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

By Ser-Huang Poon

ISBN-10: 0470856130

ISBN-13: 9780470856130

Monetary industry volatility forecasting is one in every of modern most crucial components of craftsmanship for execs and teachers in funding, alternative pricing, and monetary marketplace law. whereas many books deal with monetary industry modelling, no unmarried e-book is dedicated essentially to the exploration of volatility forecasting and the sensible use of forecasting versions. a realistic consultant to Forecasting monetary marketplace Volatility presents sensible tips in this important subject via an in-depth exam of a number renowned forecasting types. information are supplied on confirmed ideas for construction volatility versions, with guide-lines for truly utilizing them in forecasting functions.

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The pricing model relies on a riskless hedge to be followed through until the option reaches maturity. Therefore the required volatility input, or the implied volatility derived, is a cumulative volatility forecast over the option maturity and not a point forecast of volatility at option maturity. The interest in forecasting σ t,T | t−1 goes beyond the riskless hedge argument, however. Volatility Definition and Estimation 17 weekly or monthly data is better because volatility mean reversion is difficult to adjust using high frequency data.

5) the conditional variance for the one-period returns, rt+1 ≡ pt+1 − pt , is t+1 2 σs ds which is known as the integrated volatility over the period t t to t + 1. Note that while asset price pt can be observed at time t, the volatility σt is an unobservable latent variable that scales the stochastic process d Wt continuously through time. 6) and realized volatility RVt+1 = j=1,···,m 2 rm,t+ j /m . If the discretely sampled returns are serially uncorrelated and the sample path for σt is continuous, it follows from the theory of quadratic variation (Karatzas and Shreve, 1988) that t+1 p lim m→∞ t σs2 ds − j=1,···,m 2 rm,t+ j /m = 0.

2). g. an AR(1) model: σt = γ + β1 σt−1 + υt . 2. 3) can be calculated as sample standard deviations if there are sufficient data for each t interval. 2 may be used, and in the most extreme case, where only one observation is available for each t interval, one often resorts to using absolute return to proxy for volatility at t. 1 we have highlighted the danger of using daily absolute or squared returns to proxy ‘actual’ daily volatility for the purpose of forecast evaluation, as this could lead to very misleading model ranking.

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


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