Forecasting Stock Market Volatility with Kalman Filters

Jin Choi, PhD

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21 Aug 2023CPOL12 min read

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A layman's description of how Kalman Filters work, and sample code that shows how to use it to forecast stock market volatilities

Sample code and explanation of how to use Kalman Filters to forecast stock market volatilities. I provide a layman's description of how Kalman Filters work, with emphasis on the role that different variables play. I then provide sample code that implements a Kalman Filter using Pyro, a probabilistic machine learning library built on top of PyTorch.

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This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

Written By

Jin Choi, PhD

Eddy Wealth

Canada

I'm a Canadian financial advisor with a PhD in financial mathematics. I spent over a decade creating investment related algorithms for asset managers and banks. The algorithms I've created include risk models, portfolio allocation algorithms, and stock selection algorithms amongst others. I have a particular expertise in applying machine learning to finance related problems.

Forecasting Stock Market Volatility with Kalman Filters

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