Bogleheads

[rosamlorente]

[gatorking]

http://www3.wabash.edu/econome..../MCSim.htm

Check out this and other add-ins.

[Quantext]

Hi guys:

My name is Geoff Considine--I built Quantext Portfolio Planner. I tried to post some relevant articles, but I could not due to the filters here. I will try to explain without links.

The challenge of Monte Carlo simulation is to generate forward-looking parameters for a portfolio. This is much harder than the basic problem of simply simulating mean, standard deviation, and covariances. You have to develop the estimates for the means and standard deviations and, on top of this, how to deal with covariances--and you don't want to just use history.

Geoff

[Robinhood]

Thanks Gatorking. That's a tool I've been looking for.

[dothemontecarlo]

[Quantext]

First, I have an article on SeekingAlpha that shows a nine-year out-of-sample portfolio analysis using QPP to develop the asset allocations. This is benchmarked against buy-and-hold, annual rebalance, etc. The article is called Testing Forward Looking Asset Allocation, dated October 22.

I have a range of detailed articles that explain how QPP works, but to address the questions:

1) QPP is a non-stationary model, but does assume normal returns ' lognormal prices

2) QPP handles portfolio covariance using a model that is somewhat proprietary..so I cannot give exact details. I have a detailed discussion in some articles, and I compare to Style Analysis in one called:

Diversifying Portfolios With Market And Non-Market Correlations

This is on SeekingAlpha and it is from April 2006. If you read this, please note that QPP was projecting volatility would be twice as high, on average, as the recent years had been...this goes to the other question about Reversion to the Mean:

Generating Meaningful Outlooks for Portfolio Performance

This article is on SeekingAlpha and is dated March 1, 2007.

[Quantext]

As far as Gatorman's comment about having a portfolio that looked good in the model but blows up in practice:

All models can be overfitted / overtuned. QPP and other good monte carlo models use theory in combination with data to help in building better portfolios--and QPP has tested well out-of-sample---I have many studies available publicly--including by third parties. The fact remains that models can always be overfitted. I have suggested many times that if a model (any model) gives results that cannot be explained or are inconsistent with reliable people and models, this is a bad sign.

The good news is that there are consistent standards emerging. I just wrote an article in which I showed that QPP's estimates of risk for a set of stocks were remarkably consistent with some results from RiskGrades, for example. I have also shown consistent results with sources like Ibbotson, and discussions by David Swensen and El-Erian.

[dothemontecarlo]

[Quantext]

QPP handles mean reversion in a very different way than you are proposing. QPP gets historical data and uses these to generate volatility projections for each asset in the portfolio. QPP then calculates a "fair" expected return based on this volatility projection. If an asset class has high historical returns relative to risk, QPP's projections will be lower and vice versa.

[Quantext]

oops--just realized I did not answer your whole question:

The user can adjust the worldview by (1) adjusting the assumed equity risk premium or (2) by hand adjusting the expected return for each asset. There is a version of the model (called QRP) in which you can adjust all parameters (individual volatilies, Betas, etc.) but getting an internally consistent dataset when you change these factors by hand can be challenging.

[dothemontecarlo]