# garch

Recently, I wrote about fitting mean-reversion time series analysis models to financial data and using the models' predictions as the basis of a trading strategy. Continuing our exploration of time series modelling, let's research the autoregressive and conditionally heteroskedastic family of time series models. In particular, we want to understand the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models. Why? Well, they are both referenced frequently in the quantitative finance literature, and it's about time I got up to speed so why not join me! What follows is a summary of what I learned about these models, a general fitting procedure and a simple trading strategy based on the forecasts of a fitted model. Let's get started! What are these time series analysis models? Several definitions are necessary to set the scene. I don't want to reproduce the theory I've been wading through; rather here is my very high-level summary of what I've learned about time series modelling, in particular, the ARIMA and GARCH models and how they are related to their component models: At its...