The Economic Value of Volatility Timing PDF Download

Author: Jeff Fleming
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Languages : en
Pages : 32

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Book Description
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.

Author: Jeff Fleming
Publisher:
ISBN:
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Languages : en
Pages : 48

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Recent work suggests that intradaily returns can be used to construct estimates of daily return volatility that are more precise than those constructed using daily returns. We measure the economic value of this quot;realizedquot; volatility approach in the context of investment decisions. Our results indicate that the value of switching from daily to intradaily returns to estimate the conditional covariance matix can be substantial. We estimate that a risk-averse investor would be willing to pay 50 to 200 basis points per year to capture the observed gains in portfolio performance. Moreover,these gains are robust to transaction costs, estimation risk regarding expected returns, and the performance measurement horizon.

Author: Ray Y. Chou
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Languages : en
Pages : 31

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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.

Author: Ingmar Nolte
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Languages : en
Pages : 49

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This paper comprehensively investigates the role of realized jumps detected from high frequency data in predicting future volatility from both statistical and economic perspectives. Using seven major jump tests, we show that separating jumps from diffusion improves volatility forecasting both in-sample and out-of-sample. Moreover, we show that these statistical improvements can be translated into economic value. We find a risk-averse investor can significantly improve her portfolio performance by incorporating realized jumps into a volatility timing based portfolio strategy. Our results hold true across the majority of jump tests, and are robust to controlling for microstructure effects and transaction costs.

Author: Eric Jondeau
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Languages : en
Pages : 61

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Book Description
We evaluate how non-normality of asset returns and the temporal evolution of volatility and higher moments affects the conditional allocation of wealth. We show that if one neglects these aspects, as would be the case in a mean-variance allocation, a significant cost would arise. The performance fee the investor is willing to pay to benefit from our allocation is as high as the fee she is willing to pay to benefit from volatility timing. Many tests of robustness are performed, yet, the economic value of taking the non-normality and the temporal evolution of the distribution into account remains.

Author: Madhu Kalimipalli
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ISBN:
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Languages : en
Pages : 49

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Book Description
We examine the economic benefits of using high frequency volatility measures for pricing, trading and hedging in the Samp;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.

Author: Wessel Marquering
Publisher:
ISBN:
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Languages : en
Pages : 50

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Book Description
In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the Samp;P 500 index and its volatility. Using monthly data from 1954 to 2001, we test the statistical significance of return and volatility predictability and examine the economic value of a number of alternative trading strategies. While we find strong evidence for market timing in both returns and volatility, the success of market timing and volatility timing varies considerably over the sample period. Further, it appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility typically exceeds that of strategies that only employ timing in returns.

Author: Luc Bauwens
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Languages : en
Pages : 0

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Author: Massimo Guidolin
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Languages : en
Pages : 58

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We examine the ex-post performance of optimal portfolios with predictable returns, when the investor horizon ranges from one month to ten years. Due to the investor's ability to forecast shifts between bull and bear markets, predictability involves the risk premium, volatility and correlations, and may extend to third and fourth moments. We analyze three different equity portfolios data sets, each covering more than eight indexes, including commonly used US Industry and International Book-to-Market portfolios. Allowing for regimes improves portfolio performance for at least a subset of investment horizons and in all data sets. Despite substantial non-normalities in both the Industry and the book-to-market data sets, gains from predicting higher order moments obtain only in the latter. However, tracking and forecasting bull and bear markets turns out to improve realized portfolio performance more generally. The equally weighted strategy leads to lower ex-post performance measures than optimizing ones.

Author: Chunhua Lan
Publisher:
ISBN:
Category :
Languages : en
Pages : 53

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This paper studies the economic value of exploiting time variation in risk premia and in the volatility of stock returns for a real-time investor in a dynamic setting. I find that ignoring time variation in these return moments leads to economically and statistically significant utility costs. Time-varying risk premia play a more important role than time-varying volatility in forming portfolio weights and in improving portfolio performance for the real-time investor who can rebalance portfolios quarterly. In addition, dynamic policies are more susceptible to parameter uncertainty than myopic policies. This effect further reduces the utility costs of myopic behavior. This study also indicates that conducting an out-of-sample evaluation is necessary to assess the value of a dynamic portfolio policy because in-sample analysis can be misleading.