Modeling Risk and Return using Dirichlet Process Prior

TL;DR

This paper introduces a nonparametric Bayesian approach using Dirichlet Process priors to model asset returns, capturing heavy tails and dependencies, and compares risk measures on real stock data.

Contribution

It develops a novel multivariate modeling framework for asset returns using Dirichlet Process priors and elliptical copulas, extending Bayesian nonparametrics to finance.

Findings

The mixture of GBM models market behavior with heavy tails.

The proposed DP-based models fit stock return data effectively.

Risk measures like VaR and CVaR are evaluated on real datasets.

Abstract

In this paper, we showed that the no-arbitrage condition holds if the market follows the mixture of the geometric Brownian motion (GBM). The mixture of GBM can incorporate heavy-tail behavior of the market. It automatically leads us to model the risk and return of multiple asset portfolios via the nonparametric Bayesian method. We present a Dirichlet Process (DP) prior via an urn-scheme for univariate modeling of the single asset return. This DP prior is presented in the spirit of dependent DP. We extend this approach to introduce a multivariate distribution to model the return on multiple assets via an elliptical copula; which models the marginal distribution using the DP prior. We compare different risk measures such as Value at Risk (VaR) and Conditional VaR (CVaR), also known as expected shortfall (ES) for the stock return data of two datasets. The first dataset contains the return of IBM, Intel and NASDAQ and the second dataset contains the return data of 51 stocks as part of the index "Nifty 50" for Indian equity markets.