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  1. Nicole Seaman

    YouTube T2-26: Maximum likelihood estimation of GARCH parameters

    GARCH(1,1) is the popular approach to estimating volatility, but its disadvantage (compared to STDDEV or EWMA) is that you need to fit three parameters. Maximum likelihood estimation, MLE, is an immensely useful statistical approach that can be used to find "best fit" parameters. In this video...
  2. Nicole Seaman

    YouTube T2-25: Comparing volatility approaches: MA versus EWMA versus GARCH

    The general form for all three is: σ^2(n) = γ*V(L) + α*u^2(n-1) + σ^2(n-1).
  3. Nicole Seaman

    YouTube T2-24: Forecast volatility with GARCH(1,1)

    The GARCH(1,1) volatility forecast is largely a function of the first term omega, ω = γ*V(L), which itself is the product of a rate of reversion, γ, and a reversion level, V(L); aka, long-run or unconditional variance David's XLS is here: https://trtl.bz/2yGdnjv
  4. Nicole Seaman

    YouTube T2-23: Volatility: GARCH 1,1

    The GARCH(1,1) volatility estimate shares a similarity to EWMA volatility: both assign greater (lesser) weight to recent (distant) returns. But the GARCH(1,1) has an additional feature: it models a long-run (aka, unconditional) variance toward which the volatility series is pulled. David's XLS...
  5. Nicole Seaman

    P1.T2.706. Bivariate normal distribution (Hull)

    Learning objectives: Calculate covariance using the EWMA and GARCH(1,1) models. Apply the consistency condition to covariance. Describe the procedure of generating samples from a bivariate normal distribution. Describe properties of correlations between normally distributed variables when using...
  6. Nicole Seaman

    P1.T2.704. Forecasting volatility with GARCH (Hull)

    Learning objectives: Explain mean reversion and how it is captured in the GARCH(1,1) model. Explain the weights in the EWMA and GARCH(1,1) models. Explain how GARCH models perform in volatility forecasting. Describe the volatility term structure and the impact of volatility changes. Questions...
  7. Nicole Seaman

    P1.T2.703. EWMA versus GARCH volatility (Hull)

    Learning objectives: Apply the exponentially weighted moving average (EWMA) model to estimate volatility. Describe the generalized autoregressive conditional heteroskedasticity (GARCH(p,q)) model for estimating volatility and its properties. Calculate volatility using the GARCH(1,1) model...
  8. U

    R16.P1.T2. Hull - expected value of u(n+t-1)^2

    In Hull - Risk Management and Financial Institutions, it is stated, in page 222 (10.10 using GARCH(1,1) to forecase future volatility), that: "the expected value of u(n+t−1)^2 is σ(n+t−1)^2". Is this something obvious? Can anybody explain why this should be the case? Thanks!
  9. jairamjana

    GARCH(1,1) vs EWMA for Forecasting Volatility

    So I link this video which explains GARCH(1,1) as a measure to forecast future volatility. Now we know EWMA is a special case of GARCH which sums alpha and beta equal to 1 and therefore ignores any impact on long run variance, implying that variance is not mean reverting.. Again when we...
  10. Nicole Seaman

    P1.T2.502. Covariance updates with EWMA and GARCH(1,1) models

    Learning outcomes: Define correlation and covariance, differentiate between correlation and dependence. Calculate covariance using the EWMA and GARCH (1,1) models. Apply the consistency condition to covariance. Questions: 502.1. About the consistency condition, each of the following is true...
  11. Nicole Seaman

    P1.T2.409 Volatility, GARCH(1,1) and EWMA

    Concept: These on-line quiz questions are not specifically linked to AIMs, but are instead based on recent sample questions. The difficulty level is a notch, or two notches, easier than bionicturtle.com's typical AIM-by-AIM question such that the intended difficulty level is nearer to an actual...