All Questions
Tagged with correlation-matrix covariance-matrix
15 questions
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N asset covariance matrix vs N-1 asset covariance matrix
so I have been using a M-V framework to form M-V efficient portfolios. I have noticed that every time I make my investment universe smaller the minimum variance frontier moves to the right. This ...
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0
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124
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Sub-portfolio correlation
I am trying to reduce correlation matrices into sub portfolios.
For example, I have a covariance matrix $\Sigma$ and weight-vector $w$ of two line items which I blend together into a sub-portfolio $\...
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1
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122
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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 ...
1
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0
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82
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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,...
- 3,090
-2
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1
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332
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Should portfolios have zero or negative correlation between assets? [closed]
Is it more optimal to have a portfolio whose assets are negatively
correlated? (I am not requiring all assets to be negatively correlated in this case, nor (-1) perfectly negative correlation either. ...
- 3,090
1
vote
1
answer
159
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Double objective in portfolio optimization
Is there anything infeasible or ethically wrong about optimizing portfolios like this?
$$\min_w \enspace w' \Sigma w + w' C w$$
where $\Sigma$ is the asset return covariance matrix, and $C$ is the ...
- 3,090
1
vote
2
answers
362
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Which is more ill-conditioned, the asset correlation matrix or covariance matrix?
If i have a matrix of multivariate asset returns for $N$ stocks, and i compute from it the covariance matrix and then the correlation matrix, can I always know which of the two will have the higher ...
- 3,090
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120
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Covariance of Individual Return and Portfolio Return
Hi guys,
Is it possible to get the covariance between the individual return and portfolio return given the correlation matrix, volatility matrix, weights matrix and return matrix?
I know how to get ...
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0
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1k
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Computing covariance matrix with historical data
I have been reading Active Portfolio Management by Grinold and Khan. In the chapter about risk, they mention,
"The third elementary model relies on historical variances and covariances. This ...
- 187
2
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0
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3k
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Barra covariance matrix construction
I am trying to replicate the covariance matrix used by Barra risk models.
All Barra models have half life parameters for volatilities and correlations (e.g. if the half life for volatlity is 90 days, ...
- 1,910
0
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1
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513
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Average Correlation
We're given a spreadsheet with a correlation matrix for four stocks.
Then there is a calculation for average correlation, but I don't know how it's derived.
$$=\left(\operatorname{Average}(C14:F17)-\...
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2
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2k
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Correlation Between 2 Portfolios
I have a set of assets, n. I'm trying to find the correlation between 2 portfolios, say x and y, where x is nested in, or, a sub-set of y. That is, x is a portfolio based on a sub-set of n, while y is ...
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PCA on term structure of interest rates
Interest rate time series seems to be non-stationary whenever test is performed
But covariance or correlation matrix is derived from term structure time series which are non stationary and later PCA ...
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4
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2
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6k
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Does one use the covariance or correlation matrix in cholesky decomposition to generate correlated samples
Can we interchangeably use Cholesky decomposition of covariance and correlation matrix to generate simulations? If not, in which situations do we use one or the other and why?
Thanks in advance.
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4
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How to treat large (5K-10K) non-positive-definite (particularly near-singular) covariance matrices for Cholesky decomposition?
I have a very large covariance matrix (around 10000x10000) of returns, which is constructed using a sample size of 1000 for 10000 variables. My goal is to perform a (good-looking) Cholesky ...
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