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seen Nov 17 at 12:01

Monte Carlo, risk, QMC, statistical efficiency, high dimensional approximation...


Jun
11
comment Volatility Estimation
Is E in the first case the covariance between strategies? Are these two E-s coherent/how is it ensured?
Jun
7
comment Block Bootstrapping Relative Returns
Anyway for risk a 0 mean return is a common assumption. Not only using a drift is an arbitrary decision (just like 0 drift, but slightly less so) and the process might not be stationary, but there's $\mu_r$'s estimation error too. As a compromise you could blend in $\mu_r$ weighting it by (an estimate of) the estimate precision, not just strictly statistical precision but also sample relevance taking into account how far in the past you're measuring (200 days or weeks are different in two ways).
Jun
7
comment Block Bootstrapping Relative Returns
" You can't use the original prices, so you have to use the relative prices." Do you mean returns?
Jun
5
awarded  Informed
Jun
5
revised VaR for portfolio of funds
typo corrected
Jun
4
comment What commercial financial libraries are available to outsource implementation risk?
@SRKX: may I ask if in the meantime you found and decided to use one? If so, what were the topics needed&covered and the criteria that were satisfied?
Jun
4
comment George Soros models
Whops hadn't noticed your field... then please keep us updated if you find more on this!
Jun
4
comment VaR for portfolio of funds
Is it clear/enough like this or shall it be edited further? I'm writing in a hurry from work so it might not be clear at a first read... I kept it more on the historical modeling there, other than referring to forecasts discrepancies which emerge as a consequence anyway.
Jun
4
revised VaR for portfolio of funds
some detail on the hybrid approach
May
31
revised George Soros models
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May
31
comment George Soros models
@Ilya: sure, I edited the answer because of lack of space here.
May
31
revised George Soros models
added 1685 characters in body
May
31
comment VaR for portfolio of funds
@quant_dev: For example, yes, that's the first & most basic step, but that will only capture some of the discrepancies since it's a static view. Then you can feed it back in your forecasts as an independent component. Improving on this one could first model dependencies with the other variates, and then sample joint forecasts. But to also capture temporal discrepancies, such as rebalancings, you should compare/model the running forecasts (returns conditional on the statistics on previous data) historically, in a kind of backtesting, not just the 2 valuations series. I'll expand if too cryptic.
May
31
comment George Soros models
@Quant Guy: whops it seems that I downvoted by error, I want to upvote instead (although I disagree on some points), could you please edit to unlock vote changes? Thanks!
May
31
answered George Soros models
May
30
answered VaR for portfolio of funds
May
30
answered Is Unexpected Loss ever used in Basel II?
Apr
25
revised Transformation to reduce standard deviation without changing median
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Apr
24
revised Transformation to reduce standard deviation without changing median
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Apr
24
answered Transformation to reduce standard deviation without changing median