7
votes
What is the preferred GARCH method in practice?
I personally use the simple Garch(1,1) for volatility filtering in the risk management area.
In fact in most cases I don't even estimate the parameters, I stick 0.94 for mean reversion, 0.04 for the ...
- 4,307
5
votes
Accepted
Simulating from a multivariate clayton copula
Since I think this is of interest for other people, I will post the approach I found:
First, let $C_n(u_1,\ldots,u_n)$ be a $n$ - dimensional Clayton copula with generator function $F$ and inverse $F^...
- 383
3
votes
How to include heteroscedasticity in copula modelling
I don't know if this will help solve your convergence issue, but a standard way of incorporating conditional heteroskedasticity in copula models is to build a copula-GARCH model. Each time series is ...
- 2,726
3
votes
Generally how to simulate bivariate (or multidimensional) BM sample paths?
For the two-dimensional case, the Cholesky decomposition of the covariance matrix
\begin{equation}
\Sigma = \left( \begin{array}{c c} \sigma_1^2 & \rho \sigma_1 \sigma_2\\ \rho \sigma_1 \sigma_2 &...
- 5,959
3
votes
Multivariate GARCH in Python
I recently met the same problem and found a way to achieve it using R in Python.
...
- 31
3
votes
What is the preferred GARCH method in practice?
Interesting question, as All the answers (including mine) could not be generalized unfortunately. As far as I am concerned, I use a univariate EGARCH for risk modelling purposes (Filtered Historical ...
- 315
2
votes
DCC GARCH: specifying ARCH and GARCH parameter matrices in STATA
How can I change this to implement FULL ARCH and GARCH parameter matrices, to capture the spillover effects?
You cannot.
The original paper by Engle (2002) as well as the Stata manual for the DCC-...
- 2,726
2
votes
VEC GARCH (1,1) for 4 time series
VECM-GARCH models do not seem to be implemented in R as of now. However, if you are willing to accept some simplifications, you could perhaps be fine with the existing functionality.
Take, for ...
- 2,726
2
votes
Rare Events in Normal Multivariate distributions
If you’re comfortable making the assumption of multivariate normality (I’m not sure that you are), then this seems like a perfect place to use Mahalanobis distance.
One of the first facts that ...
- 353
2
votes
How to compute a single Value-at-Risk (a single quantile) of portfolio returns taking into account correlation between individual returns?
With a multivariate normal model, the portfolio has a univariate normal distribution (mean and variance are easy), so it reduces to a scaled univariate quantile.
- 240
2
votes
Accepted
INTERPRETING PCA ANALYSIS
IIRC, the signs of the PC are meaningless. +/-'ive doesn't itself tell you anything.
Rather, the cross-sectional, absolute max of the PCs will tell you which one is most important per item (eg: PC6 ...
- 135
2
votes
Accepted
Quasi Random Monte Carlo in m.v. portfolio optimization
...this technique works only when returns are generated from normal distributions?
Yes and no. Multiplying them by $C$ will produce the correlation that you wanted, but it won't preserve the ...
- 1,389
2
votes
Accepted
Multivariate MC: what am I doing wrong?
Based on Quantuple comments (thank you), I fixed many mistakes and I came up with the following code:
...
- 151
2
votes
2
votes
How to hedge a dual digital option
Dispersion trading is a way to mitigate correlation risk. The book "Foreign Exchange Option Pricing A Practitioners Guide" (Chapter 10 Multicurrency Options) introduces an analysis framework....
1
vote
Multivariable objective function optimization similar to optimx in R
Just because others may experience the same problem, here is a short answer to this problem:
To optimize a multi-variate problem with optimx (i.e. more than one parameter is optimized) you can create ...
- 111
1
vote
Multiple Indices for CAPM model
It's not clear what you're trying to accomplish by applying CAPM, or it that a goal in itself?
For example, you could, for each stock in your universe, calculate the historical $\rho$ to each of the ...
- 11.9k
1
vote
Accepted
Why is Banque de France using BVAR with different orders of integration?
So I asked on reddit, and got this answer from Rasseren :
The integration order of the endogenous variables is most often used to ensure reasonable stability (all eigenvalues of the companion form <...
- 11
1
vote
Align volume bars for multivariate analysis
It's not about timestamps. You just need to assign the same meaning to each bar.
Choose a fixed percentage of daily volume each bar should represent. Then for each individual day, compute the bar ...
- 9,797
1
vote
Cega - Correlation Delta from multi-asset derivative
Since the correlation matrix is symetric, if you move the term (i,j), you have to do it for the term (j,i) as well
Of course -> the correlation of an asset with itself is equal to 1... so it should ...
1
vote
How to do QE scheme for n correlated assets?
I am not familiar with the QE scheme, but I think your question is more general: You want to do a multi-variate diffusion, for $n$ correlated processes.
You have your instantaneous correlations ...
- 2,200
1
vote
Accepted
Quanto basket payoff
You should have a correlation matrix with the following 5 parameters:
1. SPX price.
2. SPX variance.
3. SX5E price.
4. SX5E variance.
5. FX rate.
You can even consider that FX rate is not ...
- 852
1
vote
Forecasting default rates using a macroeconomic model
I guess more than multicolinearity you are running into the issue of identification. What are you exactly identifying with such a regression? You somehow need to instrument for defaults. Although your ...
- 8,022
1
vote
Accepted
Understand the white noise condition in Vector Autoregression
I think the mistake is how to define $\ Y_t$. It is supposed to contain endogenous and exogenous variables. Hence, the multivariate white noise in the VAR analysis should full fill the following ...
- 53
1
vote
Fit Simple VAR model in Matlab
I suggest you to organize you explanatory variables in different matrix and then use the mvregress(...) command, that allows you to handle well the results.
I ...
- 23
1
vote
Multivariate normal when Cholesky decomp fails on Sigma
You need to adjust your correlation matrix such that it becomes positive definite.
There is an R routine that will do this for you - link.
Or, if you want to do it yourself, i believe the general ...
- 2,531
1
vote
Package for multivariate Garch Vech model for R?
Try the mgarch package, it's available at CRAN. In this link you will find an example from Prof. Zivot.
1
vote
Accepted
Augmented Dickey-Fuller Test/ Unit Root test on multiple time series dataframe in R
It is not clear from the post if you are querying for the mechanics/code for looping over the series or the appropriate critical values. I here make a comment on the latter.
One of the main pitfalls ...
- 4,307
1
vote
Augmented Dickey-Fuller Test/ Unit Root test on multiple time series dataframe in R
There a to ways that you can performe the ADF test to a data frame, first write a loop for applying the test to all the columns or use the apply function to your data. For leaving out the first column ...
1
vote
Multivariate GARCH in Python
mgarch is a python package for predicting volatility of daily returns in financial markets.
DCC-GARCH(1,1) for multivariate normal and student t.
distribution.
- 11
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