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 ...
- 3,166
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
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 &...
- 6,064
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
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.
- 238
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 ...
- 1,399
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
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
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 ...
- 12.6k
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 ...
- 9,877
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 ...
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 ...
- 2,230
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 ...
- 872
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 ...
- 8,561
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 ...
- 2,591
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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