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Jan
31
comment Smoothing Term Curve
You might look into interpolation techniques that incorporate liquidity (assuming you can get the data). This would effectively put less weight on bonds that aren't being actively traded. Liquidity is an important consideration in volatility surfaces so you should be able to find some research on it. Alternately you can try a parsimonious model, like Nelson-Siegel (which there should be some questions about), and take deviations from that to identify outliers.
Jan
31
comment How to score a portfolio's diversity based on security returns?
I'm not exactly sure what you mean by diversity, but there's a (rather, at least one) question on average correlation. quant.stackexchange.com/questions/8689/…
Jan
31
revised Need overlapping sample autocorrelation correction for calculating asset return correlations
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Jan
29
comment Portfolio Optimization : Shrinkage of Covariance Matrix when data is available
You seem to be focused on shrinkage only to ensure the covariance matrix is positive definite. That's not the only reason to use shrinkage. Reduction of estimation error is another reason. My answer focused on the benefits of reducing estimation error.
Jan
29
answered Portfolio Optimization : Shrinkage of Covariance Matrix when data is available
Jan
27
reviewed Approve suggested edit on Local Volatility vs. Stochastic Volatility
Jan
27
comment Where do these Orders come from and what do they mean?
@JoshuaUlrich, I feel this is related to market structure, which is on topic.
Jan
27
accepted Strategies for Liar's Poker
Jan
24
revised What are DGTW adjusted returns?
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Jan
24
comment What are DGTW adjusted returns?
@Anna you might want to incorporate that comment into the answer.
Jan
24
reviewed Approve suggested edit on What are DGTW adjusted returns?
Jan
24
comment Estimation of Empirical Expected Shortfall of a heavy tailed distribution
I've been confused by this question since when I estimate the empirical ES/CVaR, I normally don't simulate anything. For instance, for a portfolio of equities, I would use the current portfolio (with some assumption about rebalancing) with the historical returns and then follow the above formulas to get the ES. Are you concerned with securities that wouldn't have historical returns in the same way, like exotic options? (so then you simulate the returns for those conditional on everything else, and calculate the distribution of ES)
Jan
21
comment Co-integration constraints of coint(X,Z) given coint(X,Y) and coint(Y,Z)?
That's where I got stuck on too.
Jan
21
reviewed Reject suggested edit on How can I go about applying machine learning algorithms to stock markets?
Jan
21
comment Co-integration constraints of coint(X,Z) given coint(X,Y) and coint(Y,Z)?
I think doing some kind of simulation might help clarify things, but I'm not sure how to get an analytical answer.
Jan
21
comment Co-integration constraints of coint(X,Z) given coint(X,Y) and coint(Y,Z)?
You prove there is multiple cointegration, but I'm not sure if that necessarily implies cointegration for the third pair. I looked for papers to check if 2 pair-wise cointegrations imply cointegration on the third pair, but didn't find anything. Typically Johansen's test is what you use to test for multiple cointegration. In practice, I don't really spend much time worrying about these issues as I can always just estimate whatever relationships I want.
Jan
17
comment Inflation modelling
With respect to Richard's point, the European HICP inflation data all comes out NSA (non-seasonally adjusted). I checked and the Spanish data from INE is also NSA. You just have to be careful because other countries might provide the SA data (like the US does). Spanish prices are typically weak in winter before recovering in the spring.
Jan
16
revised Normality assumption in Sharpe ratio
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Jan
9
comment How can I calculate the Cumulant-Generating Function in Matlab?
Meucci might have code that does this. mathworks.com/matlabcentral/fileexchange/authors/21105
Jan
6
comment Optimizing Principal Component factor weightings over time
If you have a time-varying covariance matrix (you could construct with Garch volatility and either a constant or time-varying dependence structure), then you can perform PCA on each period or project out to the future. Not sure if that's what you're looking to do.