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Estimation of risk aversion density distribution in a given market

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 private private,

 Monday, December 4, 2017

Many algorithms and trading methods try to predict the market response to a given strategy: in other words taking into account the reaction of traders who trades against you. These approaches generally rely on a risk parameter (or distribution density) which can be related to investors risk aversion in the market. Academic papers describing these methods remain generally silent on the way one can estimate market's risk aversion, what about in practice?


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2 comments on article "Estimation of risk aversion density distribution in a given market"

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 Vasily Nekrasov, Quantitative Developer at IDS GmbH – Analysis and Reporting Services

 Sunday, December 10, 2017



It is very hard topic alone due to the problem of robust(!) estimation of market parameters.

To understand what I am talking about by means of a simple Monte-Carlo experiment consider the last R-script from this post https://letyourmoneygrow.com/2016/09/16/stripping-down-the-robo-advisors-sparrow-brains-inside/

Back to your question: Ziemba did some work and found out that e.g. Buffett is a full Kelly investor, whereas Keynes was 80% Kelly (being more than 100% Kelly asymptotically means taking idiosyncratic risk).

I also use Kelly Criterion to determine the risk preference of investors, however, only those whom I can personally talk to.

In a sense, if you manage to have a representative(!) sampling of investors, your problem is solved.


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 private private,

 Tuesday, December 12, 2017



Thank you Vasily, for your insights. You're right, in general, it is very difficult to get a robust estimation of market parameters. Therefore, the optimal enveloppe in the strategic layer is quite wide.

It seems that having a good sample of investors is the most popular way to get an idea about the distribution of risk aversion. This is done also in other areas. In general, one obtains a Gamma or Log-normal like distribution.

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