Contents
How does the GARCH model of price volatility work?
The idea of the GARCH model of price volatility is to use recent realizations of the error structure to predict future realizations of the error structure. Put more simply, we often see clustering in periods of high or low volatility, so we can exploit the recent volatility to predict volatility in the near future.
How are GARCH models used in real life?
GARCH stands for Generalized Autoregressive Conditional Heteroskedasticity Models. GARCH models are commonly used to estimate the volatility of returns for stocks, currencies, indices cryptocurrencies. Professional traders use this tool to price assets and detect which asset will potentially provide the best return in their portfolio.
How to calculate conditional variance in the GARCH model?
The last line follows since , and the . So in the second equation of the GARCH model, multiplying the and the takes advantage of the properties of variance to get just what we wanted, conditional variance of that will be big when recent volatility is big and small when recent volatility is small.
Can a GARCH model be used for Spy Returns?
Since the ARIMA model assumed constant variance, and the figure of SPY returns clearly has changing variance over time, this is something that can be improved upon, and the GARCH model is one way of accomplishing this. Next, we will go through two ways that are commonly used to visualize the changing variance of returns.
Which is the best model for modeling volatility?
In this post we will learn a standard technique for modelling volatility in a series of prices, the generalized auto-regressive conditional heteroskedasticity (GARCH) model.
How to model the variance of price returns?
As a reminder, the over-arching goal of of this and the previous posts has been to model the changing mean and variance of the price return series. The previous post used the ARIMA model to give structure to the changing mean of the series of price returns.