quasi

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quasi dot surely at gmail


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May
17
 comment Desired portfolio volume
I would. The calculations unfortunately won't be as nice as with exponential utility, but doable.
May
17
 comment Desired portfolio volume
An another approach is to use the power utility functions. This family of functions (which includes $\log x$), will have the property of constant relative risk aversion, meaning that a constant proportion of wealth will be invested.
May
17
 answered Desired portfolio volume
May
17
 comment Desired portfolio volume
Have you tried with exponential utility $U(x) = -e^{-\lambda x}$ and power utility $\frac{1}{p}x^p$?
May
16
 comment Exact value of mean reversion rate knowing terminal value of the process
That's a linear ODE with a closed form solution. Have you tried using that? Solve it with separation of variables.
May
16
 revised Covariance of brownian motion and its time average
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May
16
 answered Covariance of brownian motion and its time average
May
16
 comment Covariance of brownian motion and its time average
It looks like you're assuming that if you have $X$ and $Y$ which are equal in distribution, then for any $Z$, $\text{cov}(X,Z) = \text{cov}(Y,Z)$. This isn't true in general.
May
15
 comment How to prove that markets are incomplete under the Stochastic Volatility model?
Can you show that there are multiple different equivalent martingale measures? Completeness is equivalent to the existence of a unique martingale measure. This is called 2nd Fundamental Theorem of Asset Pricing sometimes.
May
13
 comment characterization of coherent risk measures
The property you want is true. Try rewriting your expectation under $Q$ as an expectation under $P$, using the Radon-Nikodym derivative $\frac{dQ}{dP}$.
May
7
 comment Rate Distortion Minimization in a Python Clustering Algorithm
I guess I meant that now they're algebraically dependent.
May
7
 comment Rate Distortion Minimization in a Python Clustering Algorithm
Right sorry, what you said is right. But your "observations" aren't independent now. Not sure how that will affect things.
May
7
 comment Rate Distortion Minimization in a Python Clustering Algorithm
The naive application of the paper you cited seems to be: calculate $\Sigma$ as you did, then use the distortion measure to cluster the $d$-dimensional returns as a function of time.
May
7
 comment Rate Distortion Minimization in a Python Clustering Algorithm
So I'm a little confused. You're using $\Sigma$ as your underlying data, with each row a data point. So you have $d$ points in $\mathbb{R}^d$. But in your dispersion calculation, you're not using the covariance matrix of $\Sigma$, you're using $\Sigma$ itself. This seems to not be the algorithm.
May
7
 comment Rate Distortion Minimization in a Python Clustering Algorithm
any chance you could post your data?
Apr
30
 revised reasonable asymptotic elasticity in utility maximization (paper by Kramkov / Schachermayer)
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Apr
29
 revised reasonable asymptotic elasticity in utility maximization (paper by Kramkov / Schachermayer)
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Apr
29
 comment reasonable asymptotic elasticity in utility maximization (paper by Kramkov / Schachermayer)
@hulik: Ok, the first question is explained in quite terrible detail. I'm still a little lazy on the second, but again, you will fill in the gaps using an argument like the first part.
Apr
29
 revised reasonable asymptotic elasticity in utility maximization (paper by Kramkov / Schachermayer)
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Apr
29
 comment reasonable asymptotic elasticity in utility maximization (paper by Kramkov / Schachermayer)
I'll try to clean things up for you in the next day or so.
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