Asymmetric Volatility
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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 Asymmetric Volatility Presentation Transcript

    • Traders and Investors Club
      Asymmetric Volatility and Leverage Effect
    • 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
    • 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)
    • 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)
    • Types of Volatility
      Actual Historical
      Volatility over a specified period but with the last observation on a date in the past
    • Types of Volatility
      Actual Future
      Volatility over a period starting at the current time and ending at a future date (options’ expiration date)
    • Types of Volatility
      Implied
      Volatility observed from historical prices of options
      (Black-Scholes model)
    • Types of Volatility
      Stochastic Volatility
      Tendency of volatility to revert to some long-run mean value (GARCH family models, Chen model, Heston model, etc)
    • 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.
    • 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
    • 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.
    • Stochastic Volatility
      GARCH Model: GARCH (Generalised Autoregressive Conditional Heteroskedasticity) it assumes that the randomness of variance process varies with variance.
    • 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
    • Crude Oil Futures Market
    • News Impact Curve – CL1
    • Residuals – Crude Oil
    • Gold Futures Market
    • News Impact Curve – Gold
    • Residuals - Gold
    • FTSE 100
    • News Impact Curve – FTSE100
    • Residuals – FTSE100
    • Euro vs Dollar
    • News Impact Curve – EurvsDol
    • Residuals – Euro vs Dollar
    • VIX
    • Crude Oil 10 years Impact Curve
    • 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)
    • 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