Abstract
Volatility Forecasting - A Performance Measure of Garch Techniques with Different Distribution Models
Highlights
Volatility plays a key role in finance it is responsible for option pricing and risk management
Garch technique with Generalized Error Distribution (GED) model can be considered as accurate technique compared to other models
There has been a tremendous research in Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in volatility forecasting, all these forecasting researches are based on usage of different GARCH techniques such as EGARCH, TGARCH and GARCH with maximum likelihood estimation
Summary
Volatility plays a key role in finance it is responsible for option pricing and risk management. Volatility is directly associated with risks and returns, higher the volatility the more financial market is unstable. It may result in both High profits or huge loses if volatility is changing at higher rate. Volatility directly or indirectly controls asset return series, equity prices and foreign exchange rates. If the pattern of volatility clusters is studied for longer duration we observe that, once if volatility reaches its highest point it will continue for a longer duration. These are readily recognized by Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model introduced by Bollerslev [1986]. While considering the volatility dynamic with standout lagged period, the GARCH (1, 1) model has turned into a workhorse in both scholarly and practice because of its effortlessness and instinctive understanding
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