John

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Nov 27	comment	How to make the final Interpretation of PCA? When not relying on Bayesian techniques, I can see the advantage of PCA for dimension reduction. Consider high-dimension estimation of the covariance matrix where the number of observations is smaller than the number of securities. This typically leads to problems. Alternately, VAR or Garch estimation on a small number of factors is usually faster with fewer parameters than estimating them on every security in the universe.
Nov 27	comment	Does entropy pooling apply to distributions with time-varying drift? You normally would simulate to $\widetilde{X}_{t+k}$ and apply EP to that. I'm saying simulate $\widetilde{X}_{t+1},\ldots,\widetilde{X}_{t+k}$ and concatenate them into one matrix $\widetilde{Y}\equiv\left[\begin{array}{ccc} \widetilde{X}_{t+1} & \cdots & \widetilde{X}_{t+k}\end{array}\right]$ and treat that as though it is one distribution.
Nov 27	answered	Does entropy pooling apply to distributions with time-varying drift?
Nov 21	comment	portfolio optimization from empirical return distributions Since when does Monte Carlo only do that?
Nov 18	comment	Missing factor in the factor model Not necessary. The returns on the index should explain a significant amount of the variation, but PCA can also help.
Nov 17	comment	Missing factor in the factor model I'd have to know more about what the data is like.
Nov 16	answered	Missing factor in the factor model
Nov 16	comment	Does mean reverting imply mean stationary? It is possible that he means covariance stationary.
Nov 15	answered	Why is the CAPM securities market line straight?
Nov 15	comment	Where can I find corporate bond spreads? Ah, so what you really want would be like an A- yield curve to use that spread to price a hypothetical bond. If you have a sample of all A- bond yields, you could construct your own but I'm not sure what providers give something similar.
Nov 15	comment	Where can I find corporate bond spreads? Have you tried the YAS function? It should have the G-spread on there.
Nov 14	answered	Is it possible to derive the “risk tolerance” from the portfolio efficient frontier?
Nov 13	comment	Combining Mulitple Forecasts? Budged Constraints? Let's say you estimate $z_{t}=B_{1,0}+B_{1,1}x_{t}+e_{1,t}$ and $z_{t}=B_{2,0}+B_{2,1}y_{t}+e_{2,t}$ and make forecasts from each. The optimal combined forecast wouldn't be the sum of the forecasts, but some sort of weighted average.
Nov 13	comment	Combining Mulitple Forecasts? Budged Constraints? What you have done is constructed a time series of the form $z_{t}=x_{t}+y_{t}+e_{t}$ and then regressed $z_{t}$ against $x_{t}$ and $y_{t}$. Imagine instead you have some time series $z_{t}$, you fit an AR(1) model and obtain a forecast and then fit an AR(p) model and obtain a forecast. How you combine those two forecasts is a categorically different problem.
Nov 13	comment	Combining Mulitple Forecasts? Budged Constraints? This is more like a regression model than combining multiple forecasts.
Nov 13	comment	Combining Mulitple Forecasts? Budged Constraints? I don't understand why you wouldn't want to the weights to sum to 1. Say one forecasts 10%, another forecasts 15%, it makes more sense to average the two (e.g. as in the literature on Bayesian model averaging) than sum the two.
Nov 9	comment	How do you explain the volatility smile in the Black-Scholes framework? After the 1987 crash people realized that extreme events were more likely than the log normal distribution suggests. They developed better option models, leading to the out of the money options to be priced more expensively to account for the greater risk. People still talk and think in terms of BS implied vol because 1) it is convenient, 2) many other models can be considered extensions of Black-Scholes, and 3) they can use the volatility surface from the market to price exotic options.
Nov 6	comment	Rank Correlation Based Prediction I just ran across this, which may or may not be helpful: pluto.huji.ac.il/~galelidan/papers/CopulaMLSurvey.pdf
Nov 4	comment	Why does $\hat{\epsilon}'\hat{\epsilon}$ of a factor model measure risk? I do not think that is the case generally. I often cannot explain the motivations of writers, but that paper specifically says they do not see much difference the returns of strategies investing based on idiosyncratic vs. total.
Nov 4	comment	Why does $\hat{\epsilon}'\hat{\epsilon}$ of a factor model measure risk? It is simply the variance that cannot be explained by the market or whatever factors you happen to looking at (hence, idiosyncratic risk). I think there are a lot of reasons to care about it, but recently there's been a lot of focus since people have found that stocks with high idiosyncratic variance tend to underperform those with the low. Not sure where you're going about the trace.