We propose a portfolio allocation method based on risk factor budgeting using convex Nonnegative Matrix Factorization (NMF). Unlike classical factor analysis, PCA, or ICA, NMF ensures positive factor loadings to obtain interpretable long-only portfolios. As the NMF factors represent separate...

We propose direct multiple time series models for predicting high dimensional vectors of observable realized global minimum variance portfolio (GMVP) weights computed based on high-frequency intraday returns. We apply Lasso regression techniques, develop a class of multiple AR(FI)MA models for...

Determining multiple assets’ portfolio volatility using the VaR model has proven to have so many pitfalls; once the portfolio assets are more than two, the value at risk tends to become erratic while repeated computations generate different values therefore making the VaR model...

When principal component analysis (PCA) is used on a rolling or conditional setting, ordering and incoherence issues may emerge. We provide empirical evidence supporting this claim and introduce an algorithm that allows dynamic re-ordering of the principal components (PCs). We provide additional...

This paper incorporates model uncertainty to study an inter-temporal investment-consumption choice problem. Using a modified Cox-Ingersoll-Ross model in a complete market context, we propose an approach for quantifying uncertainty, which requires only an uncertainty parameter rather than an...

This study proposes a wavelets approach to estimating time-frequency-varying betas in the capital asset pricing model (CAPM) framework. The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk-profile robustness. Furthermore, we emphasize the effect of an...

This paper minimizes the risk of Brent oil in a multivariate portfolio, with three risk-minimizing goals: variance, parametric value-at-risk (VaR), and semiparametric value-at-risk. Brent oil is combined with five emerging ASEAN (Association of Southeast Asian Nations) stock indexes and five...

The paper develops a tail risk forecasting model that incorporates the wealth of economic and financial information available to risk managers. The approach can be viewed as a regularized extension of the two-stage GARCH-EVT model of McNeil and Frey (2000) where we permit a time-varying...