Ser-Huang Poon's A Practical Guide to Forecasting Financial Market Volatility PDF

By Ser-Huang Poon

ISBN-10: 0470856130

ISBN-13: 9780470856130

Monetary marketplace volatility forecasting is one in all contemporary most crucial components of workmanship for execs and lecturers in funding, alternative pricing, and monetary industry rules. whereas many books deal with monetary marketplace modelling, no unmarried booklet is dedicated basically to the exploration of volatility forecasting and the sensible use of forecasting versions. a pragmatic advisor to Forecasting monetary industry Volatility presents useful assistance in this important subject via an in-depth exam of a variety of well known forecasting types. information are supplied on confirmed suggestions for construction volatility versions, with guide-lines for really utilizing them in forecasting functions.

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Extra resources for A Practical Guide to Forecasting Financial Market Volatility

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The confidence interval of the mean error statistic can be very wide when forecast errors are measured from variances and worse if they are squared. 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.

The two sign-based tests in particular continue to work well among small samples. The Diebold and Mariano tests have been used in a number of volatility forecasting contests. We provide the test details here. 26 Forecasting Financial Market Volatility T T T Let { X it }t=1 and { X jt }t=1 be two sets of forecasts for {X t }t=1 from T models i and j respectively. 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 ARCH (AutoRegressive Conditional Heteroscedasticity) model proposed by Engle (1982) was designed to capture volatility persistence in inflation. The ARCH model was later found to fit many financial time series and its widespread impact on finance has led to the Nobel Committee’s recognition of Rob Engle’s work in 2003. The ARCH effect has been shown to lead to high kurtosis which fits in well with the empirically observed tail thickness of many asset return distributions. The leverage effect, a phenomenon related to high volatility brought on by negative return, is often modelled with a sign-based return variable in the conditional volatility equation.

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


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