By Ser-Huang Poon
Monetary marketplace volatility forecasting is certainly one of state-of-the-art most vital parts of craftsmanship for execs and teachers in funding, alternative pricing, and monetary marketplace legislation. whereas many books deal with monetary industry modelling, no unmarried ebook is dedicated basically to the exploration of volatility forecasting and the sensible use of forecasting types. A useful consultant to Forecasting monetary marketplace Volatility offers functional assistance in this important subject via an in-depth exam of a number well known forecasting versions. info are supplied on confirmed strategies for construction volatility types, with guide-lines for really utilizing them in forecasting functions.
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Additional info for A Practical Guide to Forecasting Financial Market Volatility
So if an error statistic of model A is higher than that of model B, one cannot conclude that model B is better than A without performing tests of signiﬁcance. For statistical inference, West (1996), West and Cho (1995) and West and McCracken (1998) show how standard errors for ME, MSE, MAE and RMSE may be derived taking into account serial correlation in the forecast errors and uncertainty inherent in volatility model parameter estimates. If there are T number of observations in the sample and T is large, there are two ways in which out-of-sample forecasts may be made.
1 ENGLE (1982) The ARCH model, ﬁrst introduced by Engle (1982), has been extended by many researchers and extensively surveyed in Bera and Higgins (1993), Bollerslev, Chou and Kroner (1992), Bollerslev, Engle and Nelson (1994) and Diebold and Lopez (1995). In contrast to the historical volatility models described in the previous chapter, ARCH models do not make use of the past standard deviations, but formulate conditional variance, h t , of asset returns via maximum likelihood procedures. 1) where z t ∼ D (0, 1) is a white noise.
There is no restriction on the frequency or the size of the breaks. The second advocates the regime-switching model where volatility switches between high and low volatility states. The means of the two states are ﬁxed, but there is no restriction on the timing of the switch, the duration of each regime and the probability of switching. Sometimes a three-regime switching is adopted but, as the number of regimes increases, the estimation and modelling become more complex. Technically speaking, if there are inﬁnite numbers of regimes then there is no difference between the two models.