In this paper, assuming that returns follows a stationary and ergodic stochastic process, the asymptotic distribution of the natural estimator of the Sharpe Ratio is explicitly given. This distribution is used in order to define an approximated confidence interval for the Sharpe ratio. Particular attention is devoted to the case of the GARCH(1,1) process. In this latter case, a simulation study is performed in order to evaluate the minimum sample size for reaching a good coverage accuracy of the asymptotic confidence intervals.

DE CAPITANI, L. (2010). Interval estimation for the Sharpe Ratio when returns are not i.i.d. with special emphasis on the GARC(1,1) process with symmetric innovations [Working paper del dipartimento].

Interval estimation for the Sharpe Ratio when returns are not i.i.d. with special emphasis on the GARC(1,1) process with symmetric innovations

DE CAPITANI, LUCIO
2010

Abstract

In this paper, assuming that returns follows a stationary and ergodic stochastic process, the asymptotic distribution of the natural estimator of the Sharpe Ratio is explicitly given. This distribution is used in order to define an approximated confidence interval for the Sharpe ratio. Particular attention is devoted to the case of the GARCH(1,1) process. In this latter case, a simulation study is performed in order to evaluate the minimum sample size for reaching a good coverage accuracy of the asymptotic confidence intervals.
Working paper del dipartimento
Sharpe ratio, stationary and ergodic process, generalized method of moments, GARCH(1,1) process
English
dic-2010
DE CAPITANI, L. (2010). Interval estimation for the Sharpe Ratio when returns are not i.i.d. with special emphasis on the GARC(1,1) process with symmetric innovations [Working paper del dipartimento].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/18858
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