Does VIX or Volume improve GARCH volatility forecasts?
In answering the question of whether VIX produces better forecasts than the GARCH model, then the answer is no, but the informational content of VIX cannot be ignored and should be incorporated into forecast regressions.
Is GARCH a stochastic volatility model?
When we analyze the fit to observed prices, GARCH clearly dominates both stochastic volatility and the benchmark Black Scholes model. However, the predictions of the market risk from hypothetical derivative positions show sizable errors.
What does the GARCH model tell us?
GARCH models describe financial markets in which volatility can change, becoming more volatile during periods of financial crises or world events and less volatile during periods of relative calm and steady economic growth.
What is conditional volatility in GARCH?
Conditional volatility is the volatility of a random variable given some extra information. In the GARCH model, the conditional volatility is conditioned on past values of itself and of model errors. Unconditional volatility is the general volatility of a random variable when there is no extra information.
Is Garch stochastic?
The GARCH model has been extended via numerous variants, including the NGARCH, TGARCH, IGARCH, LGARCH, EGARCH, GJR-GARCH, etc. Strictly, however, the conditional volatilities from GARCH models are not stochastic since at time t the volatility is completely pre-determined (deterministic) given previous values.
In which way does a stochastic volatility model helps solving the implied volatility puzzle?
Stochastic volatility models correct for this by allowing the price volatility of the underlying security to fluctuate as a random variable. By allowing the price to vary, the stochastic volatility models improved the accuracy of calculations and forecasts.
What do high coefficients in the Garch model imply?
As the GARCH coefficient value is higher than the ARCH coefficient value, we can conclude that the volatility is highly persistent and clustering.
When might we use a stochastic volatility model?
In statistics, stochastic volatility models are those in which the variance of a stochastic process is itself randomly distributed. They are used in the field of mathematical finance to evaluate derivative securities, such as options.
Why stochastic volatility is important?