15
You have the correct approach.
(1) The simulation generates sampled portfolio values, $P_1,P_2, \dots, P_n$ at time $t=T$. VaR is specified as a left-tail percentile.
Order the sample as
$$P_{(1)} \leq P_{(2)} \leq \dots \leq P_{(n)}.$$
If you are considering $VaR_\alpha$ at the $100(1-\alpha) \% $ confidence level , then choose the smallest integer $k$ ...
9
Portfolio optimalisation depends heavily on the estimation of the moments (and therefore has HUGE estimation uncertainty).
Even though it's useful for comparing and analysing different existing strategies, I think practitioners are moving more towards the usage of factor portfolios for the strategies themselves (e.g. Fama-French). Also because the ...
8
Different portfolio risk decompositions answer different questions. Before discussing what method to use, first ask why you want a decomposition and what definition of risk are you using.
Is the point to examine how portfolio return volatility is affected by a tiny change in portfolio weights? On the other hand, if the point is to make a statement like, "30%...
8
CAPM states that the expected return of any given asset should equal $ER_i=R_f+β_i (R_m-R_f)$, with α being the error term of the previous equation. Now, as α has an expected value of zero, then only way to achieve higher expected returns is taking on more β (given that $E[(R_m-R_f )]>0$). Every individual stock has some idiosyncratic risk in addition 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
In recent years there has been much attention given to defining indexes other than market-cap based indices. While market-cap based indices approximate the theoretical Market Portfolio enshrined in textbooks, some people believe we could do better than that. One popular idea is that "market indexes overweight the most overvalued stocks", though this is ...
6
The first step is to divide a very large number of stocks into deciles (groups having 10% of the stocks) based on some ranked measure (for example book to market or liquidity etc.), then you construct a spread of the highest decile vs the lowest decile returns by subtracting average D1 returns from average D10 returns. The idea is simply to understand how ...
6
Does variable $x$ forecast returns?
Let's say you have some variable $x$ that you think forecasts returns, and you want to conduct statistical tests of a null hypothesis that $x$ has nothing to do with expected returns.
Why a long-short portfolio? (quick answer)
1. It gives you a reasonable shot at detecting an effect.
Imagine you have a stereo system ...
6
You can generalize the formula from a portfolio composed of 2 assets to a portfolio composed of $N$ assets as follows :
$$
\sigma^2_{port} = \sum_{i=1}^N \sum_{j=1}^N \omega_i \text{cov} (i,j)\omega_j = \sum_{i=1}^N \sum_{j=1}^N \omega_i \sigma_{i,j}\omega_j
$$
where $\sigma_{port}$ represents the standard deviation of your portfolio.
Taking $N = 2$ ...
5
If $\Sigma$ is the covariance matrix of all assets and $w$ is the column vector of weightings of the asset in a certain portfolio. Then
$$
w^T \Sigma w = VAR
$$
is the variance of the portfolio. The contribution to volatility of asset $i$ is given by
$$
w_i (\Sigma w)_i/\sqrt{VAR},
$$
where $(\Sigma w)_i$ is the $i_{th}$ entry in the vector $\Sigma w$.
Note ...
5
7 years ago I had to solve the problem of a efficiency frontier under linear constraints on the asset weights and also stumbled upon Markowitz Critial Line Algorithm. I still have a directory with some resources in it.
Since Bryce already gave a practical implementation with R code by Eric Zivot, I will concentrate on some papers which might help.
I ...
5
Of course estimating expected returns is the very core of portfolio management. Finding a useful covariance matrix too. To find both fills a book. So I first thought about closing the question. But it is a chance to discuss today's approaches.
A nice approach that is very up-to-date where mementum investing seems very fashionable is the following: Momentum ...
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 ...
4
If you only need to pick 5 out of 10 and want equal weights then just enumerate all 252 possibilities (as pointed out above) and compute the portfolio volatility
$(\textbf{1}'K^{(i)}\textbf{1})^{1/2} = \left( \sum_{ij}K^{(i)}_{ij} \right)^{1/2}$,
where $K^{(i)}$ is the covariance matrix for the $i$th subset. Then use whatever subset gives the lowest ...
4
Convex Optimisation - CVXOpt and CVXPy. Textbook by Boyd & Vandenberghe
Aside from CVXOPT (known for its cone programming, see http://cvxopt.org/) with extensive documentation by the authors, Boyd and Vandenberghe http://stanford.edu/~boyd/cvxbook/, there is CVXPY which provides an easier front end. CVXPY was designed and implemented by Steven Diamond, ...
4
I did not look at the data, but recall that beta is a parameter in the following equation:
$$
r_A = \alpha + \beta r_B + \epsilon
$$
relating two returns (random variables, samples) $r_A$ and $r_B$. To calculate beta you peform
$$
\beta = \frac{cov(r_A,r_B)}{var(r_B)}.
$$
Thus if assets $A$ and $B$ exchange roles, then only the denominator changes. In your ...
4
Using Andy Flury answer and bit polishing it gives following Python class for PnL calculator:
class PnLCalculator:
def __init__(self):
self.quantity = 0
self.cost = 0.0
self.market_value = 0.0
self.r_pnl = 0.0
self.average_price = 0.0
def fill(self, n_pos, exec_price):
pos_change = n_pos - self....
4
Actually, neither of your two results are quite correct. As explained in the Details for the Return.calculate function, most of the PerformanceAnalytics functions use discrete returns, not log returns.
To get the correct results, you will have to convert your data from log returns to simple returns.
Compare the charts from the following:
charts....
4
Non overlapping periods would make for a far smaller sample
4
Theoretically speaking (as it's done in financial textbooks at b-school level), variance and covariance are calculated on historical performance of asset classes, forward looking returns are CAPM calculated returns.
ARIMA. Practically speaking, ARIMA is useless for predicting long term returns (or portfolio management if you wish). Why? A short answer is ...
4
It is well known that the MV-optimal portfolio has some very bad properties in practice:
Backtesting: The MV portfolio performs very bad in backtesting applications
Diversification: The MV portfolio tends to invest all funds into the best asset (highest sharpe ratio) of the past, leading to very low diversification.
Non-Normality: Return distributions are ...
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
Strictly speaking, you cannot aggregate (i.e. sum)
deltas. However, equity traders often provide their net
exposure in currency units, which is a useful number. The same reasoning is
possible with equity options: You can compute the
'delta equivalent position', i.e. delta times number of
contracts (times multiplier) for each stock. Taking
the delta ...
4
It's called 'Gross return' (a.k.a. 'Gross Rate of Return'). See Zivot Course Notes Eqn. 1.8
Also, as pointed by Alex C:
... in Statistics it is called a 'link relative' or 'chain relative' (because
it links together (via multiplication) adjacent terms of the CPI index
or other time series)
4
Since you're looking to summarize the performance of a monthly return series in a single number, it is best to compute the annualized return. This is the standard used in the investment management industry.
You could also compare your portfolio returns with that of an industry benchmark like S&P 500 on an annualized basis.
Assuming your returns are in ...
4
You cannot eliminate the dependence of a solution on the risk aversion parameter (which this author confusingly calls $\lambda$).
Perhaps a source of confusion?
Typically $\lambda$ is used to denote a Lagrange multiplier in Lagrangian optimization, but the author is using $\lambda$ as a risk tolerance parameter. (In your other linked question, $\lambda$ ...
4
Theoretically, Sharpe should be the average of (compounded) excess returns divided by the volatility of the same. It was designed to measure the risk-reward preferring the risk asset to riskless. So everything should be in excess terms.
Obviously, this was a much bigger issue before the GFC, when nominal interest rates might be as high as 5%. Today with ...
4
Below proposition 1 (In the 2nd edition at p. 28?), at the beginning of the chapter, he specifically writes:
Characteristic portfolios are not necessarily fully invested. They can include long and short positions and have significant leverage. Take the characteristic portfolio for earnings-to-price ratios. Since typical earnings-to-price ratios range ...
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