The WSJ had a piece this weekend on Monte Carlo simulations, and the fact that now (surprise!!) many people believe the simulations understate the risks of real world investing.
Some portions:
As Bernstein has pointed out in the past, there's systemic risk (including the fairly regular collapse of governments and economic systems) that overwhelms some of the miniscule failure rates predicted by these MC simulators.
Also, too bad they didn't touch on FIRECalc.
Some portions:
It's good that MC simulations are getting more attention. Still, this article didn't do a good job of explaining some of the common weaknesses (esp capturing complex interdependencies among asset classes and adjacent-year returns, and instead treating them as independent. This significantly understates risk).If one had asked a financial adviser 18 months ago for retirement-planning guidance, there is a good chance he would have run a "Monte Carlo" simulation. . .
But there is little chance your Monte Carlo simulation would have highlighted a scenario like the market slide just seen. Though these tools typically run a portfolio through hundreds or thousands of potential market scenarios, they often assign minuscule odds to extreme market events. Yet these extreme events seem to be happening more often.
. . .
Monte Carlo simulation has wide appeal, and is used in online tools offered by firms like Fidelity Investments and by independent retirement planners.
. . .
Many providers of the tools argue that it is a significant improvement over the traditional retirement-planning approach, which typically involves assuming some set market return, say 8% for U.S. stocks, year after year, an assumption considered unrealistic by academics and financial pros. The questions about Monte Carlo tools reflect broader concerns about mathematical models for gauging portfolio risks. . . .
These models were supposed to help quantify and manage the risks of mortgage-backed securities, credit-default swaps and other complex instruments. But given the events of the past couple of years, it appears that the models often gave big institutions, as well as small investors, a false sense of security.
. . . .
Now, some investors have decided that if risk can't be accurately measured, they will just have to play it safe. . . .
Some financial advisers are equally skeptical. "I take whatever probability of failure that comes out of your Monte Carlo simulation and add 20 percentage points," said William J. Bernstein, author of "The Four Pillars of Investing."
Critics emphasize that the problem isn't Monte Carlo itself, but the assumptions that go into it. Since no standard approach exists, one user might plug in a range of assumptions on interest rates, inflation or volatility that is different from another user.
Also controversial is that many Monte Carlo simulations assume that market returns fall along a bell-curve-shaped distribution. . . .
"In a bell-shaped curve the probability of getting one of these extreme outcomes we're seeing is basically zero," said Paul Kaplan, vice president of quantitative research at Morningstar Inc.
. . . .
While a bell-curve model indicates there is almost no chance of a greater than 13% monthly decline in the Standard & Poor's 500-stock index, such declines have happened at least 10 times since 1926, according to a report by Mr. Kaplan.
Some Monte Carlo models, like the one used by Financial Engines, assign higher odds to extreme market events than the bell-curve distributions. . . .
Some firms are considering revising Monte Carlo models to reflect a world where big market swings happen more often. Morningstar last year tweaked its asset-allocation software offered to institutional investors, allowing users to choose a bell-curve-shaped distribution or a "fat-tailed" distribution, which assigns higher probabilities to extreme market events. The company is exploring using this model in more products, Mr. Kaplan said.
Laurence Kotlikoff, a Boston University economics professor who developed the ESPlanner financial-planning software, and Richard Fullmer, senior portfolio strategist at Russell Investments, said they also are considering offering clients Monte Carlo scenarios that incorporate fatter-tailed distributions. . . .
Some industry participants also are trying to set standards that could help Monte Carlo tools more accurately capture extreme market events. The Retirement Income Industry Association in 2007 issued a set of principles noting that the calculators should run a large number of scenarios.
The ideal models run tens of thousands or hundreds of thousands of scenarios, which help gauge extreme events at the tail end of the distribution, observers said. Yet some tools run only 1,000 scenarios or just several hundred.
As Bernstein has pointed out in the past, there's systemic risk (including the fairly regular collapse of governments and economic systems) that overwhelms some of the miniscule failure rates predicted by these MC simulators.
Also, too bad they didn't touch on FIRECalc.