# Tag Info

## Hot answers tagged portfolio-management

6

Since $dW_A$ and $dW_B$ are already correlated as per the way you construct it, your portfolio being the sum of the two is already correlated. If you want it very explicitity written out, then you could rewrite $dW_B = \rho dW_A + \sqrt{1-\rho^2}dW_Z$ where $dW_Z$ is independent of $dW_A$. More generally (higher dimensions) you can use Cholesky. Now with ...

5

After having done a lot of research on the topic I found the following excellent research piece on ETF.com: Wealthfront modifies historic asset-class returns with current market implied expected returns (Black-Litterman) as well as with the in-house views of Chief Investment Officer Burton Malkiel’s team. In addition, Wealthfront sets minimum and ...

4

Actuarial science traditionally focuses on estimation of joint probabilities using real data where math finance is on valuation of contracts under an arbitrary distribution. It means the first one deals with methods of estimation of future distributions (the number of accidents of a given kind, the probability of someone with a given profile to have a ...

4

Duration is not linear. It is the weighted average of the duration of the underlyings with the weightings being their values. To get a linear system multiply the durations by the associated pvs and match that quantity instead.

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 ...

4

Let us ignore the riskless rate for simplicity of the presentation. If you have (historical or simulated) return series $r_i$ for the portfolio and $r_i^M$ for the market, then the beta is the OLS regression beta: $$\beta = cov(r_i,r_i^M)/var(r_i^M).$$ Then if you write $r_i = \alpha + \beta r_i^M + \epsilon_i$ on the other hand $$\epsilon_i = r_i - ( ... 3 Let me use a notation that I am more used to:$$ \sigma^2_{i,t} = \omega_i + \alpha_i\varepsilon^2_{i,t-1} + \beta_i\sigma^2_{i,t-1} $$where i=1,2. Since$$ \text{Var}(X+Y) = \text{Var}(X) + \text{Var}(Y) + \text{Corr}(X,Y)\sqrt{\text{Var}(X)}\sqrt{\text{Var}(Y)} $$and$$ \text{Var}(x_{1,t})=\sigma_{1,t}^2, \ \ \ ...

3

First and foremost you are using bad data. min(data) gets me -3.67 (it's random remember) which would be -367% as in the position went bankrupt and took out two other ones (could be possible in a levered porftolio). However for the sake of an reproducible answer lets use the edhec data set, very little changes to your original code need to be done. ...

3

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 none of the ...

3

I use the 'implied correlation' defined as $$\rho = \frac{V^2_P-\sum V^2_j}{(\sum V_j)^2-\sum V^2_j}$$ for $V_p$ the VaR (or volatility) of the portfolio, and $V_j$ the VaRs (or volatilities) of the individual components. Essentially it shows what would be the common correlation that I would need to use in order to aggregate the stand-alone risks to the ...

3

If you're annualising your data with T it should always be the same, not changing with the length of your data. To demonstrate, annualising monthly returns, the Sharpe ratios turn out fairly similar:- Note The reason for multiplying by root 12 is that the mean return is annualised by multiplying by 12 and volatility is annualised by m = 12. 12 on the ...

3

$\sigma_p=\sqrt{\omega_a^2 \sigma_a^2+(1-\omega_a)^2 \sigma_b^2+2 \omega_a (1-\omega_a) \rho_{ab} \sigma_a \sigma_b}$ with $\rho_{ab}=-1$ the term under the square root simplifies to $(\omega_a \sigma_a-(1-\omega_a) \sigma_b)^2$ which is equivalent to $(-\omega_a \sigma_a+(1-\omega_a) \sigma_b)^2$ therefore $\sigma_p=\omega_a \sigma_a-(1-\omega_a) ... 3 Put-call parity says that the difference between a call and a put is equivalent to the difference in the current stock price (adjusted down for dividends) and the strike price discounted at the risk-free rate. $$Call - Put = S_0*e^{-div} - K*e^{-rt}$$ So, if you want to have 120 dollars in the future, you would need to need to have$120 worth of "K" or ...

2

You do note require a sum up constraint that gives you that the weights sum up to 1? Then the problem is equivalent to a maximization without constraints: $$Z(\omega)=w'\mu - \frac{\gamma}{2}w'Vw$$ then it holds that $$\frac{dZ}{d\omega}=\mu-\gamma V\omega\overset{!}{=}0\\ \Leftrightarrow \frac{1}{\gamma}\mu=V\omega^*\\ \Leftrightarrow\omega^* = ... 2 In literature you'll find many approaches to compute the variance. As mentioned already, the standard ideas are to use MLE, Shrinkage on the Covariance Matrix (Ledoit, Wolf), Shrinkage on the inverse of the Covariance Matrix (Kourtis,Dotsis) which makes sense as in fact the inverse of the Covariance Matrix determines the shape of the efficient frontier. ... 2 I think generally there are two approaches: "calendar rebalancing" (such as monthly as you mention) and "optimal corridor width". For the first option, the danger is the portfolio could stray considerably from your benchmark between rebalancing dates. For the second option, track tactical deviation on a continuous basis. When you are outside the corridor, ... 2 That's the way you apply. Usually you get the closest number of shares possible. However, if you use that strategy you are very likely to underperform the market. Check table 3 on this paper for the Out of sample performance of the Markowitz strategy. Over their sample the Sharpe Ratio is 0.07 whereas a simple naive strategy 1/N yielded 0.18. 2 Yes, it is normal for a L/S fund to have a lot of cash. When you short securities your account is credited with the proceeds from the sales. So if you short 1 million of stock you end up with 1 million cash and -1 million short stock position. Another way to look at it is: as you mentioned, the weights as a fraction of NAV have to add up to 1.0 by definition ... 2 The classical connection is the http://en.m.wikipedia.org/wiki/Esscher_transform developed for actuaries in 1932 which essentially transforms the objective probability measure into the risk neutral one used in quant finance. 2 You wrote:$$d[5] = (DJIR[5] - \mu) * Covariance$$but you left out half of it (the inverse and the transposed vector on the right side). The correct formula is$$d[5] = (DJIR[5] - \mu)^2 / Var[DJIR]$$The covariance "matrix" becomes the variance in a 1-dimensional case (in other words x_i and y_i are both equal to DJIR[i] in this case) and the "matrix ... 2 Try to formulate the problem as a constrained optimization problem, and examine the KKT (Karush-Kuhn-Tucker) complementary slackness conditions. 2 If two or more (I(1)) time series are cointegrated, then this means that you can find a linear combination of them that is mean-reverting. Thus, if you create a portfolio with weights that are proportional to this linear combination, then the portfolio returns will also be mean-reverting. There is a large literature on cointegration and asset prices and ... 2 First of all I’ll work with column vectors because I find it easier than with row vectors as you did. I guess it’s a little bit easier if we modify your first equation a little bit. Notice that is really the first order condition of the following lagrangian:$$L(w, \lambda, \delta)= \frac{1}{2}{\bf w^TCw} - \lambda({\bf w^Tm} - \mu_v) - \delta({\bf w^Tu} - ...

2

The general formula for the global minimum variance portfolio is $w=\frac{C^{-1} 1}{1^T C^{-1} 1}$ where C is the covariance matrix and 1 is a vector of 1's. In this case the covariance matrix is diagonal with $\sigma_i^2$ in the ith diagonal element. Its inverse is also diagonal and has $\frac{1}{\sigma_i^2}$ in the ith diagonal element. Evaluating the ...

2

In 2006 Choueifaty proposed a measure of portfolio diversification, called the Diversification Ratio (DR), which he defined as the ratio of the weighted average of the volatilities of the assets in the portfolio, to the portfolios overall volatility. The DR of a long only portfolio is greater than or equal to one, and equals unity for a ...

2

You can also use the Herfindahl-Hirschman-Index (HCI) as a measure for concentration. In portfolio analysis, you can calculate it as $\frac{1}{N} \leq HCI(x) = \sum_{i=1}^N x_i^2 \leq 1$ where $x$ is a vector of $N$ portfolio asset weights. One can easily see that $HCI(x) = 1$ if 100% is invested in a single asset, and $HCI(x) = 1/N$ if the portfolio is ...

2

I'll try to answer according to what I've read (and I hope mostly understood). Let's assume the mean of daily returns is 1%, and the standard deviation of daily returns is 1%. Then: $$Sharpe = \sqrt{252} \frac{mean(daily\ return)}{stddev(daily\ return)} \approx \sqrt{252} \frac{1 \%}{1 \%} = \sqrt{252}$$ Now let's assume we work with monthly returns. In ...

2

You can calculate variance of a portfolio/basket by taking direct weighed averages of the components and then adding the relevant correlation terms * weights for each pair. Can take sqrt of the expression obtained to have Standard deviation. Exact formula for calculation goes like this :

2

Let's call R the riskless security (100 today, 120 at time T). And call S the stock = 50, and either 70 or 30 at time T. One way to look at it is: A] Consider: buy 2 call options (C), short the stock (S), invest 50 (proceeds from S) in R. At time T: S=70: 2C=40, buy back S=-70, proceeds from R=60. net: 30 S=30: 2C=0, buy back S=-30, proceeds from ...

2

If the riskless security cost $100$ at time $t=0$ and $120$ at time $T$ then the risk free rate, $r$, is $20\%$. So that, $r=0.2$. Denote the initial stock price as $S_0$ and price of the call option as $c$. Suppose that at time $t=0$ you buy one stock and sell $\Delta$ options. Your portfolio value at time $t=0$ is $$P_0 = -\Delta\times c + S_0$$. At time ...

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