Forecasting with VAR models: Fat tails and stochastic volatility
نویسندگان
چکیده
منابع مشابه
Volatility Forecasting II: Stochastic Volatility Models and Empirical Evidence
ln(σt) = α + φ(ln(σt−1)− α) + ηt so that ln(σt) is an AR(1) process, where φ is a parameter that represents how quickly volatility gets pulled toward its mean, α. If ηt is normally distributed with mean 0 and variance σ η, then ln(σt) is normally distributed, and σt therefore has a lognormal distribution. To get the unconditional mean and variance of ln(σt), E[ln(σt)] = α + φ(E[ln(σt−1)]− α) + ...
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ژورنال
عنوان ژورنال: International Journal of Forecasting
سال: 2017
ISSN: 0169-2070
DOI: 10.1016/j.ijforecast.2017.03.001