is robust to estimation risk and transaction costs.

The Economic Value of Volatility Timing

Numerous studies report that standard volatility models have low explanatory power, leading some researchers to question whether these models have economic value. We examine this question by using conditional mean-variance analysis to assess the value of volatility timing to short-horizon investors. We find that the volatility timing strategies outperform the unconditionally efficient static portfolios that have the same target expected return and volatility. This finding is robust to estimation risk and transaction costs.