ewma

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...

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...

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 \[ r_n=\ln(s_n/s_{n-1}) \].

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

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...

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?

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...

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...

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...

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

How can I understand the notations better?

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...

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