Abstract
We have already discussed that volatility plays an important role in modeling financial systems and time series. Unlike the term structure, volatility is unobservable and thus must be estimated from market data.
Reliable estimations and forecasts of volatility are important for large credit institutes where volatility is directly used to measure risk. The risk premium, for example, is often specified as a function of volatility. It is interesting to find an appropriate model for volatility. The capability of macroeconomic factors to forecast volatility has already been examined in the literature. Although macroeconomic factors have some forecasting capabilities, the most important factor seems to be the lagged endogenous return. As a result recent studies are mainly concentrated on time series models.
Try to steal a chicken, but end up with losing the rice
Try to steal a chicken, but end up with losing the rice
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Borak, S., Härdle, W.K., López-Cabrera, B. (2013). Time Series with Stochastic Volatility. In: Statistics of Financial Markets. Universitext. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33929-5_13
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DOI: https://doi.org/10.1007/978-3-642-33929-5_13
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