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Why Monte Carlo is Not Enough: Analysing Portfolio Risk in the New Normal

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by Ortec Finance
| 04/05/2022 13:13:07

Advisers face an increasing urgency to provide comprehensive planning services to their clients while facilitating practice growth. The adviser’s role in planning clients’ portfolios, managing and monitoring their progress and forecasting outcomes is now more complex.

Recent market volatility, greater service level demands on an adviser’s practice and the increasing impact of “black swan” events – such as the subprime mortgage crisis and Covid-19 – on the long-term performance of portfolios are all headwinds that an adviser needs to contend with.

A better toolkit is in order. Traditional models of portfolio planning and projection are not enough in these market conditions. Moreover, the models often thought of as an improvement, based on simplistic Monte Carlo simulations, also fall short.

The use of Monte Carlo simulations in investment planning is viewed as an enhancement over older and simplistic straight-line return assumptions, to generate a more realistic range of scenarios and possible outcomes for advisers and clients. However, Monte Carlo simulations are driven by an erroneous assumption of a normal distribution of returns, and as a result, they often generate a range of probabilities not unlike those of straight-line assumptions.

Interest rates and weather
We can use two examples to demonstrate Monte Carlo’s limitations. In an investment context, if a fixed income investor in 2020 were to apply a Monte Carlo simulation when analysing the past 15 years of U.S. government bond returns. The model would assume a 4% annual return for the entire simulation of clients’ financial plans, ignoring the reality of Treasury yields being at all-time lows in 2020 due to Covid-19.

For a more stark example of Monte Carlo’s limitations, we can look at a non-investment analogy: weather forecasting. A forecast based on a simplistic Monte Carlo simulation would just generate thousands of simulations based on broad assumptions, like average daily temperature and standard deviation. When Monte Carlo is used to forecast August temperatures in the city of Toronto, for instance, it provides an impractical range of outcomes from -25°C (-13°F) to 53°C (127°F), with an expected temperature of 13°C (55°F)! Far from useful. That is because the Monte Carlo simulation does not account for factors like the most recent daily temperature and seasonality.

Advisers need a more sophisticated approach to scenario analysis in long-term wealth planning. In our next article, we will discuss how a more advanced portfolio projection model can give advisers and clients the superior insights they need in today’s uncertain markets.

Read the original article here.