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Found 9 papers, 1 papers with code
Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR
In this paper, a new way to integrate volatility information for estimating value at risk (VaR) and conditional value at risk (CVaR) of a portfolio is suggested.
Two is better than one: Regularized shrinkage of large minimum variance portfolio
In this paper we construct a shrinkage estimator of the global minimum variance (GMV) portfolio by a combination of two techniques: Tikhonov regularization and direct shrinkage of portfolio weights.
Is the empirical out-of-sample variance an informative risk measure for the high-dimensional portfolios?
The main contribution of this paper is the derivation of the asymptotic behaviour of the out-of-sample variance, the out-of-sample relative loss, and of their empirical counterparts in the high-dimensional setting, i. e., when both ratios $p/n$ and $p/m$ tend to some positive constants as $m\to\infty$ and $n\to\infty$, where $p$ is the portfolio dimension, while $n$ and $m$ are the sample sizes from the in-sample and out-of-sample periods, respectively.
Dynamic Shrinkage Estimation of the High-Dimensional Minimum-Variance Portfolio
In this paper, new results in random matrix theory are derived which allow us to construct a shrinkage estimator of the global minimum variance (GMV) portfolio when the shrinkage target is a random object.
Bayesian Quantile-Based Portfolio Selection
By using simulation and real market data, we compare the new Bayesian approach to the conventional method by studying the performance and existence of the global minimum VaR portfolio and by analysing the estimated efficient frontiers.
Statistical inference for the EU portfolio in high dimensions
In this paper, using the shrinkage-based approach for portfolio weights and modern results from random matrix theory we construct an effective procedure for testing the efficiency of the expected utility (EU) portfolio and discuss the asymptotic behavior of the proposed test statistic under the high-dimensional asymptotic regime, namely when the number of assets $p$ increases at the same rate as the sample size $n$ such that their ratio $p/n$ approaches a positive constant $c\in(0, 1)$ as $n\to\infty$.
Sampling Distributions of Optimal Portfolio Weights and Characteristics in Low and Large Dimensions
In this paper, we characterise the exact sampling distribution of the estimated optimal portfolio weights and their characteristics.
Bayesian mean-variance analysis: Optimal portfolio selection under parameter uncertainty
The parameters of the posterior predictive distributions are functions of the observed data values and, consequently, the solution of the optimization problem is expressed in terms of data only and does not depend on unknown quantities.
Statistical Finance Portfolio Management
Optimal shrinkage-based portfolio selection in high dimensions
In this paper we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices.
