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Introduction to R for Quantitative Finance by Zsolt Tulassay, Dr. Kata Váradi, Péter Csóka, Michael Puhle, Márton Michaletzky, Gergely Daróczi, Dr. Edina Berlinger, Daniel Havran, Agnes Vidovics-Dancs

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Modeling volatility

As we saw earlier, ARIMA models are used to model the conditional expectation of a process, given its past. For such a process, the conditional variance is constant. Real-world financial time series exhibit, among other characteristics, volatility clustering; that is, periods of relative calm are interrupted by bursts of volatility.

In this section we look at GARCH time series models that can take this stylized fact of real-world (financial) time series into account and apply these models to VaR forecasting.

Volatility forecasting for risk management

Financial institutions measure the risk of their activities using a Value-at-Risk (VaR), usually calculated at the 99% confidence level over a 10 business day horizon. This is the ...

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