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

By Ser-Huang Poon

Monetary industry volatility forecasting is one in every of state-of-the-art most crucial parts of craftsmanship for pros and lecturers in funding, choice pricing, and monetary industry law. whereas many books deal with monetary industry modelling, no unmarried ebook is dedicated essentially to the exploration of volatility forecasting and the sensible use of forecasting versions. a realistic consultant to Forecasting monetary industry Volatility offers useful advice in this very important subject via an in-depth exam of various renowned forecasting types. info are supplied on confirmed options for development volatility types, with guide-lines for really utilizing them in forecasting purposes.

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I am grateful to Frank de Jong for explaining this to me at a conference. 7 σ t,T | t−1 denotes a volatility forecast formulated at time t − 1 for volatility over the period from t to T . In pricing options, the required volatility parameter is the expected volatility over the life of the option. 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.

For j period) is taken to be the sum of individual multi-step point forecasts sj=1 h T + j|T . These multi-step point forecasts are produced by recursive substitution and using the fact that εT2 +i|T = h T +i|T for i > 0 and εT2 +i|T = εT2 +i for T + i ≤ 0. g. ) and the forecast horizon. If returns are iid (independent and identically distributed, or strict white noise), then variance of returns over a long horizon can be derived as a simple multiple of single-period variance. 2. 7 Complication in relation to the choice of forecast horizon is partly due to volatility mean reversion.

1 Single-state historical volatility models The simplest historical price model is the random walk model, where the difference between consecutive period volatility is modelled as a random noise; σt = σt−1 + vt , Historical Volatility Models 33 So the best forecast for tomorrow’s volatility is today’s volatility: σ t+1 = σt , where σt alone is used as a forecast for σt+1 . In contrast, the historical average method makes a forecast based on the entire history 1 (σt + σt−1 + · · · + σ1 ) . t The simple moving average method below, σ t+1 = 1 (σt + σt−1 + · · · + σt−τ −1 ) , τ is similar to the historical average method, except that older information is discarded.

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