Asymmetric Volatility
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Presentation about Volatility Trading Techniques at London Traders & Investors Club

Presentation about Volatility Trading Techniques at London Traders & Investors Club

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Asymmetric Volatility Presentation Transcript

  • 1. Traders and Investors Club
    Asymmetric Volatility and Leverage Effect
  • 2. Introduction
    1) THEORY AND DEFINITIONS
    2) VOLATILITY PROXY AND LOG-RETURNS
    3) STOCHASTIC VOLATILITY
    3A) STOCHASTIC VOLATILITY CHARTS: GOLD,CRUDE OIL,FTSE and EURO vs DOLLAR
  • 3. Leverage Effect
    1)“ Negative returns seemed to be more important predictors of volatility than positive returns. Large prices declines forecast greater volatility than similarly large prices increases” (R. Engle)
  • 4. Leverage Effect
    2)”Volatility of stocks tends to increase when the price drops” (F. Black)
    3)”Negative correlation between past returns and future volatility”(J.P. Bouchaud)
  • 5. Types of Volatility
    Actual Historical
    Volatility over a specified period but with the last observation on a date in the past
  • 6. Types of Volatility
    Actual Future
    Volatility over a period starting at the current time and ending at a future date (options’ expiration date)
  • 7. Types of Volatility
    Implied
    Volatility observed from historical prices of options
    (Black-Scholes model)
  • 8. Types of Volatility
    Stochastic Volatility
    Tendency of volatility to revert to some long-run mean value (GARCH family models, Chen model, Heston model, etc)
  • 9. Proxy for Volatility
    True volatility cannot be observed because it is very difficult to separate:
    - market-wide factors (systematic variables)
    • stock-specific factors (idiosyncratic variables).
    Therefore, log-normal returns are usually employed as a proxy for the true volatility.
  • 10. Log-Normal Returns
    The log-normally distribution of data allows for a more accurate estimation of the return sensitivity for a given change in the information set available in the market for any given time period
  • 11. Log-Normal Returns
    Rt = ln (Pt / Pt-1)
    Where Rt denotes the log - return at time t for the asset price , Pt denotes the price at time t whilst Pt-1 represents the price at time t-1.
  • 12. Stochastic Volatility
    GARCH Model: GARCH (Generalised Autoregressive Conditional Heteroskedasticity) it assumes that the randomness of variance process varies with variance.
  • 13. Stochastic Volatility
    The GARCH variance is a weighted average of 3 different variables:
    1) Long run average volatility
    2) Forecasted volatility values calculated in previous period
    3) New information not available when the previous forecast was made
  • 14. Crude Oil Futures Market
  • 15. News Impact Curve – CL1
  • 16. Residuals – Crude Oil
  • 17. Gold Futures Market
  • 18. News Impact Curve – Gold
  • 19. Residuals - Gold
  • 20. FTSE 100
  • 21. News Impact Curve – FTSE100
  • 22. Residuals – FTSE100
  • 23. Euro vs Dollar
  • 24. News Impact Curve – EurvsDol
  • 25. Residuals – Euro vs Dollar
  • 26. VIX
  • 27. Crude Oil 10 years Impact Curve
  • 28. Conclusions
    The analysed markets present strong evidence of leverage effect processes
    The financial crises “re-shaped” many markets that were usually considered NOT TO BE LEVERAGED (Currency markets, Crude Oil , Gold, commodity markets)
  • 29. Conclusions
    In leveraged markets returns drop much more quickly than “normal markets”
    Asymmetric volatility can be used to scale trades and re-enforce short or long positions
    Asymmetric volatility is often used in options and futures strategies both for speculating and hedging purposes