Questions tagged [covariance-estimation]
The covariance-estimation tag has no usage guidance.
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Calculating Portfolios Covariance via Bilinearity with Log or Simple Returns
I'm wanting to calculate the covariance between two portfolios $A$ and $B$ which are allocated to assets $X_i$ (where $i \in \left[1, 2, \cdots, N \right]$) with weights $\vec{w_A}$ and $\vec{w_B}$, ...
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Estimating covariance with intraday data
I have intraday (30 min) data for a number of stocks, and I would like to calculate the covariance matrix of returns.
For the purpose of calculating the covariance matrix, is it better/more correct to ...
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Bias-Variance tradeoff for Covariance Estimation w/ Different Frequencies
In general, what does the bias-variance tradeoff look like when estimating covariance matrices with varying return frequencies (i.e. daily, weekly, monthly returns)?
From my observations I've noticed ...
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What does a non-stochastic limiting shrinkage function mean?
I'm reading the paper "The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation" by Ledoit and Wolf (2020). When a function that is used to transform the ...
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Sample Variance of Portfolio
Let $w$ denote a vector of portfolio weights, $r_i$ denote the $i$th return vector, $\Sigma$ denote the Covariance matrix of $r_i$ and let $\hat{\Sigma}$ denote the sample covariance matrix of $r_i$.
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Shrinkage of the Sample Covariance matrix, theory
is there any theory behind the covariance matrix shrinkage paper, why it works?
I am talking about this stats exchange thread
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395
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Number of Observations for Non-Singular Covariance Matrix Estimation
Marcos López de Prado writes the following in his book Advances in Financial Machine Learning:
In general, we need at least \frac{1}{2} N (N+1) independent and ...
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Implementing Hierarchical PCA for financial time series in R
I would like to implement the method "Hierarchical PCA", as described in the following paper and compare it to a "standard" PCA. I like to do this in R
AVELLANEDA, Marco. ...
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Odd Result from Computing Correlation Matrix from Kalman Filter Posteriori Covariance Estimate
I am using a Kalman Filter to estimate the return dynamics of a forwards curve on a particular commodity. My state space is the initial forwards values, and an initial guess of the drift functions for ...
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Can the covariance matrix be represented as a scalar or something similarly small, instead of a large pair-wise grid?
The covariance matrix tabulates pair-wise interactions between variables (assets) one-at-a-time into a grid, which can quickly become large as the number of assets included in a portfolio, for example,...
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Update sample covariance matrix
I would like to update a covariance matrix $\mathbf{R}_T$ with a new incoming sample at time $T+1$, i.e. I would like a rank-1 update of the form $\frac{1}{T+1} [T \mathbf{R}_T + \mathbf{x}_{T+1}\...
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Meaning of an identity matrix for the covariance in portfolio optimization
Instead of using a sample covariance matrix for portfolio optimization, Ledoit and Wolf use an estimator that is the weighted average of the sample covariance matrix and the identity matrix, $I$. This ...
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Covariance Shrinkage in Black-Litterman Framework
Good evening guys
I am looking into the effects of covariance shrinkage on the diversification of asset weights for different portfolio optimisations. Initially, I was interested to see how it affects ...
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Ledoit/Wolf covariance shrinkage in risk-parity optimisation
This is more of a theoretical question.
I have been working on some mean-variance / Black-Litterman models and played around with Ledoit/Wolf's covariance shrinkage method (sklearn function in Python)....
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Effective Time Length of Exponentially Weighted Covariance Matrix Estimate
In [1] Pafka, Potters and Kondor mention the following in section 2:
In contrast, if this covariance matrix estimate is used for portfolio optimization (i.e.
for selecting the portfolio in a ...
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Risk-Neutral covariance matrix of arbitrage-free Nelson Siegel
For my thesis on a Bayesian sampling routine for a modification on arbitrage-free Nelson-Siegel I came across an equation that involves a matrix exponential within an integral, i.e.
$\int_{0}^{\Delta ...
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Creating a Covariance Matrix
Lets say that you have the correlation of x,y and you have the standard deviations of x and y , how would you then find the covariance of x,y using the correlation of x,y and and the standard ...
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Is a more robust Covariance estimation possible?
I'm working on a mean-variance optimization problem, but instead of financial securities I'm choosing a 'portfolio' of N athletes. It is a 1-period optimization problem over one generic statistic ...
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Correlation coefficient without cash flows?
I'm an intern at a company and one of our tasks is to calculate the the probability of default of both participants of a Swap(a Client and a Bank), for which we first need the correlation coefficient ...
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Misunderstanding of time series autocovariance
I'm reading the "Time Series: Theory and Methods (2nd ed.)" by P.J.Brockwell and R.A.Davis. I've stopped at the one moment at pp.218-219 (Chapter 7 "Estimation of the mean and the Autocovariance ...
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Estimating an GARCH(1,1) model? Long hand method
I am really trying to invest some time to estimate a GARCH(1,1) method, I know there is many statistical packages that will do this for me (Eviews, MATLAB, R), but I am trying to do this by hand, so ...
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Is Ledoit-Wolf Shrinkage with a Constant Correlation Prior Reasonable for a Stock/Bond Mix?
I've been looking into Ledoit-Wolf shrinkage but I've found the papers concentrate on large numbers of assets that tend to all be highly correlated. Often a universe of large cap stocks.
I'm ...
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Shrink covariance or correlation matrix
Is it preferable to shrink the covariance matrix vs the correlation matrix? Technically this amounts to either shrinking the sample correlation matrix and then transforming the shrunk correlation ...
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Black Litterman - numerical instability
I am trying to work out the formula for the posterior mean in Black Litterman's model assuming 100% confidence :
Ref: https://corporate.morningstar.com/ib/documents/MethodologyDocuments/IBBAssociates/...
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Portfolio Optimisation/Covariance Estimation on a large scale
When using Markowitz Portfolio Theory, e.g. for finding an optimal portfolio composition, one needs to have estimates of the returns, but most importantly of the covariance matrix. If our universe of ...
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What is special about covariance estimation from statistical factor models?
If you were to compare the usual sample covariance estimate to a robust covariance estimate (such as MCD), you can say that the robust estimate is more tolerant to outliers in the data and will not be ...
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Fourier transform covariance estimator
I am estimating realized variance and covariance by the estimator described in this paper, and relying on Fourier Transform.
Now, as my data is one day of data in ultra high frequency, so that the ...
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OHLC Covarianc Estimation
Is there an R package which can estimate a covariance matrix using OHLC (Open/High/Low/Close) share prices for upwards of 40 shares using the Yang & Zhang method using daily data? I google ...
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Implementation of Ledoit Wolf shrinkage estimator within R package tawny
I want to implement the shrinkage intensity given by Ledoit and Wolf, see here page 13. They define $y_{it}$ with $1\le i\le N$ and $1\le t\le t$ be the return on stock $i$ at time $t$. Moreover, $z_i:...
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Multivariate GARCH in Python
Is there a package to run simplified multivariate GARCH models in Python? I found the Arch package but that seems to work on only univariate models. I'd like to test out some of the more simple ...
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MLE estimate of normal distribution
Probably a naive question. I am quoting this from Greene's econometrics book:
"The occasional statement that the properties of the MLE are only optimal in large samples is not true, however. It can ...
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Bayes Stein Porfolio Implementation
From this paper from Jorion.
Has anyone implemented this? How is the Covariance matrix estimated? It needs to estimate also the conditional distribution of the returns?
Best
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How to get Multivariate Betas from an Estimated EWMA co variance Matrix?
I have a portfolio of 4 assets. I also have returns for 3 indices. I want to get the multivariate betas for these 4 assets-based on these assets. I only have the 7 x 7 covariance matrix estimated by a ...
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Portfolio Optimization : Shrinkage of Covariance Matrix when data is available
It seems that shrinking the covariance matrix is especially useful if the number of individual stocks is greater than the number of data points. However is there any special gain if you're not ...
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Explanation or implementation of Ledoit-Wolf estimator (without math packages)
I have calculated weights of selected assets in a market-neutral portfolio (presumably with min variance) using PCA and simple data covariance matrix.
The question is :
It is obvious that Cov Matrix ...
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Step-by-Step PCA algorithm (checking correctness without math packages)
I would appreciate if someone could correct me if i am wrong in my suggestion.
I am using PCA to :
find measure of cointegration between selected assets
find the eigenvector and its portfolio with ...
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Shrinkage Estimator for Newey-West Covariance Matrix
I like to apply the Newey-West covariance estimator for portfolio optmization which is given by
$$
\Sigma = \Sigma(0) + \frac12 \left (\Sigma(1) + \Sigma(1)^T \right),
$$
where $\Sigma(i)$ is the lag ...
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Covariance estimation: shrinkage, random matrix theory, what else?
Shrinkage was much en-vogue before random matrix theory (RMT) took everybody's attention in covariance matrix estimation, however the latter also showed its limits. A plethora of other estimators has ...
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