Dynamic portfolio choice with uncertain rare‑events risk in stock and cryptocurrency markets

In response to the unprecedented uncertain rare events of the last decade,we derive an optimal portfolio choice problem in a semi-closed form by integrating price diffusion ambiguity,volatility diffusion ambiguity,and jump ambiguity occurring in the traditional stock market and the cryptocurrency market into a single framework.We reach the following conclusions in both markets:first,price diffusion and jump ambiguity mainly determine detection-error probability;second,optimal choice is more significantly affected by price diffusion ambiguity than by jump ambiguity,and trivially affected by volatility diffusion ambiguity.In addition,investors tend to be more aggressive in a stable market than in a volatile one.Next,given a larger volatility jump size,investors tend to increase their portfolio during downward price jumps and decrease it during upward price jumps.Finally,the welfare loss caused by price diffusion ambiguity is more pronounced than that caused by jump ambiguity in an incomplete market.These findings enrich the extant literature on effects of ambiguity on the traditional stock market and the evolving cryptocurrency market.The results have implications for both investors and regulators.

Portfolio Choice under the Mean-Variance Model with Parameter Uncertainty

Assuming the investor is uncertainty-aversion,the multiprior approach is applied to studying the problem of portfolio choice under the uncertainty about the expected return of risky asset based on the mean-variance model. By introducing a set of constraint constants to measure uncertainty degree of the estimated expected return,it built the max-min model of multi-prior portfolio,and utilized the Lagrange method to obtain the closed-form solution of the model,which was compared with the mean-variance model and the minimum-variance model; then,an empirical study was done based on the monthly returns over the period June 2011 to May 2014 of eight kinds of stocks in Shanghai Exchange 50 Index. Results showed,the weight of multi-prior portfolio was a weighted average of the weight of mean-variance portfolio and that of minimumvariance portfolio; the steady of multi-prior portfolio was strengthened compared with the mean-variance portfolio; the performance of multi-prior portfolio was greater than that of minimum-variance portfolio. The study demonstrates that the investor can improve the steady of multi-prior portfolio as well as its performance for some appropriate constraint constants.

Precautionary Saving and Health Insurance： A Portfolio Choice Perspective

This paper analyzes the impact of health insurance on household portfolio choice. Using the U.S. Survey of Consumer Finance and Health Retirement Survey databases, it finds that insured households are more likely to own stocks and invest a larger proportion of financial assets in stocks than uninsured households do. The results remain strong even after controlling for household characteristics and reverse causality. Furthel, the results are robust across different survey years and data sources. It suggests that a precautionary motive is strong in household portfolio choice decisions.

Are suspicious activity reporting requirements for cryptocurrency exchanges effective?

This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities.We build a theoretical model for the reporting decision structure of a private bank or cryptocurrency exchange and show that an inferior ability to detect money laundering(ML)increases the ratio of reported transactions to unreported transactions.If a representative money launderer makes an optimal portfolio choice,then this ratio increases further.Our findings suggest that cryptocurrency exchanges will exhibit more excessive reporting behavior under this regulation than private banks.We attribute this result to cryptocurrency exchanges’inferior ML detection abilities and their proximity to the underground economy.

Behavioral Portfolio Selection with Loss Control

In this paper we formulate a continuous-time behavioral （4 la cumulative prospect theory） portfolio selection model where the losses are constrained by a pre-specified upper bound. Economically the model is motivated by the previously proved fact that the losses Occurring in a bad state of the world can be catastrophic for an unconstrained model. Mathematically solving the model boils down to solving a concave Choquet minimization problem with an additional upper bound. We derive the optimal solution explicitly for such a loss control model. The optimal terminal wealth profile is in general characterized by three pieces： the agent has gains in the good states of the world, gets a moderate, endogenously constant loss in the intermediate states, and suffers the maximal loss （which is the given bound for losses） in the bad states. Examples are given to illustrate the general results.

A New Method of Portfolio Optimization Under Cumulative Prospect Theory

In this paper, the portfolio selection problem under Cumulative Prospect Theory （CPT） is investigated and a model of portfolio optimization is presented. This model is solved by coupling scenario generation techniques with a genetic algorithm. Moreover, an Adaptive Real-Coded Genetic Algorithm （ARCGA） is developed to find the optimal solution for the proposed model. Computational results show that the proposed method solves the portfolio selection model and that ARCGA is an effective and stable algorithm. We compare the portfolio choices of CPT investors based on various bootstrap techniques for scenario generation and empirically examine the effect of reference points on investment behavior.

Portfolio optimisation using constrained hierarchical bayes models

It iswell known that traditionalmean-variance optimal portfolio delivers rather erratic and unsatisfactory out-of-sample performance due to the neglect of estimation errors.Constrained solutions,such as no-short-sale-constrained and norm-constrained portfolios,can usually achieve much higher ex post Sharpe ratio.Bayesian methods have also been shown to be superior to traditional plug-in estimator by incorporating parameter uncertainty through prior distributions.In this paper,we develop an innovative method that induces priors directly on optimal portfolio weights and imposing constraints a priori in our hierarchical Bayes model.We showthat such constructed portfolios are well diversified with superior out-of-sample performance.Our proposed model is tested on a number of Fama–French industry portfolios against the na飗e diversification strategy and Chevrier and McCulloch’s(2008)economically motivated prior(EMP)strategy.On average,our model outperforms Chevrier and McCulloch’s(2008)EMP strategy by over 15%and outperform the‘1/N’strategy by over 50%.

Risk and Potential:An Asset Allocation Framework with Applications to Robo-Advising

We propose a novel dynamic asset allocation framework based on a family of mean-variance-induced utility functions that alleviate the non-monotonicity and time-inconsistency problems of mean-variance optimization.The utility functions are motivated by the equivalence between the mean-variance objective and a quadratic utility function.Crucially,our framework differs from mean-variance analysis in that we allow different treatment of upside and downside deviations from a target wealth level.This naturally leads to a different characterization of possible investment outcomes below and above a target wealth as risk and potential.Our proposed asset allocation framework retains two attractive features of mean-variance optimization:an intuitive explanation of the investment objective and an easily computed optimal strategy.We establish a semi-analytical solution for the optimal trading strategy in our framework and provide numerical examples to illustrate its behavior.Finally,we discuss applications of this framework to robo-advisors.