ewma

  1. Nicole Seaman

    P1.T4.24.5. GARCH, EWMA, and Return Distributions

    Learning Objectives: Explain how asset return distributions tend to deviate from the normal distribution. Explain reasons for fat tails in a return distribution and describe their implications. Distinguish between conditional and unconditional distributions and describe regime switching. Compare...
  2. B

    Understanding EWMA

    Hi all, Refer to the attached file which covers EWMA, I dont get the paragraph highlighted in blue. If new weight (alpha sub i+1) = lambda x current weights (alpha sub i) where lambda is between 0 and 1, it means that new weights has a lesser weights compared to old weights. For eg, old weight...
  3. H

    EWMA model

    Hi, I have a question about the EWMA model equation. How comes we are using the previous day's return? I thought we are using today's return as \[ r_n=\ln(s_n/s_{n-1}) \].
  4. 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).
  5. Nicole Seaman

    YouTube T2-22: Volatility: Exponentially weighted moving average, EWMA

    The exponentially weighted moving average (EWMA) cures the key weakness of the common historical standard deviation by assigning greater weight to more recent returns and lessor weights to more distant (in the past) returns. Its key parameter is lambda, λ, which specifies the ratio of...
  6. Z

    EWMA model returns

    Hi, I am reviewing the EWMA model section and found returns were calculated on straight (Pt+1/Pt)-1, shouldn't the returns be calculated on log basis? Also what's the assumption for the test per se?
  7. 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...
  8. 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...
  9. 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...
  10. desh

    Calculating revised VaR Hybrid approach

    The 5th percentile should be between lowest and 2nd lowest transaction i.e. -4.70 % -4.10% then how -3.6% and -3.4% choosen?? Please clarify
  11. S

    Questions/Doubts

    How can I understand the notations better?
  12. 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...
  13. 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...
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