Markov Chain Monte Carlo in Portfolio Optimisation and Efficient Frontier Exploration

Investors seek to balance risk and return, aiming to maximize their potential gains while minimizing the associated volatility.

Constructing an optimal investment portfolio is a fundamental challenge in modern finance. Investors seek to balance risk and return, aiming to maximize their potential gains while minimizing the associated volatility.

Markov Chain Monte Carlo (MCMC), a powerful computational technique based on the idea that investors can optimize their portfolio by selecting a combination of assets that maximizes expected return for a given level of risk or minimizes risk for a given level of expected return. This is achieved by diversifying the portfolio across different assets and sectors, which helps to reduce the overall volatility and risk of the portfolio.

Computational method that can be employed to explore the efficient frontier - the set of optimal portfolios that offer the highest expected return for a given level of risk.

In this article, I will demystify MCMC, break it down into digestible chunks, and explore its real-world applications.

What is MCMC?

MCMC is a class of algorithms that use ran…