Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
A measure of the variation in price over time. Also a measure of the risk of a financial instrument.
12
votes
2
answers
716
views
How to estimate the following model?
Suppose I have the following model:
$$r_t=\sigma_t * \epsilon_t$$
where $r_t$ is the return at time t, $\sigma_t$ is the volatility, the model used to model this volatility is an exponentially weighted … Second question:
Suppose the volatility is modeled by an ARCH process. …
7
votes
1
answer
2k
views
Fitting distributions to financial data using volatility model to estimate VaR
The R code could look like (data):
volatility<-0
quantile<-0
for(i in 11:length(dat)){
volatility[i]<-sd(dat[(i-10):(i-1)])
}
for(i in 1:length(dat)){
quantile[i]<-qnorm(0.975,mean=0,sd=volatility[i] … No matter what volatility model they use, I cannot understand the connection of distribution and volatility model. …