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2
votes
0answers
72 views

Is it too important that my residuals be normal? I am Using an ARMA/GARCH model

I am trying to fit an ARMA/GARCH model to a time series. I found that the best candidate is an ARMA(1,0) + GARCH(1,1) with gaussian white noise It has coefficients with p-values near cero and the ...
2
votes
0answers
34 views

What kind of errors arise when I fit ARMA(1,1) to data generated from ARMA(1,1)-GARCH(1,1) process?

As far as I know estimates of parameters of ARMA(1,1) are asymptotically optimal when fitted to data from ARMA(1,1)-GARCH(1,1) process, and only their variance increase, so when we assume large ...
2
votes
1answer
123 views

ARIMA model, cannot get rid of low order ACF spike

I've gone through all the steps to fit a good ARIMA model - I plotted the data, I looked at the ADF tests, I looked at the ACF plot with no AR and MA terms just a constants. I came up with an ...
3
votes
0answers
76 views

Filtering out AR(1) effects before using stochastic volatility model

I wonder if I first filter out AR(1) (autoregressive model with lag 1) effects from univariate time series and then fit stochastic volatility model does above procedure introduce any bias at first or ...
4
votes
6answers
221 views

Find the order of an ARMA model (q & p )

I fit an ARMA model in Matlab and before I calculate the predicted value with the prediction error I set the order $(p,q)$ to some random value. But how can I determine the number of AR (p) and MA ...
3
votes
2answers
167 views

Intuition behind interest rate models

I am modelling the 3M yield of US Treasuries using an ARMA/ GARCH approach. Most interest rate models (e.g. Vasicek) describe the process as follows: $r_{t}-r_{t-1} = some ARMA+ \epsilon_t $ ...
3
votes
1answer
361 views

How is the MA (moving average model) useful?

How is the MA model useful in modeling financial data, for example the stock indices? For example, from what i understand in the AR (auto-regressive) model portion, we can use the ADF test to check ...
3
votes
0answers
200 views

Fitting Student t-distributions to log-returns

It seems that some tail-risk centric groups are bent on using Paretian and t-distributions to account for tail risk when fitting log-returns. It has been observed, however, that with and without ...
5
votes
0answers
1k views

Algorithm to fit AR(1)/GARCH(1,1) model of log-returns

I am fitting numerically an AR(1)/GARCH(1,1) process to index and stock log-returns, $r_t=\log(P_t/P_{t-1})$, where $P_t$ is the price at time $t$, and thus far am not clear on where the observed log ...
9
votes
1answer
336 views

rugarch: Joint estimation leads to different results

I want to fit an ARMA-GARCH model to my data using rugarch package in R. First of all, I look at the acf and pacf: ...
8
votes
2answers
3k views

Why do we use GARCH(1,1) to predict volatility?

What makes GARCH(1,1) so prevalent in modeling especially in academia? What does this model has that is significantly better than the others?
2
votes
1answer
649 views

R ARMA-GARCH rugarch package doesn't always converge

I'm trying to compute the standard ARMA(1,1)-GARCH(1,1) as shown in this answer for an entire index,just to store in a database to quickly lookup values for back ...
7
votes
3answers
792 views

DSP: stationary non-periodic signal: what's the best causal technique?

This is a bit DSP-related: so if you turn your non-stationary time series into a stationary process, you'll probably see that it is not periodic.. This is an issue for Fourier-based techniques because ...
4
votes
1answer
2k views

Mean reverting strategies

I would like to take advantage of a volatile market by selling highs and buying lows. As we all know the RSI indicator is very bad and I want to create a superior strategy for this purpose. I have ...