GARCH: Introduction to Trading
Beginners in financial trading must learn industry tools and models. An example is the GARCH model. This essay will explain GARCH and its significance to trading, especially for novices.
How about GARCH?
Econometric model GARCH predicts financial market volatility. Volatility is an asset’s price change over time. GARCH captures short-term and long-term volatility well.
In the 1980s, economists Robert F. Engle and Clive W.J. Granger developed the GARCH model, an extension of ARCH. The original ARCH model assumed constant asset return conditional variance. However, the GARCH model accounts for volatility variation in its computations.
How does GARCH work?
The GARCH model calculates asset return conditional variance using historical variances and shocks. It captures volatility dynamics using autoregressive and moving average parameters. The model estimates conditional volatility at each time period using this equation:
__(t) = __ + ____(t-1) + ___(t-1)
The conditional variance at time t is __(t) in this equation. __(t-1) is the squared error term at time t-1, while __, __, and _ are estimated parameters.
_____ represents the constant component of volatility, _____ represents the immediate effect of shocks on conditional variance, and _____ reflects historical volatility persistence. By evaluating these factors, traders may better understand asset volatility and make educated trading choices.
Trading GARCH Importance
GARCH helps traders control risk, making it crucial. By predicting volatility, traders may change their strategy and position sizes. High volatility times may need lower position sizes to limit risk, whereas low volatility periods may enable greater holdings.
GARCH also helps set stop-loss and take-profit levels. A stop-loss order limits losses, while a take-profit level specifies a price at which the trader wants to exit and profit. GARCH lets traders establish stop-loss and take-profit levels based on predicted volatility, managing risk.
Sources and Links
These sources were used to write this article:
R. F. Engle (1982). Autoregressive Conditional Heteroscedasticity with UK Inflation Variance Estimates. The Econometric Society Journal, 357-386.
T. Bollerslev (1986). Generalized ARCH heteroskedasticity. The Econometric Journal, 307-327.
R. S. Tsay (2010). Financial Time Series Analysis. J. Wiley.