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The Forecast for Risk in 2013
By Geoff Considine
January 8, 2013


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With the new year upon us, pundits are issuing their forecasts of market returns for 2013 and beyond.  But returns don’t occur in a vacuum – meeting clients’ goals requires an asset allocation that appropriately balances return and risk.  So what follows are my predictions for risk across major asset classes, based on a theoretically sound approach that has proven to be reliable in the past.

A number of big uncertainties loom on the horizon.  Will economic growth rise to its historical average?  Will unemployment drop?  Will the Eurozone get back on track?  Will emerging economies decouple from the over-leveraged developed economies? 

Risk lurks in all these questions. But we can get a sense of the magnitudes of those risks by considering different sectors in the options markets. 

I will reveal the forecast for risk and volatility for key asset classes – along with the likelihood of a catastrophic, black-swan event – but first let’s review the theoretical basis for using options as a tool to project risk and the key assumptions upon which my methodology depends.

Theoretical background

Option prices are primarily determined by the market’s consensus view of future volatility, akin to how expected future earnings determine the price of a stock.  It’s unsurprising, then, that multiple studies have shown option prices to be the best predictors of future volatility. 

In early 2007, for example, the options markets reflected a very clear consensus view that market volatility would increase dramatically, contradicting the widespread belief that the markets had undergone a “great moderation” of risk – a prescient warning of what was to come in 2008. 

There’s a simple reason why option markets offer such accurate predictions. The fact that someone is willing to sell you risk protection (in the form of an option) reflects the conviction of the seller.  In the same way that purveyors of hurricane insurance have the strongest incentives to forecast storm activity accurately, the market-makers in options have a strong incentive to estimate future risk as well as possible.  For a robust market in options to persist, market makers cannot systematically misprice the options that they sell; if their predictions weren’t accurate, the market would not exist.

Implied volatility is not a perfect forecast, of course, but it is clearly the best predictor of risk we have.

The asset classes

I surveyed options that expire in January 2014 for a number of key asset classes.

For bonds, I focused on two types: long-dated Treasury bonds and high-yield bonds – because they provide pure exposure to interest-rate risk and default risk, respectively.  Put options on those two asset classes dictate the price of insurance against losses due to corporate defaults or a rise in interest rates.
For equities, I examined options on the S&P 500 index (via the ETF SPY), the EAFE index (EFA), emerging markets (EEM), and two of the largest individual emerging markets, Brazil (EWZ) and China (FXI). 

For alternative asset classes, I examined options on gold (GLD) and REITs (IYR). 

Baseline volatility projections

I’ll compare historical volatility to the implied volatility for options that are at-the-money (ATM).  Those options have a strike price equal to the current price of the asset.  An ATM put option provides the buyer with protection against any decline in price in that asset class from today until expiration. 

The third column in the table below shows the trailing three-year volatilities for each asset class I identified above, while the fourth column shows the implied volatility for ATM put options on the ETFs that track them.  Higher implied volatility corresponds to greater risk that the price will decline. 

Historical, Projected, and Implied Volatility (Put Implied Volatilities are from Morningstar)

Asset Class

Ticker

Trailing 3-Year Volatility

Put Implied Volatility

QPP Projected Volatility

S&P 500 Index

SPY

15%

18%

18%

EAFE Index

EFA

20%

21%

22%

MSCI Emerging Mkt Index

EEM

24%

22%

25%

Long-Term Treasury Bonds

TLT

16%

16%

19%

High-Yield Bond

HYG

10%

12%

11%

Brazil Stocks

EWZ

29%

24%

29%

REITs

ICF

19%

20%

23%

China Stocks

FXI

24%

25%

28%

Gold

GLD

19%

17%

23%

 

For the S&P 500, the options market is projecting volatility over the next year that is somewhat higher than we have experienced, on average, over the past three years: The trailing three-year volatility for the S&P 500 (SPY) is approximately 15%, while the put-implied volatility (PIV) is 18%.

To generate my predictions, I calibrated my Monte Carlo simulation, Quantext Portfolio Planner (QPP), to assume future volatility for the S&P 500 will equal the implied volatility of the current ATM put options on SPY.  QPP then projected the future volatility of all other asset classes based on a combination of the S&P’s expected volatility, historical volatilities, and correlation data.  The final column in the table above shows those projections for the other asset classes. 

One striking feature of this table is that trailing and forward risk levels are remarkably similar.  Contrast this with the situation just before the 2008 crash – when projected volatility and PIV were both much higher than trailing volatility – and with the situation in late 2008, when the investors were exceptionally risk averse and implied volatility far exceeded QPP’s projections.

For purposes of portfolio planning, I assume the worst-case annual returns for an asset class to be roughly twice the expected volatility – two standard deviations below current returns.  The worst-case scenario for emerging market stocks on this basis is a return of -44% to -50%, while the worst case for the S&P 500 is perhaps -36% over the next year.  These returns are consistent with the outcomes investors experienced in 2008, when EEM lost 49% and SPY lost 37%.  Similarly, EWZ could lose 48% to 58% by this measure; it lost 55% in 2008.  I don’t foresee a decline of those magnitudes, but the relationship between projected volatility and maximum loss potential has been reliable in the past.  Note that this is a heuristic for planning, and is not part of QPP’s calculations.

For more detail on this phenomenon, see Appendix A, a short case study that looks at how well this approach to predicting volatility across a range of asset classes performed for the three-year period from 2010-2012. 

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