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 Dec9 comment Book recommendation on robust optimization I am afraid for a bachelor in economics, the Ben Tal / Nemirovski book will be far too technical. Dec4 comment How to extrapolate VaR? as @Richard pointed out, the scaling rule depends on the distribution. Value at Risk is a distribution quantile. The Quantile of a Normal Distribution with $\mu = 0$ scales with $\sqrt{T}$, in general it does not! Dec2 comment How do I artificially generate intraday ticks data from a given input (Open,High,Low,Close,Volume) using Brownian Bridge method? @jaamor You could do that, but those times will not be independent I suppose. I think the result wouldnt look too good. I would take the high and low value and use it as a dispersion measure to improve my intraday volatility estimator and then create BB paths from it. They will, in general, not reach the high and low values but the question is: Does it make sense to enforce this? Dec2 comment Is an arbitrary prior for Black-Litterman valid? Or do we need a market implied one? Hope it helps. Lets try to find a day - check your inbox in a few hours! Dec2 comment Is an arbitrary prior for Black-Litterman valid? Or do we need a market implied one? @Richard I tried to clarify my answer a little and added an additional point. Dec1 comment Is an arbitrary prior for Black-Litterman valid? Or do we need a market implied one? @Richard Hallo, I have to check the details about the risk parity portfolio agai and will catch up on that tomorrow. Oct7 comment Determine $E[W_p W_q W_r]$ For a normal distributed rv $X ~ N(\mu,\sigma)$, $E[(X-\mu)^3] = \mu^3+3\mu\sigma^2$ (just search for moment normal). In our case, $\mu=0$ and $\sigma = p$. Alternatively, you can calculate it by hand (several times integration by parts) or via the moment-generating function. Oct1 comment quadratic programming portfolio optimisation Hm, thats why I wasn't sure this is the right explanation because $x$ should be $x = [0.8,0,0.5,0,-0.3,0]$ in this case (splitting $x$ up in a positive and a negative part). Now, $x(1:3)+x(4:6)$ is the original portfolio and $x(1:3) - x(4:6)$ its component-wise absolute value. Oct1 comment quadratic programming portfolio optimisation Its not linear because $|-x|\neq-|x|$ which would hold for a linear function. Oct1 comment quadratic programming portfolio optimisation Please provide some more information about the constraints the example employs. There are techniques to reformulate optimization problems that simplify those but I doubt thats the problem here. Does your example use a different solver than matlab? Have you tried solving the example your way and compared the solutions? Further more: If you look at F and multiply with vectors $(x,-x)^T$ and $(x,-x)$ from both sides, you will miss the factor $1/2$. Probably the author moved into the $c$ but without any more information its really hard to say... Jul23 comment reference question about portfolio optimization Although I voted to close this question: You are recommending the Pfaff book. I havent read it yet, is it good? Just a small comment though: #4 is also weritten by Bernd Sherer and those are commercial software packages. #3 Is basically a cookbook for the corresponding R package and treats rather advanced optimization problems (compared to classical MVO). Jul17 comment Handling Missing values in stocks returns when estimating the co variance matrix @user3481555 Well if you delete the stock you simply reduce your investment universe. My intuition says that this induces at least some bias because the stocks with shorter history had a reason to enter the index later on. On the other hand, If you throw away the all the data where at least one stock has a missing value you might run into dimension problems (if you estimate a covariance matrix for hundrets of assets you want to search for "dimension reduction" or "shrinkage" or "robust" in quant.SEs search function). I will see if I can come up with the paper I mentioned... Jul2 comment Implementing A 50/50 Prediction Model Strategy @emcor The 50% rate is only the percentage of right classifications. Just imagine an algorithm that gets the lottery numbers right 50% of the time. See also the comment by Joshua Ulrich Jun26 comment economic facts that causes the financial time series to be heavy tailed For EVT, the more puzzling fact is that the "real" heavy tails do not scale with time... Jun23 comment What Is A Good Success Rate Using Machine Learning For A Beginner? I think this question is definitely appropriate here. I am no expert on this topic, but I think if you use models like that you aim for a success rate that is well above $50\%$ with the amount depending on the cost of implementing the trading signal so that you place bets with an expected value >0. Further more, you would want to add some additional margin (for estimation and modelling error or changing environment for example). It definitely depends on the cost of getting in and out of the position. Thats why there can't be a single target number. Jun11 comment PDF Calculation by Fourier Inversion of Characteristic Function for Affine Intensity Process in Matlab Have you tried the matlab-function ifft? It seems to make more sense than doing the quadrature explicitly. Depending on the implementation, you might have to normalize your vector afterwards though. Apr25 comment derive black scholes greeks I suppose $q$ is a continuous yield dividend and $r$ the risk-free rate. But be careful, $N^\prime$ is most likely not the differential operator here! It is the probability density function of the standard normal. Apr15 comment how to extend lognormal model so that $\sigma$ is correlated to $\mu$? @athos: I guess what you will end up with in the end will something like the "market price of risk" (the linear relationship you mentioned) and the notion of a risk-neutral measure. Please do not mix up correlation and simply a functional relationship. The correlation of deterministic quantities is always $0$. If you really want to pour time into this: This seems like a dead end to me. Mar31 comment Understanding the derivation of a ML-estimator Could you clarify the first equation in (1)? There are some brackets missing I suppose? And why the notation $(\mathbb{1}^\prime \mathbb{1})^{-1}$? Isn't that just $1/T$? Mar21 comment Scaling of a transition matrix @BrianB Nice!! Now that I read it, the Schur decomposition looks like the natural approach for this...