19
Markowitz's concepts attracted a great deal of interest from theorists (and still do), but never had much application in practice. The results from practical application were always disappointing (starting in the 1970's, well before DeMiguel, Garlappi, and Uppal (2007) study of $\frac{1}{N}$ portfolios), mainly because it is so difficult to provide accurate ...
12
I am a professor of finance who has spent his life working in the capital markets in operations, sales, compliance, and research. I would love to tell you about the existence of industry standards, but they do not exist. There is little improvement in the state of the art since the 1970's.
As a disclosure note, I am a strong critic of mean-variance ...
10
Have a look at this classic paper:
Honey, I Shrunk the Sample Covariance Matrix by O. Ledoit and M. Wolf
The abstract answers your question already:
The central message of this article is that no one should use the
sample covariance matrix for portfolio optimization. It is subject to
estimation error of the kind most likely to perturb a mean-...
10
You seem to have two distinct problems:
How to generate random portfolios
How optimal portfolios are structured
Ad 1)
A straightforward way to simulate the weights of random portfolios is to use the Dirichlet distribution $Dir(\alpha_1,\ldots,\alpha_n)$. This is a distribution on the Simplex (i.e. on $S=\{x\in\mathbb{R}^n | \sum x_i =1, x_i\geq 0\}$, ...
9
If $Q$ is your covariance matrix, and $r$ is a vector of your expected returns, then the maximum Sharpe ratio is given by the following math program.
$${\rm maximize} \frac{r^t x}{\sqrt{0.5 x^t Q x}}$$
subject to
$$ 1^t x = m$$
$$ x \in \{0,1\}^n$$
Where $x$ is a vector of indicators of which of the $n$ assets are part of the $m$ selected assets. While the ...
9
There has been a split in the community ever since Mandelbrot published his paper "On the Variation of Certain Speculative Prices."
See:
Mandelbrot, B. (1963). The variation of certain speculative prices.
The Journal of Business, 36(4):394–419.
To understand why this is so important, you must first realize what economists are trying to do. When you ...
9
The underlying problem: your ACTR constraints aren't convex
The $i$th constraint on your risk contribution can be written:
$$ w_i \sum_j \sigma_{ij} w_j \leq c_i s$$
And this isn't a convex constraint because of the $w_j w_i$ terms (a function $g(x,y)=xy$ isn't convex in $x$ and $y$). They're not convex constraints, so you won't be able to write them as ...
8
The estimation of a covariance matrix is unstable unless the number of historical observations $T$ is greater than the number of securities $N$ (5000 in your example). Consider that 10 years of data represents only 120 monthly observations and about 2500 daily observations.
Depending on the application, using data dating farther back than 10 years may be ...
8
Of course, optimal control is at the core of math finance. Take few applications:
Option Pricing: you have an exposure to a time dependent combination of market factors; you have some knowledge of their dynamics. They are partly deterministic, partly stochastic (i.e. random). At each "time step" you can adjust your portfolio at a given cost. Your goal is to ...
8
It is more complicated than that: It is not the optimization per se that leads to inferior results but the data you use.
Kritzman et al. makes a strong case in defense of optimization vs. 1/N in this popular paper:
In Defense of Optimization: The Fallacy of 1/N, Financial Analysts Journal, Vol. 66, No. 2, 2010 by Mark Kritzman, Sebastien Page and David ...
8
There's no easy answer to your question, as noob2 pointed out. You can look online for info from Universa. That fund does exactly what you are asking: https://www.universa.net/riskmitigation.html Of course, post a crash, such as the one we just experienced, the cost of hedges is larger than it is prior to such events.
Understand that you aren't going ...
7
The Kelly criterion is just one approach to portfolio construction (or bet sizing) that considers the risk-return tradeoff. There are many possible strategies (static or dynamic) that incorporate other criteria such as the maximum drawdown, probability of ruin, etc.
As pointed out by @John, Kelly is maximizing the log of wealth, which is equivalent to ...
7
Well there are two main things to consider here.
Many implementation of Black-Litterman use the market portfolio and the ex post volatility and correlation structure to back out implied returns to use as prior. As far as I know, there is no standard way to reverse-engineer the optimization problem in the presence of nonnormal markets. (the first guess is ...
6
Without the discrete constraints, the minimum tracking error/variance problem is a quadratic program. If you constrain the tracking error, you have a convex quadratically-constrained problem which is solved as an SOCP by modern commercial solvers. SOCP does not address discrete constraints like cardinality of assets or minimum investment levels. SOCP ...
6
Transaction costs - even for banks, funds etc, every trade has an associated cost, so if you would be buying a small number of shares, it's probably cheaper to carry the risk and not make those small trades.
The source data is imperfect, and contains noise. A lot of the smaller components are simply artefacts of that noise so it would be both an unnecessary ...
6
This is the well known Euler's equation for optimality. The trick here is to setup the budget constraint correctly. Your initial wealth $W_0$ is irrelevant. The terminal (risky) wealth is,
$$W = W_0( 1 + \pi_1 (R_1 - r_f) + \ldots + \pi_n (R_n - r_f) )$$
(Check that this can be written this way), where $\pi_i \in \mathbf{R}$ is the weight allocated to ...
6
Mean-variance (MV) is a framework rather than a prescription. This framework allows one to make, discuss, and defend his investment decision.
In practice, there are many ways to make adjustments to this framework, if you believe they will improve performance. E.g. you can adjust the framework by stating "I will MV-optimize weights subject to "0" if the ...
6
That's a pretty heavy question for this forum, and its answer is worthy of a semester-long discussion in a university course. The short answer is that (for convex optimization) the dual problem can give you a lower bound on your objective function (for minimization).
In addition, the values of the dual variables are related to the sensitivity of your ...
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
concerning your first question: the derivative does not disappear: $\sigma(R_p)$ contains the square root.
To be more precise, set
$$
\sigma(R_p) = \sqrt{w_1^2\cdot\sigma(R_1)^2 + w_2^2\cdot\sigma(R_2)^2 + 2w_1w_2\text{Cov}(R_1, R_2)}.
$$
Then we get using the chain rule:
\begin{align}
\frac{\partial\sigma(R_p)}{\partial w_1} &= \frac 12 \cdot \biggl(\...
6
Order execution optimization: how to execute changes to your portfolio without suffering (too much) from implementation shortfall. Work of Almgren and Chris set a modern foundation of this space, and on top of that work of Jim Gatheral for closed form solution.
In addition, consider if you're executing an order for market neutral portfolio but market is ...
6
To complement @skoestimeier's answer on the shortselling-allowed case, I provide a vectorised version. Using the original notation in my post (you may change $r$ to something like $r-r_f$, but this doesn't affect the algebraic structure). Our goal is to find the maximiser for the problem
$$\max_{w}f(w):=\frac{w^T r}{(w^T\Sigma w)^{1/2}}.$$
Let
$$\phi: w\...
6
Let $R$ be a random vector of risky returns and let $r_f$ denote the risk free rate. Let vector of expected returns $\boldsymbol{\mu} = \operatorname{E}[R]$ and covariance matrix $\Sigma = \operatorname{Cov}(R)$.
The maximum Sharpe ratio portfolio among risky assets is called the tangency portfolio.
Quick method to tangency portfolio
Let's find the ...
6
The Minimum Variance Portfolio (without constraints, other than the weights sum to one) is usually found as
$$w=\frac{\Sigma^{-1} \iota}{\iota^T\Sigma^{-1} \iota}$$
where $\Sigma$ is the Covariance matrix and $\iota$ is a vector of all ones.
However, there is another (equivalent) way to find it. Memmel and Kempf (2006) SSRN 940367 showed that you can find ...
6
1) To be honest, any horizon is problematic in this respect. Simple sampling statistics 101 will tell you that the standard error around any estimate of true mean returns is the root time * variance. So for eg stocks at 20 vol, that's a +/-40% 1y 95% confidence interval around your sample mean ;-) With 100 years of data, that's still +/-4%! Which is in-line ...
6
Two separate cases were identified by R.C. Merton in 1972:
In the economically more relevant case, where $r_f < b/c$, efficient
portfolios are combinations of a long position in [the tangency] portfolio M and
lending or borrowing at the risk–free rate.
In the case where $r_f > b/c$, efficient portfolios are generated by short (or zero) ...
6
Suppose the covariance matrix is $V$ (which is n by n) and the weights are $w$ (of length n).
Then the Portfolio Variance is $V_p = w^T V w$
and the Risk Contribution (in terms of variance) of asset $k$ is
$RC_k=w_k \sum_j V[k,j]w_j$
in words this is "the weight of asset k times the inner product of the k-th row of $V$ and the weight vector". (Sometimes ...
5
There are plenty of books on portfolio issues built according to formula "some theory + some R code (or Matlab, or S - which is very similar to R)". See for example
Pfaff B. Financial Risk Modelling and Portfolio Optimization with R.// 2013.
Best M.J. Portfolio Optimization. Chapman & Hall, 2010.
Würtz D. et al. Portfolio Optimization with R/Rmetrics. ...
5
This optimization is trivial
$$
w^{T,J}_i = \begin{cases} 1 \quad \text{if } i=\arg \max_i R^{T,J}(S_i) \\0 \quad \text{otherwise} \end{cases}
$$
That is to say, when you optimize only one weight will be nonzero. That's because these ratios incorporate no notion of distributional width, and therefore do not reward diversification.
With no concentration ...
5
There's more than one way to shrink a covariance matrix. You can think of shrinking a covariance matrix as part of general class of estimators that limit the norms of a matrix. You could alternately think of shrinkage as a form of Bayesian analysis. Given the broad set of techniques one could use, it can be more helpful to think in terms of techniques to ...
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