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^...
simzoor's user avatar
  • 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 ...
Richard Hardy's user avatar
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 &...
LocalVolatility's user avatar
3 votes

Multivariate GARCH in Python

I recently met the same problem and found a way to achieve it using R in Python. ...
Bowen Cao's user avatar
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-...
Richard Hardy's user avatar
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 ...
Richard Hardy's user avatar
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 ...
Dave's user avatar
  • 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.
userid is i's user avatar
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 ...
oliversm's user avatar
  • 1,389
2 votes

Simulating from a multivariate clayton copula

Clayton Copula-Matlab Code ...
Frankova T's user avatar
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 ...
jason m's user avatar
  • 135
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: ...
akasolace's user avatar
  • 151
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....
Chiu-Tzu-Hsuan's user avatar
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 ...
JKupzig's user avatar
  • 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 ...
Dimitri Vulis's user avatar
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 <...
Jur's user avatar
  • 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 ...
chrisaycock's user avatar
  • 9,817
1 vote

Manually calculating and backtesting VaR and CVaR from DCC-GARCH R

Apparently, the VaR arguments are simply ignored by the function. If you try running something absurd, such as specifying that calculate.VaR = 'banana', the function still runs. The object has the ...
Tamir Einhorn Salem's user avatar
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 ...
Appel Moi Dougy's user avatar
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 ...
byouness's user avatar
  • 2,210
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 ...
Valometrics.com's user avatar
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 ...
phdstudent's user avatar
  • 8,306
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 ...
Nord1's user avatar
  • 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 ...
Dave92's user avatar
  • 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 ...
will's user avatar
  • 2,571
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.
aureliano.bressan's user avatar
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.
Faisal Nawaz's user avatar

Only top scored, non community-wiki answers of a minimum length are eligible