Correlation and correlation structure (3), estimate tail dependence using...
What is tail dependence really? Say the market had a red day and saw a drawdown which belongs with the 5% worst days (from now on simply call it a drawdown): One can ask what is now, given that the...
View ArticleMultivariate volatility forecasting (5), Orthogonal GARCH
In multivariate volatility forecasting (4), we saw how to create a covariance matrix which is driven by few principal components, rather than a complete set of tickers. The advantages of using such...
View ArticleMultivariate volatility forecasting, part 6 – sparse estimation
First things first. What do we mean by sparse estimation? Sparse – thinly scattered or distributed; not thick or dense. In our context, the term ‘sparse’ is installed in the intersection between...
View ArticleThe case for Regime-Switching GARCH
GARCH models are very responsive in the sense that they allow the fit of the model to adjust rather quickly with incoming observations. However, this adjustment depends on the parameters of the model,...
View ArticleMultivariate Volatility Forecast Evaluation
The evaluation of volatility models is gracefully complicated by the fact that, unlike other time series, even the realization is not observable. Two researchers would never disagree about what was...
View ArticleCurse of dimensionality part 3: Higher-Order Comoments
Higher moments such as Skewness and Kurtosis are not as explored as they should be. These moments are crucial for managing portfolio risk. At least as important as volatility, if not more. Skewness...
View ArticleMultivariate volatility forecasting, part 2 – equicorrelation
Last time we showed how to estimate a CCC and DCC volatility model. Here I describe an advancement labored by Engle and Kelly (2012) bearing the name: Dynamic equicorrelation. The idea is nice and the...
View ArticleCorrelation and correlation structure (2), copulas
This post is about copulas and heavy tails. In a previous post we discussed the concept of correlation structure. The aim is to characterize the correlation across the distribution. Prior to the global...
View ArticleMultivariate volatility forecasting (3), Exponentially weighted model
Broadly speaking, complex models can achieve great predictive accuracy. Nonetheless, a winner in a kaggle competition is required only to attach a code for the replication of the winning result. She is...
View ArticleMultivariate volatility forecasting (4), factor models
To be instructive, I always use very few tickers to describe how a method works (and this tutorial is no different). Most of the time is spent on methods that we can easily scale up. Even if...
View ArticleCorrelation and correlation structure (3), estimate tail dependence using...
What is tail dependence really? Say the market had a red day and saw a drawdown which belongs with the 5% worst days (from now on simply call it a drawdown): One can ask what is now, given that the...
View ArticleMultivariate volatility forecasting (5), Orthogonal GARCH
In multivariate volatility forecasting (4), we saw how to create a covariance matrix which is driven by few principal components, rather than a complete set of tickers. The advantages of using such...
View ArticleMultivariate volatility forecasting, part 6 – sparse estimation
First things first. What do we mean by sparse estimation? Sparse – thinly scattered or distributed; not thick or dense. In our context, the term ‘sparse’ is installed in the intersection between...
View ArticleThe case for Regime-Switching GARCH
GARCH models are very responsive in the sense that they allow the fit of the model to adjust rather quickly with incoming observations. However, this adjustment depends on the parameters of the model,...
View ArticleMultivariate Volatility Forecast Evaluation
The evaluation of volatility models is gracefully complicated by the fact that, unlike other time series, even the realization is not observable. Two researchers would never disagree about what was...
View ArticleClik here to view.
Create own Recession Indicator using Mixture Models
Context Broadly speaking, we can classify financial markets conditions into two categories: Bull and Bear. The first is a “todo bien” market, tranquil and generally upward sloping. The second describes...
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Portfolio Construction Tilting towards Higher Moments
When you build your portfolio you must decide what is your risk profile. A pension fund’s risk profile is different than that of a hedge fund, which is different than that of a family office....
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Multivariate Volatility Forecast Evaluation
The evaluation of volatility models is gracefully complicated by the fact that, unlike other time series, even the realization is not observable. Two researchers would never disagree about what was...
View ArticleA New Parameterization of Correlation Matrices
In volatility modelling, a typical challenge is to keep the covariance matrix estimate valid, meaning (1) symmetric and (2) positive semi definite*. A new paper published in Econometrica (citing from...
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Correlation and Correlation Structure (6) – Distance Correlation
While linear correlation (aka Pearson correlation) is by far the most common type of dependence measure there are few arguably better ways to characterize\estimate the degree of dependence between...
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