Financial Risk Modeling with Markova Chain

Abstract

Investors use different approaches to select optimal portfolio. so, Optimal investment choices according to return can be interpreted in different models. The traditional approach to allocate portfolio selection called a mean - variance explains. Another approach is Markov chain. Markov chain is a random process without memory. This means that the conditional probability distribution of the next state depends only on the current state and not related to earlier events. This type of memory is called the Markov property. Based on proposed approach, the possibility of testing the assumption of independence of the intervals selected a portfolio of distribution of a relationship between these values there. The presence of this dependency, consider a model based on Markov chain makes it possible. In this paper, assuming that independent portfolios can be modeled by a Markov chain model to describe different portfolio selection, Value at risk (VaR) and Conditional Value at Risk (CVaR). In fact, the portfolio return is selected, the ranges are divided into n range, each interval of a discrete Markov chains, we consider the situation. Finally, the results of this study indicate that the optimal portfolio selection based on Markov models arehigh performance but complex.