Dynamic Factor Model-Based Multiperiod Mean-Variance Portfolio Selection with Portfolio Constraints
Motivated by practical applications, we explore the constrained multi-period mean-variance portfolio selection problem within a market characterized by a dynamic factor model. This model captures predictability in asset returns driven by state variables and incorporates cone-type portfolio constraints that are crucial in practice. The model is broad enough to encompass various dynamic factor frameworks, including practical considerations such as no-short-selling and cardinality constraints. We derive a semi-analytical optimal solution using dynamic programming, revealing it as a piecewise linear feedback policy to wealth, with all factors embedded within the allocation vectors. Additionally, we demonstrate that the portfolio policies are determined by two specific stochastic processes resulting from the stochastic optimizations, for which we provide detailed algorithms. These processes reflect the investor's assessment of future investment opportunities and play a crucial role in characterizing the time consistency and efficiency of the optimal policy through the variance-optimal signed supermartingale measure of the market. We present numerical examples that illustrate the model's application in various settings. Using real market data, we investigate how the factors influence portfolio policies and demonstrate that incorporating the factor structure may enhance out-of-sample performance.
Code (1)
Similar Papers 제목 키워드 기반
Data-driven Multiperiod Robust Mean-Variance Optimization
We study robust mean-variance optimization in multiperiod portfolio selection by allowing the true probability measure to be inside a Wasserstein ball centered at the empirical probability measure. Given the confidence l…
The Hansen ratio in mean--variance portfolio theory
It is shown that the ratio between the mean and the $L^2$-norm leads to a particularly parsimonious description of the mean-variance efficient frontier and the dual pricing kernel restrictions known as the Hansen-Jaganna…
Dynamic Portfolio Allocation in High Dimensions using Sparse Risk Factors
We propose a fast and flexible method to scale multivariate return volatility predictions up to high-dimensions using a dynamic risk factor model. Our approach increases parsimony via time-varying sparsity on factor load…
Vocal Bursts Intensity PredictionIdentifying and exploiting alpha in linear asset pricing models with strong, semi-strong, and latent factors
The risk premia of traded factors are the sum of factor means and a parameter vector we denote by {\phi} which is identified from the cross section regression of alpha of individual securities on the vector of factor loa…
Fundamental properties of linear factor models
We study conditional linear factor models in the context of asset pricing panels. Our analysis focuses on conditional means and covariances to characterize the cross-sectional and inter-temporal properties of returns and…