Constrained Reweighting of Distributions: An Optimal Transport Approach.

We commonly encounter the problem of identifying an optimally weight-adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the moments, tail behavior, shapes, number of modes, etc., of the resulting weight-adjusted empirical distribution. In this article, we substantially enhance the flexibility of such a methodology by introducing a nonparametrically imbued distributional constraint on the weights and developing a general framework leveraging the maximum entropy principle and tools from optimal transport. The key idea is to ensure that the maximum entropy weight-adjusted empirical distribution of the observed data is close to a pre-specified probability distribution in terms of the optimal transport metric, while allowing for subtle departures. The proposed scheme for the re-weighting of observations subject to constraints is reminiscent of the empirical likelihood and related ideas, but offers greater flexibility in applications where parametric distribution-guided constraints arise naturally. The versatility of the proposed framework is demonstrated in the context of three disparate applications where data re-weighting is warranted to satisfy side constraints on the optimization problem at the heart of the statistical task-namely, portfolio allocation, semi-parametric inference for complex surveys, and ensuring algorithmic fairness in machine learning algorithms.

Sectoral connectedness: New evidence from US stock market during COVID-19 pandemics.

We examine volatility connectedness of 11 sectoral indices in the US using daily data from January 01, 2013 to December 31, 2020. We employ the connectedness measures of Diebold and Yilmaz (2009, 2012, 2014), unveiling changes in sectoral connectedness and stylized facts regarding specific sectors during the COVID-19 pandemic. Among several results, we find extraordinary increase in total connectedness, from early stages of international spread to the end of July 2020; some relevant changes in the pairwise connections between sectors, especially among the originally stronger ones. However, in a total net connectedness perspective, there is little evidence of structural changes.

Between Nonlinearities, Complexity, and Noises: An Application on Portfolio Selection Using Kernel Principal Component Analysis.

This paper discusses the effects of introducing nonlinear interactions and noise-filtering to the covariance matrix used in Markowitz's portfolio allocation model, evaluating the technique's performances for daily data from seven financial markets between January 2000 and August 2018. We estimated the covariance matrix by applying Kernel functions, and applied filtering following the theoretical distribution of the eigenvalues based on the Random Matrix Theory. The results were compared with the traditional linear Pearson estimator and robust estimation methods for covariance matrices. The results showed that noise-filtering yielded portfolios with significantly larger risk-adjusted profitability than its non-filtered counterpart for almost half of the tested cases. Moreover, we analyzed the improvements and setbacks of the nonlinear approaches over linear ones, discussing in which circumstances the additional complexity of nonlinear features seemed to predominantly add more noise or predictive performance.

How the Closure of a U.S. Tax Loophole May Affect Investor Portfolios.

In the United States, exchange-traded funds can defer capital gains taxes of their investors by taking advantage of a legal loophole. To quantify the impact of this tax loophole on investor portfolios, we study a rank-dependent expected utility model. We develop an approximation formula for the sensitivity of the optimal investment strategy with respect to changes in the expected asset returns. By applying this approximation formula, we are able to quantitatively estimate how much investor portfolios may change depending on the investment horizon if the tax loophole is closed.

A novel methodology for perception-based portfolio management.

Selecting and investing in stock market with right proportions is one of the major challenges. Majority of the investors end up losing their invested equity capital due to uncertainty in the market. The present study provides a novel framework for novice investors to construct portfolio based on multicriteria decision making techniques under fuzzy environment. The scores obtained from these techniques were used to introduce two non-dimensional parameters for categorization of risky and non-risky assets. Three perceptions portfolios were constructed based on the proposed non-dimensional parameters along with fractional lion clustering algorithm. In order to demonstrate the proposed framework, an illustrative application is included in equity portfolio selection. The returns and risks of these perception based portfolios are compared to major Index funds for validating the efficiency and are found to overpower the Index funds with significant margins by maintaining the risk comparable to Index funds. Further, Markowitz based efficient frontier is plotted for better understanding of optimal returns and risk for perception based investment.

Algorithmic portfolio tilting to harvest higher moment gains.

Many financial portfolios are not mean-variance-skewness-kurtosis efficient. We recommend tilting these portfolios in a direction that increases their estimated mean and third central moment and decreases their variance and fourth central moment. The advantages of tilting come at the cost of deviation from the initial optimality criterion. In this paper, we show the usefulness of portfolio tilting applied to the equally-weighted, equal-risk-contribution and maximum diversification portfolios in a UCITS-compliant asset allocation setting.

Skew selection for factor stochastic volatility models.

This paper proposes factor stochastic volatility models with skew error distributions. The generalized hyperbolic skew t-distribution is employed for common-factor processes and idiosyncratic shocks. Using a Bayesian sparsity modeling strategy for the skewness parameter provides a parsimonious skew structure for possibly high-dimensional stochastic volatility models. Analyses of daily stock returns are provided. Empirical results show that the skewness is important for common-factor processes but less for idiosyncratic shocks. The sparse skew structure improves prediction and portfolio performance.

The distribution of individual cabinet positions in coalition governments: A sequential approach.

Multiparty government in parliamentary democracies entails bargaining over the payoffs of government participation, in particular the allocation of cabinet positions. While most of the literature deals with the numerical distribution of cabinet seats among government parties, this article explores the distribution of individual portfolios. It argues that coalition negotiations are sequential choice processes that begin with the allocation of those portfolios most important to the bargaining parties. This induces conditionality in the bargaining process as choices of individual cabinet positions are not independent of each other. Linking this sequential logic with party preferences for individual cabinet positions, the authors of the article study the allocation of individual portfolios for 146 coalition governments in Western and Central Eastern Europe. The results suggest that a sequential logic in the bargaining process results in better predictions than assuming mutual independence in the distribution of individual portfolios.