# Tag Info

7

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 introducing a powerful risk management tool to generalize and stress-test correlations

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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 squared error and I get the constant by matching the series variance. My experience is that there is no point pretending to finetune parameters when vol is ...

6

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 the transformations can happen. To answer your actual question I'll attempt to describe the steps involved as simply as I can. Let's say you use the copula ...

6

High and low prices are frequently used in many contexts, such as estimating volatility. See, for example, the Garman-Klass and Yang-Zhang estimators. Brandt and Kinlay provide a nice summary of some of these estimators. However, it sounds like you are more interested in using high/low information for evaluating whether mean reversion has taken place. In ...

5

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 $F_Y(y) = P[Y\le y]$ and you want to find some $F_{X,Y}(x,y) = P[X \le x, Y\le y]$ such that when you look at marginals you get $F_{X,Y}(x,\infty) = F_X(x)$ and ...

4

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 of freedom. So it has t copula dependence. It is either a spherical or an elliptical distribution. I can't think of a good reason to use a multivariate t. The ...

4

Since pentahedrons are 3d shapes, but there is no reason to think currencies live in a 3d world, you can just treat the 'pentahedron' as a weighted node graph of the 5 currencies. A graph edge from one currency to another represents an exchange of those two currencies. So in the same fashion as usual vectors, I can go from currency A to C via B by executing ...

3

For the two-dimensional case, the Cholesky decomposition of the covariance matrix $$\Sigma = \left( \begin{array}{c c} \sigma_1^2 & \rho \sigma_1 \sigma_2\\ \rho \sigma_1 \sigma_2 & \sigma_2^2 \end{array} \right)$$ is given by B = \left( \begin{array}{c c} \sigma_1 & 0\\ \rho \sigma_2 & \sigma_2 ...

3

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 Simulation (FHS), etc.). 1 - EGARCH, merely because GARCH models do not take into account so-called leverage effects, which is crucial to me for skewed and ...

3

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 models in mentioned class from the statsmodels. Probably you have to implement it by your own in python, so this class might be used as a starting point. ...

3

Yes, it exists and it is called ccgarch package. You can install that by simply running in R install.packages("ccgarch") and learn more about that on the CRAN relative paper. Moreover, I suggest you to read this lecture hold by the author during an R conference. Hope this help.

3

Yes you can, how depends fully on your required accuracy and also whether PC1 and PC2 are sufficient in explanatory power of the log differences of your futures contract. Also, make sure you understand the signs of the eigenvalues (sign of the PC) can be different from one experiment to the next as they are arbitrary (the values are obviously not). Here ...

2

Why not fit an ARMA model to the rolling returns first, and then model the residuals in your regression equation? That way you should be removing most of the effects of auto-correlation.

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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 code here: http://prescientmuse.blogspot.co.uk/2015/02/vanilla-trading-algorithm.html Hope that helps!

2

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 example, the "rmgarch" package in R. It allows combining univariate conditional mean-conditional variance models with several multivariate GARCH models that take ...

2

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 $F^{-1}$. Then, Generate $n$ independent r.v. from $U (0,1)$ Calculate $n-1$ derivatives of $F$, where $F_{n-1}$ denotes the $n-1$-th - order derivative of $F$ ...

1

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 $R^2$ is high, does your regression make any sense? Take for example the coefficient on unemployment. It is negative, so that seems to imply that higher ...

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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 matrix $R = (\rho_{i,j})_{i,j}$ where $d \langle W^i, W^j \rangle_t = \rho_{i,j} dt$, and I am assuming here you know how to simulate brownian increments for a ...

1

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 tried in the past to use pre-built command for VAR but I find way simpler to organize it by myself and use usual regression commands.

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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 method is to do an eigen value decomposition, set any negative eigenvalues to zero, and then reconstruct the original matrix. If you're going to go down this ...

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Try the mgarch package, it's available at CRAN. In this link you will find an example from Prof. Zivot.

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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 when testing multiple hypotheses is the fact that a certain percentage would fail under the null (as this xkcd strip nicely illustrates https://xkcd.com/882/ ). ...

1

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 just create an other data frame like this: da=yourDataName[,-1]. the code for the ADF would be something like apply(da,2,adfTest,lags=0,type="c"). The 2 is ...

1

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-GARCH model reveal that the model admits a different form than the one represented in the equation in your question. (What you have there is a special case of a ...

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Your question is formulated in a very general way, this is why any answer will need to be general as well. In a nutshell and in full generality you need to estimate the joint distribution from your historical data since in most cases correlations alone are not sufficient to define the joint distribution. In a second step you can calculate the distribution ...

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I found a link and I have to repeat: I don't think that PCA helps you to find a price ... it helps to model the movements of prices but not their values. You get something like a factor model ... this does not directly give you a price ... maybe you also want to have a look at this link where PCA is applied to the oil market.

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Try fitting a model with ARMA errors? However, if by "rolling returns" you imply a moving average of returns or some QoQ or YoY return series, which has much persistence, I am not so sure what the right way to proceed really is (with the exception that you can apply some corrections suggested in econometrics literature).

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It simply points to the fact that your model as stands does not have much explanatory power of monthly returns. One reason could be of a observation period mismatch. I am not a fundamental type of guy, but I imagine that the monthly returns are measured over too short a period (1 month) while most fundamental factors are updated on a quarterly basis (...

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