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5

I would create categories, and work on risk parity among the categories. Otherwise, variance is not really a good measure of downside risk: Change your risk measure, use a rolling window historical VaR or Expected Shortfall at some horizon that matches your investment style. downside semi-variance could do the trick too if don't want to change your algo ...


2

I have experience of C# as a strategy client at the end of a VB .Net ticker plant. The latency fluctuations caused by the garbage collection could be in the order of seconds! And occurred every four or five minutes with a stream of a 1000-ish ticks a second. I was the first engineer to test our trading system in this way, it was a shock to all concerned and ...


2

I'd recommend M. Joshi and T. Leung "Using Monte Carlo simulation and importance sampling to rapidly obtain jump-diffusion prices of continuous barrier options". Though it assumes jump-diffusion process for the returns it is straightforward to obtain the scheme for a diffusion process. Also Paul Glasserman's [book][2] [2]: ...


2

This problem is not interesting enough, because putting your money in the bank guarantees you zero volatility (and a zero return on investment). In practice, whatever set of assets you chose you would get a very extreme solution (e.g. 100% weight on one asset with very low volatility.) With a minor tweak, you can get a very interesting problem. You can ...


1

Since there is a closed form in the BS case for continuous barrier options, you probably won't find a huge amount of work on this since it's not needed. In the discrete case, I did a paper with Tang: http://ssrn.com/abstract=1441142 Pricing and Deltas of Discretely-Monitored Barrier Options Using Stratified Sampling on the Hitting-Times to the Barrier


1

Are you working with futures data (mixing rate futures with equity futures) OR allocating between macro instruments? If so, using non-linear variants of GARCH (GJR-GARCH, TGARCH etc.) are common way to solve your risk parity allocation issue that you might be facing. One common related issue is not that the volatility to a couple of instruments drop ...


1

I know this is a really old question but here is something I ran into while trying to do essentially the same thing. One of the problems that you face when trying to detect patterns using (say) k means clustering is how do you encapsulate a pattern. For example, suppose on a certain day the index goes up 2% over a minute and then goes down 1% over the next ...


1

It does create a see-saw. This can be reduced by having it charge a slightly bigger spread, which gets contributed to b. In this way, b effectively becomes a market making fund, and volatility decreases as trade volume increases. This makes the LMSR market maker liquidity sensitive. This makes the market more efficient as spreads decrease over time as ...


1

Hello people. This is quite a complex problem if you want to solve this in a computationally efficient way for a rolling window. I have gone ahead and written a solution to this in C#. I want to share this as the effort required to replicate this work is quite high. First, here are the results: here we take a simple drawdown implementation and ...



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