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

By Ser-Huang Poon

Monetary marketplace volatility forecasting is one in all modern most vital parts of workmanship for execs and lecturers in funding, choice pricing, and monetary industry rules. whereas many books handle monetary industry modelling, no unmarried ebook is dedicated basically to the exploration of volatility forecasting and the sensible use of forecasting types. A functional advisor to Forecasting monetary industry Volatility presents functional assistance in this very important subject via an in-depth exam of a variety of renowned forecasting types. info are supplied on confirmed thoughts for development volatility versions, with guide-lines for really utilizing them in forecasting functions.

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Let the associated forecast errors be {eit }t=1 T and {e jt }t=1 . g. 2) such that g X t , X it = g (eit ) . Next define loss differential dt ≡ g (eit ) − g e jt . The null hypothesis is equal forecast accuracy and zero loss differential E(dt ) = 0. 1 Diebold and Mariano’s asymptotic test The first test targets on the mean 1 d= T T |g(eit ) − g(e jt )| t=1 with test statistic d S1 = 1 2π f d (0) T T −1 2π f d (0) = 1 τ =−(T −1) γ d (τ ) = 1 T S1 ∼ N (0, 1) τ γ d (τ ) S (T ) T dt − d dt−|τ | − d .

Do we penalize underforecast, X t < X t , more than overforecast, X t > X t ? (iv) Given that all error statistics are subject to noise, how do we know if one model is truly better than another? (v) How do we take into account when X t and X t+1 (and similarly for εt and X t ) cover a large amount of overlapping data and are serially correlated? All these issues will be considered in the following sections. 1 THE FORM OF Xt Here we argue that X t should be σt , and that if σt cannot be estimated with some accuracy it is best not to perform comparison across predictive models at all.

For thick tail distribution, the upper bound for R 2 is lower than 1/3. Christodoulakis and Satchell (1998) extend the results to include compound normals and the Gram–Charlier class of distributions confirming that the mis-estimation of forecast performance is likely to be worsened by nonnormality known to be widespread in financial data. Hence, the use of εt2 as a volatility proxy will lead to low R 2 and undermine the inference on forecast accuracy. Blair, Poon and Taylor (2001) report an increase of R 2 by three to four folds for the 1-day-ahead forecast when intraday 5-minutes squared returns instead of daily squared returns are used to proxy the actual volatility.

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