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ewma

1. 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).
2. 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...
3. 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?
4. 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...
5. 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...
6. 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...
7. 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
8. Questions/Doubts

How can I understand the notations better?
9. 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...
10. 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...