Asset Allocation Modeling with Black Swans Using Monte Carlo Simulation

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Presented by

Joe DiNunno, Chartered Alternative Investment Analyst

About this talk

The traditional approach to asset allocation has been to use efficient frontier models that seek to find the optimal portfolio mix that has the lowest possible level of risk (standard deviation) for its level of return (mean). In these models all asset class returns are assumed to follow the normal distribution. The financial crisis of 2008 convinced us that it was time to look closer at the use of the normal distribution for modeling the probability of investment returns, in particular for equities. While the normal distribution assigns about 1 chance in 1000 for the stock market to experience a 3-standard-deviation decline, such as the market experienced in 2008, the reality is that a move of this magnitude occurs more frequently than that. Looking back at historical data, a move up or down of 3 standard deviations or more, over 6 months to a year, happens more like 2 out of every 100 time periods. The problem then is: How do you add 'Fat Tails' (outlier events that occur more frequently in reality than would be predicted by the normal distribution) to the model distribution in an effort to give the ‘theoretical’ stock market a more realistic chance of experiencing one of these unexpected 'Black Swan' events? In this webinar we'll demonstrate how @RISK can be used to create financial models that give investors a more realistic view of the risks in their portfolios by incorporating simulated distributions that more closely resemble reality and provide more realistic 'Fat Tails'. Another revelation of the financial debacle in 2008 was the fact that correlations between most asset classes were, at least temporarily, much higher than expected. On the other hand, some asset classes that historically had a low or negative correlation with equities had positive returns that year, a result that makes sense. In this case we'll talk about how we were able to incorporate a second correlation table in to our model to account for stress in financial markets.

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