A Practical Guide to Forecasting Financial Market Volatility by Ser-Huang Poon

By Ser-Huang Poon

Monetary marketplace volatility forecasting is considered one of modern most vital components of craftsmanship for execs and teachers in funding, choice pricing, and fiscal marketplace legislation. whereas many books deal with monetary marketplace modelling, no unmarried ebook is dedicated essentially to the exploration of volatility forecasting and the sensible use of forecasting types. a realistic consultant to Forecasting monetary industry Volatility presents functional advice in this important subject via an in-depth exam of a variety of renowned forecasting types. info are supplied on confirmed suggestions for construction volatility types, with guide-lines for truly utilizing them in forecasting purposes.

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In practice, the rolling scheme is more popular, where a fixed number of observations is used in the estimation. So the forecast for n + 2 will be based on information set {2, · · · , n + 1}, and the last forecast at T will be based on {T − n, · · · , T − 1}. The rolling scheme omits information in the distant past. It is also more manageable in terms of computation when T is very large. The standard errors developed by West and co-authors are based on asymptotic theory and work for recursive scheme only.

1) 2 t=1 where σ tB M is the benchmark forecast, used here to remove the effect of any scalar transformation applied to σt . LINEX has asymmetric loss function whereby the positive errors are weighted differently from the negative errors: LINEX = 1 N N [exp {−a (σ t − σt )} + a (σ t − σt ) − 1]. 2) t=1 The choice of the parameter a is subjective. If a > 0, the function is approximately linear for overprediction and exponential for underprediction. Granger (1999) describes a variety of other asymmetric loss functions of which the LINEX is an example.

4 Serially correlated loss differentials Serial correlation is explicitly taken care of in S1 . For S2 and S3 (and their asymptotic counter parts S2a and S3a ), the following k-set of loss differentials have to be tested jointly di j,1 , di j,1+k , di j,1+2k , · · · , di j,2 , di j,2+k , di j,2+2k , · · · , .. di j,k , di j,2k , di j,3k , · · · . A test with size bounded by α is then tested k times, each of size α/k, on each of the above k loss-differentials sequences. The null hypothesis of equal forecast accuracy is rejected if the null is rejected for any of the k samples.

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