Statistical Methods in Finance 2025

Financial Statistics with Julia

By: Sourish Das

Abstract: This workshop introduces participants to the statistical foundations and computational methods that drive modern financial analysis, using Julia as the programming environment. The sessions will cover a structured sequence of topics, beginning with the Random Walk Hypothesis and Geometric Brownian Motion as models for asset price dynamics. Building on these foundations, we will explore mean-variance analysis, the Efficient Frontier, and techniques for Portfolio Optimization, leading naturally into the Capital Asset Pricing Model (CAPM) as a framework for risk-return trade-offs. Risk management will form a central component of the workshop, with discussions on Volatility Risk, Value at Risk (VaR), and related measures. The workshop will also provide an introduction to derivative pricing, focusing on European Options and the Binomial Option Pricing Model as accessible yet powerful tools for valuation. Emphasis will be placed on hands-on learning: participants will implement models and simulations in Julia, enabling them to connect theory with practice and to develop a computational toolkit for analysing financial markets. By the end of the workshop, attendees will have gained both conceptual clarity and practical coding experience in key areas of financial statistics.

Short Bio:Sourish Das is an Associate Professor in the Data Science Group at Chennai Mathematical Institute. He holds a Ph.D. in Statistics from the University of Connecticut and has conducted postdoctoral research at Duke University and SAMSI. Before joining CMI in 2013, he worked as a software engineer at SAS. He was instrumental in designing and launching the Data Science programme at CMI in 2018, one of the first of its kind in India, well before Data Science became mainstream in higher education. His research spans Bayesian statistics, machine learning, quantitative finance, and environmental statistics, with applications ranging from agricultural risk modelling to sea ice analysis. He has published widely in these areas and was awarded the Commonwealth-Rutherford Fellowship in 2018 for his work on functional big data in environmental applications. At CMI, he teaches courses on predictive analytics, financial statistics, and Bayesian data analysis, and actively mentors students in data science research.

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