Volatility-Modelling-Using-HiddenMarkovModels
Applying Hidden Markov Models to model Gold Intraday Volatility by detecting regime switches from low-volatility regimes to high-volatility
Hidden Markov Models (HMMs) are a class of probabilistic graphical model that allow us to predict a sequence of unknown (hidden) variables from a set of observed variables. A > simple example of an HMM is predicting the weather (hidden variable) based on the type of clothes that someone wears (observed)."
The aim of this repository is to share with you my foray into volatility modelling using HiddenMarkovModels (HMM's.) HMM's were picked for this for their state-ful nature. Markets have been shown to exist in regimes, states if you were, and if you can predict when a market ia bout to switch regime - sya from low volatility to high volatility it can be monetized. This is what I have done within this repo.
The values beind modelled are intraday returns. The model aims to predict when the market will switch from low intraday returns (low volatility) to high intraday returns (high volatility)
I have modelled the volatility regime intraday (1D Candles) of Gold Prices (obtained from NASDAQ) from 2013-2021. The model aims to provide you with a graph which alternates from 0 (High Volatility) to 1 (Low Volatility) providing direction from when the Gold market is about to switch regime.
I have chosen to make this piece of research public as intra-day investing does not suit my personal style and I am eternally grateful to the entire open source community and so felt it was only right for me to give back.
All data and notebooks are provided.
Here is what the model outputs for the last circa 500 days of Gold price movement. As you can see highlighted in red are the clusters of high volatility.
