Dynamic factor long memory volatility
| Autor: | Anh T. H. Nguyen, Richard Harris |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
050208 finance
Hardware_MEMORYSTRUCTURES Stochastic volatility Investment strategy 05 social sciences finance long memory Implied volatility volatility timing 01 natural sciences Volatility risk premium 010104 statistics & probability factor models Volatility swap 0502 economics and business Forward volatility Volatility smile Econometrics Economics 0101 mathematics Volatility (finance) General Economics Econometrics and Finance Finance conditional variance-covariance matrix |
| Zdroj: | Harris, R D F & Nguyen, A T H 2017, ' Dynamic factor long memory volatility ', Quantitative Finance, vol. 17, no. 8, pp. 1205-1221 . https://doi.org/10.1080/14697688.2016.1260757 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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