A Practical Guide to Forecasting Financial Market Volatility - download pdf or read online

By Ser-Huang Poon

ISBN-10: 0470856130

ISBN-13: 9780470856130

Monetary marketplace volatility forecasting is one among modern most vital parts of workmanship for pros and teachers in funding, choice pricing, and fiscal marketplace law. whereas many books handle monetary industry modelling, no unmarried booklet is dedicated essentially to the exploration of volatility forecasting and the sensible use of forecasting types. a pragmatic advisor to Forecasting monetary marketplace Volatility presents sensible advice in this important subject via an in-depth exam of quite a number well known forecasting types. information are supplied on confirmed ideas for construction volatility types, with guide-lines for truly utilizing them in forecasting functions.

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Extra info for A Practical Guide to Forecasting Financial Market Volatility

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The squaring of the error again will give greater weight to large errors. 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.

In the pre-ARCH era, there were many studies that covered a wide range of issues. Sometimes forecasters would introduce ‘learning’ by allowing parameters and weights of combined forecasts to be dynamically updated. These frequent updates did not always lead to better results, however. Dimson and Marsh (1990) found ex ante time-varying optimized weighting schemes do not always work well in out-of-sample forecasts. Sill (1993) found S&P500 volatility was higher during recession and that commercial T-Bill spread helped to predict stock-market volatility.

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. Given that most investors would treat gains and losses differently, the use of asymmetric loss functions may be advisable, but their use is not common in the literature. 3 COMPARING FORECAST ERRORS OF DIFFERENT MODELS In the special case where the error distribution of one forecasting model dominates that of another forecasting model, the comparison is Volatility Forecast Evaluation 25 straightforward (Granger, 1999).

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

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