By Ser-Huang Poon
Monetary marketplace volatility forecasting is considered one of latest most crucial parts of workmanship for execs and lecturers in funding, choice pricing, and monetary industry legislation. whereas many books handle monetary marketplace modelling, no unmarried e-book is dedicated basically to the exploration of volatility forecasting and the sensible use of forecasting types. a pragmatic advisor to Forecasting monetary marketplace Volatility offers useful tips in this important subject via an in-depth exam of more than a few renowned forecasting types. info are supplied on confirmed recommendations for construction volatility versions, with guide-lines for really utilizing them in forecasting functions.
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Extra info for A Practical Guide to Forecasting Financial Market Volatility
3)) with the sum of squared variation of X i . P compares the amount of variation in the forecast errors with that in actual volatility. If prediction errors are small, 30 Forecasting Financial Market Volatility P is closer to 1. 3), P is likely to be smaller than conventional R 2 . 5) can be greater than 1. A negative P means that the forecast errors have a greater amount of variation than the actual volatility, which is not a desirable characteristic for a well-behaved forecasting model. 5 OTHER ISSUES IN FORECAST EVALUATION In all forecast evaluations, it is important to distinguish in-sample and out-of-sample forecasts.
Do not contain additional information, then the regression coefﬁcients will not be different from zero. While it is useful to have an unbiased forecast, it is important to distinguish between bias and predictive power. A biased forecast can have predictive power if the bias can be corrected. An unbiased forecast is useless if all forecast errors are big. For X i to be considered as a good forecast, Var(υt ) should be small and R 2 for the regression should tend to 100%. Blair, Poon and Taylor (2001) use the proportion of explained variability, P, to measure explanatory power P =1− Xi − Xi 2 (X i − µ X )2 .
A decision maker might be more risk-averse towards the larger errors. 1 the impact of using squared returns to proxy daily volatility. Hansen and Lunde (2004b) used a series of simulations to show that ‘. . the substitution of a squared return for the conditional variance in the evaluation of ARCH-type models can result in an inferior model being chosen as [the] best with a probability converges to one as the sample size increases . . ’. Hansen and Lunde (2004a) advocate the use of realized volatility in forecast evaluation but caution the noise introduced by market macrostructure when the intraday returns are too short.
A Practical Guide to Forecasting Financial Market Volatility by Ser-Huang Poon