By Ser-Huang Poon
Monetary marketplace volatility forecasting is certainly one of ultra-modern most crucial components of workmanship for execs and teachers in funding, choice pricing, and fiscal industry legislation. whereas many books deal with monetary industry modelling, no unmarried e-book is dedicated essentially to the exploration of volatility forecasting and the sensible use of forecasting types. a realistic advisor to Forecasting monetary industry Volatility offers sensible tips in this very important subject via an in-depth exam of a variety of renowned forecasting versions. information are supplied on confirmed thoughts for construction volatility types, with guide-lines for truly utilizing them in forecasting functions.
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Additional info for A Practical Guide to Forecasting Financial Market Volatility
This leads to difﬁculty in ﬁnding signiﬁcant differences between forecasting models. Davidian and Carroll (1987) make similar observations in their study of variance function estimation for heteroscedastic regression. Using high-order theory, they show that the use of square returns for modelling variance is appropriate only for approximately normally distributed data, and becomes nonrobust when there is a small departure from normality. Estimation of the variance function that is based on logarithmic transformation or absolute returns is more robust against asymmetry and nonnormality.
Let ρτ denote the correlation between xt and xt−τ . The time series xt is said to have a short memory if nτ =1 ρτ converges to a constant as n becomes large. A long memory series has autocorrelation coefﬁcients that decline slowly at a hyperbolic rate. Long memory in volatility occurs when the effects of volatility shocks decay slowly which is often detected by the autocorrelation of measures of volatility, such as absolute or squared returns. A long memory process is covariance stationary if nτ =1 ρτ /τ 2d−1 , for some positive d < 12 , converges to a constant as n → ∞.
Research in ﬁnancial market volatility has been concentrating on modelling and less on forecasting. Work on combined forecast is rare, probably because the groups of researchers in time series models and option pricing do not seem to mix. What has not yet been done in the literature is to separate the forecasting period into ‘normal’ and ‘exceptional’ periods. It is conceivable that different forecasting methods are better suited to different trading environment and economic conditions. 3 Historical Volatility Models Compared with the other types of volatility models, the historical volatility models (HIS) are the easiest to manipulate and construct.
A Practical Guide to Forecasting Financial Market Volatility by Ser-Huang Poon