<t>The long memory property of S&P 500 daily returns has been documented in Ding and Granger (1996). Long memory Garch models include PARCH (Ding, Granger and Engle (1993)) and FIGARCH/FIEGARCH (Baillie, Bollerslev and Mikkelsen(1996)). There might be others as well. PARCH seems to be easier to ...

<t>Exotiq,If the data is skewed you are going have a hard time fitting a square into a round hole (i.e. a skewed distribution into a normal distribution). You are assuming that the GARCH parameters will somehow able to explain way the skewness. It might, but then again it probably will not. I don't ...

<t>Exotiq, Two strends of not-so-developed literature out there ... One estimates the skewness and kurtosis a la GARCH style, done in additional to variance estimation. The kurtosis and skewnes is, if memory is correct, time varying. The results are not very statisfactory. The other estimates the ga...

<t>Lees, See #2, #3 and #5 below for possible reasons for your problem.Reasons for "badly behaved" parameters:1) bad starting values (ex. starting values where loglikelihood is undefined or close to undefined, value too far from the optimum value, etc.)2) sample too small -- not enough data points t...

<t>Muzzex,1) Does these packages offer the ability of variance targetting? If GARCH's volatility is too high and you do not have large enough sample, the constant in the volatility equation might be important. Usually people estimate that constant. However, another way is to use variance targeting. ...

<t>Habib, In general you'll like to have more observations than less. A report Engle and I prepared a while back indicates that parameters estimated from small sample such as 500 observations tend to be unstable. 1000 or more would be better. Anyway, it's a good question. I think the best way to mea...

<t>MAE and RMSE might not be the best criteria to use. The distribution of squared returns (sq'd rt) is quite non-normal. I suspect, don't know for sure, that these forecasting criteria might best be used if data is close to normal. One idea might be to use the log of sq'd rt vs. log of the forecast...

<t>Remember the squared returns are just a proxy for the realized and unobservable variance. You'll have to define "perform very bad." What criteria are you using? Are you sure you moving average has the same informationally content as your GARCH model? That is you are not using information at time ...

<t>Criteria are created because we don’t live in asymptopia. They are small sample corrections, as you term it ad hoc. Theories do not tell you which one is the most reasonable. If the theory doesn't show you the way, then what do you have left to go on? Schwarz criterion (BIC) is usually more restr...

<t>QuoteOriginally posted by: blueseasearching choleskey decomposition in google lead me to this page.I have such topic this quarter and what my instructor`s equation is X=chol(cov())*randn()+Mu()I have no idea why it is the product of chol, not directly of covariance? Cholesky decoposition remain t...

<t>QuoteOriginally posted by: mrbadguy2.Could be interesting comparing Black-Scholes versus GARCH option pricing models given following process for conditional variance in Garch(1,1): h(t+1)=w+alfa*epsilon(t)*h(t)+beta*h(t) any ideas?Thanks.There are two published papers on GARCH option pricing. One...

<t>adannenberg, they don't teach this in grad school, huh. When it comes down to it there is no easy answer. There is no magic bullet that will work in all situations. A method that works for one set of models might not work for another. As you have alluded, nothing replaces real world human experie...

<t>There are three methods that I am aware of that might make this problem tractable. Riskmetric uses exponential moving average to estimate correlation between assets. It's not too difficult to implement. Robert Engle's new DCC (Dynamic Conditional Correlation) model is able to estimate large corre...