OLS
OLS
OLS
Fichier Détails
| Cartes-fiches | 89 |
|---|---|
| Langue | English |
| Catégorie | Mathématiques |
| Niveau | Université |
| Crée / Actualisé | 16.11.2017 / 17.11.2017 |
| Lien de web |
https://card2brain.ch/box/20171116_ols
|
| Intégrer |
<iframe src="https://card2brain.ch/box/20171116_ols/embed" width="780" height="150" scrolling="no" frameborder="0"></iframe>
|
It suggests that an asset with a higher perceived risk would pay a higher return on average. In other words the mean return would be related to the variance of the return.
Negative surprises seem ti increase volatility more than positive surprises.
VaR measures the probability that a portfolio will face ist worse outcome.
The models can be used to estimate the dynamic relationship amongst variables; These models are used heavily for short-term forecasting.
Each variable depends on the lags of all variables included in the VAR; It is stationary if ist mean and variance are constant over time.
It is chosen such that the value of AIC and SIC are minimised; AIC tends to favour model with longer lags and SIC returns the mosta parsimonious model as it penalises extra estimated parameters more heavily.
If the lagged values of one variable have no explanatory power for all variables in the VAR, then this variable is said to be weakly exogenous; Granger causality can occur because a variable left out of the VAR is correlated with the variables included. So results should be interpreted with caution.
Can be used to measure how relationships between variables changes over time; Used to estimate stochastic volatility models that are central to many financial theories; Decompose time-series into a cycle and trend component; used to determine if a common component is driving a group of time-series; Estimate missing observations.
Provides estimates of the state given current information; one may be interested in estimating the state vectir at date t based on information contained in the entire dataset.
