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Asset Allocation, Value-at-Risk Summary Incorporating longer time horizons and probability functions associated with asset classes, analysts at FidicuaryVest were able to use RISK to answer questions that cannot be addressed using static or optimized financial models.

More importantly, the models give our clients a strong indication of the impact that each component of asset allocation and diversification delivers in terms of their specific needs for future investment returns and risk management.

Joe DiNunno, Insightful Ideas Decision makers for all long term investment pools face a critical challenge, regardless of their level of sophistication: Multi-year allocation among the various asset categories available to investors.

This particular decision is known to be the primary factor that will determine long-term portfolio outcomes.

Adequate investment management within the underlying asset class is actually a secondary issue. That being said, the inherent question is: There are actually two challenges here: The challenge can be met by assembling the following elements: A large volume of historical market and economic data that is well catalogued and in significant detail.

Significant investing knowledge and experience. Sophisticated software tools that are specifically designed for both historical pattern-identification and delivery of forecasted future outcomes, displayed in an easy-to-communicate graphic, probability format.

The partners typically build forecast models for three types of investment projects for clients: By adding liabilities to the modeling equation, the partners are able to determine, for example: Why They Turned to RISK 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.

When all asset class returns are assumed to follow the normal distribution, an efficient frontier model will yield risk vs. While this approach provides useful results, it still leaves many questions unanswered.

To begin with, one feature of RISK that DiNunno found very helpful in building financial models is its capacity to analyze historical data to determine a probability distribution for each asset class.

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Customarily, the normal distribution has been used to model asset classes. Another important motivation for choosing Monte Carlo simulation, in preference to the simpler optimization techniques of the efficient frontier model, is that it allows the user to realistically incorporate the effect of time horizons longer than one year.

This is important because: The Building Blocks Approach The usefulness of any forecast of anything depends heavily on the quality of assumptions used to produce it.

The foundation of all building blocks is the market-determined current risk-free interest rate, for the forward period that the client determines is its investing time horizon. Individual asset-class return assumptions are thus driven by the current risk-free rate, as well as the projected risk-free rate of return over the investment time horizon.

The risk premium developed for each asset class is then added to this risk free rate to build the expected return used in their models. While the partners update their asset allocation model assumptions on a regular basis to reflect current market conditions, the biggest change in recent years has been the movement from models that included potential asset classes within a client portfolio i.

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It is a relatively straightforward technique to review a correlation matrix, in order to understand the diversification benefits of adding a single asset class to a portfolio of just stocks and bonds. When compared to getting a handle on the impact of escalating a portfolio of 5 or 6 asset class investments to a dozen or more, the benefits of a more robust analysis are immediately evident.

Current asset management and short term financing

Lessons from the Finance Debacle The financial crisis of convinced Gregg and Joe that it was time to look closer at the use of the normal distribution for modeling the probability of investment returns for equities.

While the normal distribution assigns about 1 chance in for the stock market to experience a 3-standard-deviation decline, such as the market experienced inthe 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 1 out of every 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 'Black Swan' events?

The solution, it turns out, was to use a combination of standard distributions that creates a simulated distribution that more closely resembles reality and provides more rational 'Fat Tails'.

Another revelation of the financial debacle in was the fact that correlations between most asset classes were, at least temporarily, much higher than expected. On the other hand, some of the asset classes that historically have had a low correlation with equities had positive returns for the year, a result that makes sense.FHIF is an open-ended Income Fund having an objective of providing returns from a portfolio of medium risk short to long duration assets.

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