9 votes
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Copulas simply explained

I found Coping With Copulas by Thorsten Schmidt really helped me to get a more basic understanding of copulas. As well as looking at some simple examples in R and thinking about different directions ...
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  • 206
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 ...
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  • 4,217
7 votes

Copulas simply explained

In the theory of copulas you want to model a multivariate (often bivariate) distribution and keep the marginals fixed. Thus you have random variables $X$ and $Y$ with cdf $F_X(x) = P[X \le x]$ and $...
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  • 13.3k
7 votes

Copulas simply explained

The best introduction to copulas I know, i.e. with rigour and intuition, is the following. THE QUANT CLASSROOM BY ATTILIO MEUCCI A Short, Comprehensive, Practical Guide to Copulas Visually ...
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  • 26.9k
5 votes
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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^...
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  • 363
4 votes

Mutivariate t markets

A multivariate normal distribution can be thought of as normal margins with a normal copula. The multivariate t is the same way, but it has t margins with a t copula and they all have the same degrees ...
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  • 5,311
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 ...
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  • 315
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 &...
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3 votes
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Multivariate GARCH in Python

PYTHON I have found this class from the statsmodels library for calculating Garch models. Unfortunately, I have not seen MGARCH class/library. Below you can see the basic information about the garch ...
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3 votes

Any package to run VAR-GARCH or VECM-GARCH models in R?

Yes, it exists and it is called ccgarch package. You can install that by simply running in R install.packages("ccgarch") and ...
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  • 2,438
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 ...
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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-...
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2 votes

Copulas simply explained

There is a brief and not overly technical introduction here: http://prescientmuse.blogspot.co.uk/2015/01/a-brief-introduction-to-copula.html And an application of use in a trading system with full R ...
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  • 184
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 ...
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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.
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2 votes

Simulating from a multivariate clayton copula

Clayton Copula-Matlab Code ...
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2 votes
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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 ...
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  • 135
2 votes
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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 ...
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  • 1,359
2 votes
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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: ...
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  • 151
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 ...
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1 vote

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 ...
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  • 161
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 ...
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  • 6,830
1 vote
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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 ...
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  • 53
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 ...
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  • 2,110
1 vote
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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 ...
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  • 4,217
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 ...
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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 ...
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  • 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 ...
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  • 2,406
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.
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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.
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