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Mastering Volatility Models for Financial Forecasting

Explore Stochastic Volatility, ARCH(1), GARCH(1,1), GJR models, and more in financial forecasting. Learn about their estimation, extensions, and practical applications in creating volatility forecasts.

alden-mcmahon
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Mastering Volatility Models for Financial Forecasting

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  1. Fin250f: Lecture 5.2 Fall 2005 Reading: Taylor, chapter 9 Volatility Models

  2. Outline • Stochastic volatility models • ARCH(1) • GARCH(1,1) • GARCH(p,q) • GJR and volatility asymmetry

  3. Stochastic Volatility

  4. Stochastic Volatility • Very straightforward • Difficult to estimate • Extensions: • h(t) follows discrete markov process

  5. ARCH(1)Autoregressive Conditional Heteroskedasticity

  6. ARCH(1) • Alpha<1 • Omega>0 • Squared return correlations not persistent enough

  7. GARCH(1,1)

  8. GARCH(1,1) standardized residuals

  9. GARCH(1,1) • Most heavily used volatility model on Wall St. • Estimation: • maximum likelihood (not too difficult) • Moments • Variance • Skew = 0 • Kurtosis > 3

  10. GARCH volatility forecasts

  11. More volatility forecasts

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