Index tracking model, downside risk and non-parametric kernel estimation
Yong Li and
Journal of Economic Dynamics and Control, 2018, vol. 92, issue C, 103-128
In this paper, we propose an index tracking model with the conditional value-at-risk (CVaR) constraint based on a non-parametric kernel (NPK) estimation framework. In theory, we demonstrate that the index tracking model with the CVaR constraint is a convex optimization problem. We then derive NPK estimators for tracking errors and CVaR, and thereby construct the NPK index tracking model. Monte Carlo simulations show that the NPK method outperforms the linear programming (LP) method in terms of estimation accuracy. In addition, the NPK method can enhance computational efficiency when the sample size is large. Empirical tests show that the NPK method can effectively control downside risk and obtain higher excess returns, in both bearish and bullish market environments.
Keywords: Non-parametric kernel estimation; Index tracking model; Conditional value-at-risk (search for similar items in EconPapers)
JEL-codes: G11 G10 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:92:y:2018:i:c:p:103-128
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