# Tag Info

1

I would confirm it. For time series forecasting, one can use 3 versions of random walk: RW model 1 (basic geometric random walk): stock returns in different periods are statistically independent (uncorrelated) and identically distributed (constant volatility) RW model 2: stock returns in different periods are statistically independent bot not identically ...

3

The return equation is just an econometric equation that models stock returns (or other asset returns) as a function of: (i) intercept (i.e. the average return), (ii) some independent variables/features, (iii) noise that has zero mean and time-varying variance. There are sometimes other things in the return equation too that form more advanced models. The ...

4

Basically he's just saying that you don't have to estimate parameters assuming they're the same in every period. Arch and Garch parameters are typically estimated via maximum likelihood. In MLE, parameters are estimated by $$\theta \equiv argmax\left\{ \sum_{t=1}^{T}ln\left(f\left(x_{t}|\theta\right)\right)\right\}$$ where $\theta$ are some parameters ...

0

Your question is more about "how to estimate correlations between variables sampled at different frequencies?" than about PCA. After all, PCA is just diagonalization of the covariance (or correlation) matrix, aiming to obtain principal vectors driving the joint dynamics of your variables in an $L^2$ sense. Since data are by construction not synchronized at ...

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