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

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

By Ser-Huang Poon

Monetary industry volatility forecasting is one in all latest most vital components of workmanship for pros and lecturers in funding, choice pricing, and fiscal industry rules. whereas many books handle monetary marketplace modelling, no unmarried ebook is dedicated essentially to the exploration of volatility forecasting and the sensible use of forecasting versions. a realistic advisor to Forecasting monetary industry Volatility offers sensible assistance in this very important subject via an in-depth exam of a number well known forecasting versions. info are supplied on confirmed options for construction volatility versions, with guide-lines for truly utilizing them in forecasting functions.

Show description

Read Online or Download A Practical Guide to Forecasting Financial Market Volatility PDF

Best investing books

The trader's guide to key economic indicators

The unstable inventory industry is popping severe traders into macroeconomic-data junkies. but figuring out simply what the commercial facts suggest, their position within the genuine machinations of the financial system and monetary markets, and the way to decipher the market's most likely reactions to the most recent pronouncements is a frightening problem.

The London Stock Exchange

In 2001, the London inventory alternate could be two hundred years outdated, notwithstanding its origins return a century earlier than that. This ebook strains the historical past of the London inventory trade from its beginnings round 1700 to the current day, chronicling the demanding situations and possibilities it has confronted, refrained from, or exploited through the years.

A Practical Guide to Forecasting Financial Market Volatility

Monetary marketplace volatility forecasting is one among trendy most vital parts of craftsmanship for pros and lecturers in funding, alternative pricing, and monetary industry law. whereas many books handle monetary industry modelling, no unmarried e-book is dedicated essentially to the exploration of volatility forecasting and the sensible use of forecasting types.

The Asian Financial Crisis: Causes, Cures, and Systemic Implications (Policy Analyses in International Economics)

The turmoil that rocked Asian markets after the center of 1997 and that unfold a long way afield used to be the 3rd significant forex trouble of the Nineties. Thailand, Indonesia, Malaysia, and South Korea suffered outright recessions in 1998. for you to include the situation, virtually $120 billion used to be pledged in IMF-led legitimate rescue applications.

Additional resources for A Practical Guide to Forecasting Financial Market Volatility

Example text

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.

2588, which means that εt2 is 50% greater or smaller than σt2 nearly 75% of the time! √ √ If rt ∼ N 0, σt2 , then E (|rt |) = σt 2/π. Hence, σ t = |rt |/ 2/π if rt has a conditional normal distribution. 4 For example, the maximum likelihood method proposed by Ball and Torous (1984), the high–low method proposed by Parkinson (1980) and Garman and Klass (1980). 4) are generated by a GARCH(1,1) process, Andersen and Bollerslev (1998) show that the population R 2 for the regression εt2 = α + βσ 2t + υt is equal to κ −1 where κ is the kurtosis of the standardized residuals and κ is finite.

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.

Download PDF sample

Rated 4.07 of 5 – based on 49 votes