An Anticipative Markov Modulated Market

TL;DR

This paper introduces a novel anticipative Markov modulation model for financial markets, allowing for future-aware market trend predictions and analyzing their impact on optimal portfolio strategies.

Contribution

It develops a new mathematical framework using enlargement of filtrations to incorporate anticipative Markov chains into market modeling.

Findings

Enhanced investor gains with anticipative information

Explicit solutions for optimal portfolios in anticipative settings

Comparison of complete and incomplete market scenarios

Abstract

A Markovian modulation captures the trend in the market and influences the market coefficients accordingly. The different scenarios presented by the market are modeled as the distinct states of a discrete-time Markov chain. In our paper, we assume the existence of such modulation in a market and, as a novelty, we assume that it can be anticipative with respect to the future of the Brownian motion that drives the dynamics of the risky asset. By employing these own techniques of enlargement of filtrations, we solve an optimal portfolio utility problem in both a complete and an incomplete market. Many examples of anticipative Markov chains are presented for which we compute the additional gain of the investor who has a more accurate information.