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seen Nov 25 at 5:19

Nov
24
comment What are the canonical books for statistics applied to finance?
And his website has tons of support materials.
Nov
19
comment Different ways of portfolio optimization
@user8 I think James is pretty clear. For these problems, it is pretty easy to analytically show that they all produce the same efficient frontier. So for a $\lambda$ you could find a target return or target volatility problem that will produce the same optimal portfolio. Whether you want to use one or the other depends on what you're trying to do and what optimization software you have. Nevertheless, contra James I still could imagine a situation where I would optimize utility and still constrain variance or tracking error.
Nov
12
comment Mutivariate t markets
@Quartz I made a slight edit to throw in some basic background. I'm not sure how the elliptical point really helps you. I find that once I start working with heavy tail distributions, I typically just move to a Monte Carlo approach rather than work analytically. Any other issues I can help you with?
Nov
6
comment Factor Model - Minimum Variance Portfolio [Complete Proof]
It's not a coding issue so much as it's the result of using long short portfolios. If you do the optimization imposing the long-only constraint on weights, then the results will be more stable. I'm sort of loathe to do things in terms of factor weights because it might force me to go short a lot of positions I wouldn't want. You might alternately just allow yourself to be long and short liquid instruments (like long SPY short IWM to account for a size effect).
Nov
6
comment Factor Model - Minimum Variance Portfolio [Complete Proof]
Perhaps I wasn't particularly clear. The $\Sigma$ could be any invertible covariance matrix. I meant that you can plug in the formula for $\Sigma$ being whatever it is. Obviously, if you're only using the factor covariance matrix, then your weights would be $K \times 1$ (which seems silly to me), but you can easily transform that into the security covariance matrix and have it be $N \times 1$. So you could replace $\Sigma$ with whatever formula you need and then express the weights analytically in terms of each part.
Oct
27
comment Portfolio Optimization using S&P Universes
This question might be a bit too general to be able to answer. It might be improved by discuss within the context of a particular optimization or factor model.
Oct
27
comment How is stock data objectively different to this random walk?
You can have a random walk with a non-normal distribution. However, if the non-normal distribution is the result of some underlying process (like regimes per @vonjd or stochastic volatility), then it would not be a random walk anymore.
Oct
26
comment Statistical arbitrage using eigen portfolios
If you were implementing Section 5.3-4 in practice, then yes, you'd net things out across all the different arb trades you're making and betas on each portfolio to have to figure out how much of each stock to buy.
Oct
22
comment Where can I find a list of VaR and CVaR formulas for continuous distributions?
Other than the one posted by @YuliaV, I'm not aware of any papers like that off the top of my head. In practice, I just don't use the analytic formula often (really only for normal VaR). Not sure how common that is for others.
Oct
22
comment Where can I find a list of VaR and CVaR formulas for continuous distributions?
I disagree with your assertion that CVaR is not a commonly used term. They are interchangeable, as far as I'm concerned.
Oct
16
comment Expected Shortfall (CVaR) Backtesting
@emcor I had meant simply using the historical CVaR for the purposes of the backtest, rather than rely on any assumptions about the distribution of returns for the strategy. If you want a confidence interval on the ES, you can bootstrap from the historical returns. I've seen some literature on expectiles, but I haven't had the chance to read it yet.
Oct
3
comment Portfolio Turnover Constraint
@Richard It's not just them being positive, it's the piecewise nature of the constraint. Optimizers that require continuous functions will tend to not like it when you use absolute values. Also, this approach can easily be extended to include transaction costs, though you probably need to add in non-linear constraints $b_{i}s_{i}=0$ to ensure that sells are zero if you have buys, and vice-versa. My recollection is that it's not usually needed for turnover, but there might be cases where it is.
Sep
30
comment How to interpret ACF and PACF plots
I don't see anything interesting in these. Looks stationary.
Sep
25
comment Factor model assumptions
Alternately, consider a model like Barra where you use cross-sectional regression. The $B$ term changes in every period, but you can still use that formula to decompose the covariance of $X$.
Sep
25
comment Factor model assumptions
Suppose you regress every stock in $X$ against the Fama-French factors (so this is a time series factor model). If you assume constant 2nd moment, then you can do the covariance matrix of $X$ as is normal for factor models. If you assume Garch volatility for $F$ with constant correlation and assumptions as above for idiosyncratic returns, then you can still use that formula, but you have to adjust it to be a conditional covariance.
Sep
15
comment Exporting Time Series Data For Securities Prices From Bloomberg to Excel
Just get your firm to pay for the add-in.
Sep
9
comment How do Return.portfolio and Return.rebalancing work in Performance Analytics in R?
I do not see your motivating example in the PerformanceAnalytics pdf. The closest comparable thing I see is round(Return.rebalancing(edhec,weights),4). You'll note that the 2007-01-01 return matches up with edhec[121,1:11]%*%t(weights[8,]), ignoring the rounding.
Aug
28
comment What kind of front end/ gui is used with trading applications?
Can you recommend a more thorough reference on the subject (preferably starting from the basics and working up)?
Aug
28
comment State Space models with Short Time Series
More to just get a sense if it's working the way that it should.
Aug
28
comment State Space models with Short Time Series
I was focusing on the estimation. Combining that with testing a trading strategy will introduce more complexity. No one right answer, I think. One thing I do sometimes is assume the model is true, then simulate a bunch of paths of data, and then run the trading strategy on simulated data and evaluate it way on the larger sample. Not sure if that makes sense in your case, because I'm really not sure what you're doing.