# Tag Info

16

it doesn't require normality. What it requires is that the investor's decisions are determined by mean and variance. A normal distribution is determined by mean and variance, so if you assume joint normality then there is no point in the investor being interested in anything else. (we try to discuss assumptions thoroughly in our book, Introduction to ...

11

This is indeed an interesting question. According to this website, a paper by Goldman Sachs [Tierens and Anadu (2004)] proposes three alternative methods for estimating average stock correlations: Calculate a full correlation matrix, weighting its elements in line with the weight of the corresponding stocks in the portfolio/index, and excluding ...

8

I just want to add to vonjd's answer some info on the comparison of the 3 methods. This is too big for a comment so I'm posting as a separate answer but please upvote his answer, not mine. Do the differences in methodologies matter in practice? To gauge the practical importance of the biases in methods 2 and 3, we calculate the weighted stock correlation ...

8

This is the result of the Sherman-Morrison inversion for the sum of an invertible matrix and an outer product. You will find this (and many other helpful methods) in the Matrix Cookbook. Specifically, this is equation 160 on p 18: $$\left(\boldsymbol{A}+\boldsymbol{bc}^T\right)^{-1}=\boldsymbol{A}^{-1}-\frac{\boldsymbol{A}^{-1}\boldsymbol{bc}^T\boldsymbol{A}... 8 If your two assets are denoted by random variables X_1, X_2, with 2x2 covariance matrix \mathbf{Q} and the portfolios:$$ Z_1 = w_{11} X_1 + w_{12} X_2  Z_2 = w_{21} X_1 + w_{22} X_2 $$Then, Cov(Z_1, X_1) = w_{11}Cov(X_1,X_1) + w_{12} Cov(X_2, X_1) , etc. In matrix algebra:$$ \mathbf{Z} = \mathbf{W} \mathbf{X}$$The 4x4 covariance matrix, is: ... 7 To supplement the other answer, yes there are optimization reasons for the covariance matrix being symmetric positive definite (SPD). All positive definite matrices are invertible and its inverse is also positive definite. This guarantees a unique global minimum in a quadratic optimization problem (MVO). Lots of material available on the topic: https://www.... 6 What you basically do here is a Principal Component Analysis (PCA). A good starting point in the financial sphere is Managing Diversification by Attilio Meucci (2010) Page 3: "The most natural choice of uncorrelated risk sources is provided by the principal component decomposition of the returns covariance [...] The eigenvectors define a set of N ... 6 Let s be a N\times1 vector of standard deviations and C be an N\times N correlation matrix. The covariance matrix is equal to$$\Sigma=\text{diag}(s) \ C \ \text{diag}(s)$$where \text{diag}(x) is a function that takes an N\times1 vector and puts it on the diagonal of a N\times N matrix. If you get some better standard deviation estimates, ... 6 Suppose the covariance matrix is V (which is n by n) and the weights are w (of length n). Then the Portfolio Variance is V_p = w^T V w and the Risk Contribution (in terms of variance) of asset k is RC_k=w_k \sum_j V[k,j]w_j in words this is "the weight of asset k times the inner product of the k-th row of V and the weight vector". (Sometimes ... 6 What does 'simulate a covariance matrix' mean? If the question means, generate an arbitrary correlation matrix for 1000 stocks, then we can choose any symmetric matrix with all 1s down the diagonal, so long as every element is between -1 and 1 and the matrix is positive semi-definite. The large size of the matrix means that putting random values in every ... 6 Positive definite matrix A is defined as x^TAx > 0 for all vectors x. Since a term w^T\Sigma w in Markowitz (and other models as well) expresses variance in returns, it is a measure of dispersion. Any measure of dispersion has to be positive (or maybe zero but it is a case where there is no uncertainty and hence no risk). Negative dispersion is ... 5 We have weights w_A, w_B and w_C = 1 - w_A - w_B that sum to 1. With de-meaned returns r_A, r_B, and r_C, the portfolio variance is$$E\{[w_A r_A + w_B r_B + (1 - w_A - w_B)r_C]^2 \} = w_A^2\sigma_A^2 + w_B^2\sigma_B^2 + 2 w_A w_B\rho_{AB}\sigma_A \sigma_B,$$assuming the cash volatility \sigma_C is zero. 5 Multivariate volatility models for replacing the sample covariance matrix with in the mean-variance portfolio selection model: RiskMetrics 1996 EWMA (Exponentially weighted moving average) covariance matrix RiskMetrics 2006 EWMA covariance matrix Multivariate DCC-GARCH covariance matrix Jon Danielsson "Financial risk forecasting" has EWMA and GARCH for R ... 5 here is how to get covariance matrix from correlations: 5 If you assume that a financial asset price has a change that is a wiener process then you can view the future value of that asset as the initial value plus the sum of the independent daily changes (for equity or returns based then you would need log version of this):$$ S_t = S_0 + \sum \Delta S_i  where $\Delta S_i = S_i - S_{i-1}$ is a wiener process. ...

5

I'm not a Python programmer, however, reading the reference manual of np.var, you're using the "biased" version of the variance estimator. Instead use the unbiased variance estimator: import numpy as np from numpy.random import randn X = randn(1000,3) Sigma = np.cov(X.T) w = np.array([0.2,0.3,0.5]) print(np.var(X@w, ddof=1)) ...

4

For a swap, we have a sequence of re-setting and payment dates. The # of forward rates corresponding to the # of payment dates. For example, let us assume that we have $n$ payment dates $t_1, \ldots, t_n$, where $0< t_1 < \cdots < t_n$. Then there are $n$ forward rates. During the simulation, for time steps prior to $t_1$, there exist $n$ "...

4

It depends on your investment process: more specifically, on how you generate views. Here are three practical cases which lead to different choices for $\Omega$: Let's assume you are an investor who acts on (more or less) arbitrary bits of opinion: e.g. you like Italian equities because you like Italy, and German equities because you find Angela Merkel's ...

4

One way to this is the following (you can code all these constraints if you use the right software, I am doing such things using mathematica) You define $w_{i,j}$ which is the weight of asset $j$ in subportfolio $i$, furthermore you define $w =(w_j)_{j=1}^{\text{no of assets}}$ the total weight of the portfolio in asset $j$. the objects for the optimization ...

4

He is forced to use some tricks because Excel can only take average of a rectangular area, but he wants the avg of upper non-diagonal elements of the matrix only. So he subtracts $\frac{1}{n}$ (the average of the 1's on the diagonal), then scales the result by $\frac{n^2}{n(n+1)/2}$ which is the number of total elements divided by on-or-below-diagonal ...

4

This is a common problem in covariance matrix estimation, with several possible solutions. One of the simplest involves two steps: (1) You compute each element of the covariance matrix on a 'best efforts' basis, meaning you take the covariance of the two time series involved after REMOVING any data pairs having a N/A value. (Note that this means each ...

4

EDITED You are right. We have to look town to the "leaves" in each iteration. I would do it the following way: If $L_i^{(j)}$ is the set of indices in the $j$ branch ($j \in \{1,2\}$), then we define $s_i^{(j)}=\sum_{n \in L_i^{(j)}w_n}$, the weight of the branch before scaling and $n_i^{(j)}=\left|L_i^{(j)}\right|$ the number of leaves in the branch. ...

4

Just to expand on Alex answer. Empirically it is simply not true. Focusing on the diagonal of the variance-covariance matrix, we know that there is a large variance risk premium. Take a look at table 3 from Carr and Wu (2009). Regarding covariances we do not have much evidence, because there are no options on every single pair of stocks. However, we do know ...

4

There is nothing wrong mathematically (nor ethically) with this objective function. However, this objective is weird in a couple of ways. First, there is no weighting on these which implies you prefer to minimize these terms in accordance with their orders of magnitude. As has been pointed out, the correlation term is likely much larger so your optimization ...

4

This turns out to be a general drawback of the HRP algorithm, as pointed out by Pfitzinger, J., & Katzke, N. (2019) (my highlights): As shown in Figure 2.3, the naive bisection rule can violate the intuitive character of the result, by placing similar assets into separate clusters for allocation purposes. While centered bisection yields a symmetric ...

3

I thought I would answer the question of "what am I using." All shrinkage estimators map to a Bayesian estimator that differs only in the prior distributions. In other words, you get a point estimate that is indistinguishable from a Bayesian estimate except that the calculation rule determines the prior distribution. Stein estimators for the Gaussian are ...

3

one solution that works is set up the usual correlation matrix and pre- and post multiply by a diagonal matrix with semi standard deviations down the diagonal taking care that they are not zero

3

In theory, the Ledoit and Wolf shrinkage estimator is supposed to guarantee a positive-definite matrix, given that it adds a positive-definite matrix (the target) to a semi-positive one (the sample covariance). I can see four reasons why you didn't get a positive-definite matrix: Your true covariance is effectively not full rank, i..e you have perfect ...

3

The problem with Ledoit-Wolf is that it's very sensitive to outliers. You should try these: DCC GARCH unfortunately, not available in Python Exponentially weighed moving average (EWMA) gives slighly worse results than DCC-GARCH Minimum Covariance Determinant suggestted by Scikit-Learn bootstrap could be used to calculate confidence interval, then ...

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