Shrinkage estimation of covariance matrix

The objective of this dissertation is to investigate that whether the investors can improve the performance of minimum – variance optimized portfolios by altering the estimators of covariance matrix input. Besides, based on the results of out – of – sample portfolio performance metrics, the dissertation is going to select the suitable estimators of covariance matrix for portfolio optimization on Vietnam stock market.

129p mmlemmlem_124 22-12-2020 11 2   Download

The research objective of the thesis is to consider how the change of covariance matrix factor will affect the results of portfolio selection and through that to find out whether investors have Is it possible to improve portfolio performance by adjusting the covariance matrix in the optimized model with the smallest variance.

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Muirhead (1987) reviews the large literature on shrinkage estimators of the covariance matrix in finite-sample statistical decision theory. All these estimators suffer from at least two severe drawbacks, either of which is enough to make them ill-suited to stock returns: (i) they break down when N T; (ii) they do not exploit the a priori knowledge that stock returns tend to be positively correlated to one another.

9p quaivattim 04-12-2012 70 3   Download

This is why, in this paper, we study another way of imposing factor structure. It is to take a weighted average of the sample covariance matrix with Sharpe’s (1963) single-index model estimator. The weight ® (between zero and one) assigned to the single-index model controls how much structure we impose: the heavier the weight, the stronger the structure. This is a well-known technique in Statistics called shrinkage dating back to Stein (1956): ® is called the shrinkage intensity, and the single-index model is our choice of shrinkage target.

0p quaivattim 04-12-2012 61 1   Download