Record Low Rates and Volatility Muddle Financial Models

Alarm bells have been ringing over the summer about remarkably low levels of volatility — a key input in many common investment models — across global markets. Most recently, the Bank for International Settlements (BIS), in its annual report, expressed surprise at the financial market exuberance over the past year. According to the BIS, “Volatility in equity, fixed income, and foreign exchange markets has sagged to historical lows. Obviously, market participants are pricing in hardly any risks.”

One contributory issue raised by the BIS is the continued easy monetary policy of central banks, notably the Federal Reserve. But does the Fed actually control the interest rates that have been buoying markets? In a new study, Nobel Laureate Eugene Fama, architect of the efficient market hypothesis, considered the degree of control that the Fed exerts on interest rates, both short and long term, finding that although short-term control of rates is under the Fed’s thumb, results for longer-term rates are less conclusive. Simply put, the (ever-efficient) market, rather than the Fed, is in the driver’s seat for long-term rate setting. Understanding the Fed’s power versus market forces could better inform trading decisions and improve forecasting models.

As long as systematic attempts to understand and model markets have been around, a process cemented by the hold such theories gained on business and economic faculties, so their assumptions and usability have come under attack. Investors trying to use variants of modern portfolio theory (MPT) are often confounded by the input parameters that need to be fed into the model. Traditionally, investors input not only their personal expected returns for securities (or a variant derived from market prices) but also estimates of volatility for each security as well as estimations of the covariance between pairs of securities, most often based mathematically on very short-term price history. Each of these three inputs can fluctuate remarkably over time depending on the particular time periods selected.

The bottom line for most investors, encapsulated in such theories as the capital asset pricing model (CAPM), is that a premium return is needed to justify investing in risky assets that increase vulnerability to higher losses. But such risk measures as volatility and beta treat downside and upside returns the same. To tackle this issue, the authors of a recent study defined a new left-tail risk measure, called “excess conditional value at risk” (ECVaR), and examined whether tail risk is rewarded with higher risk-adjusted returns. Higher returns compensate for tail risk in US and non-US equity mutual funds. In contrast, volatility is not compensated on a risk-adjusted basis in either market.

In another volatility study by a trio of researchers from the New York University Stern School of Business and Morgan Stanley, which assessed distressed corporate bond portfolios, the authors concluded that investors are better off using a buy-and-hold strategy and investing in low-volatility distressed securities. When the portfolios are updated continuously as securities become distressed, the lowest-volatility portfolio outperforms because of lower default rates and higher terminal values. In theory, new data are absorbed by all markets simultaneously and incorporated into asset prices immediately. The authors of another study investigated the relationships between stock and bond market returns to assess whether a price movement in corporate bonds can predict price changes for corresponding stocks. They showed that stock markets react with a lag when information is negative, contrary to theory. Given such theoretical irregularities, perhaps considering company-specific risk in isolation, or even market risk, isn’t always the best explanatory approach. Maybe the multifactor approach is a more practical methodology.

In a new study titled “The Puzzle of Index Option Returns,” the authors tested multifactor pricing models using S&P 500 Index put and call option portfolios. They found that just four crisis-related factors, among the many they considered, can sufficiently explain index option returns. Nonetheless, the multifactor approach shares several famous controversies with the other models. It relies on recent historical data, which may contain artificially low volatility readings, and it applies linear thinking, which assumes a uniform link between individual stock prices and the market captured in the chosen factors.

Not quite a model, but certainly an individual investor’s favorite investing yardstick, the dividend–price ratio has the ability to forecast future US returns but not future dividend growth, according to recent research. The study found that for every 1 percentage point rise in the dividend yield, market prices rise by more than 5 percentage points. This relationship is statistically insignificant in many other countries, such as Germany, Italy, and Sweden.

“To gain an edge, investment analysts need to look at data differently,” says Nicholas J. Colas, speaking at a recent CFA Institute conference in New York. “The best approach is to focus on fewer and better data elements that are uncorrelated with what other market participants analyze.” More data are not better data, according to Colas. Instead, more imaginative use of data is the way to find the eternal truths. Successful analysts are imaginative analysts who master new fields of knowledge and new technologies to find superior data using unconventional tools, such as Google AutoFill and Google Trends. Perhaps imagination and a return to investing using old-fashioned ratios can trump malfunctioning investment models for a while.

Recent CFA Digest summaries and related resources of interest to readers are summarized below:

• Does the Fed Control Interest Rates?: Architect of the efficient market hypothesis and a Nobel Laureate, the author investigates the extent to which the Fed’s monetary policy controls short- and long-term interest rates. Evidence suggests that Fed actions with respect to its target rate have little impact on long-term rates, and there is substantial uncertainty about the Fed’s control of short-term rates.
• Volatility versus Tail Risk: Which One Is Compensated in Equity Funds?: Higher returns compensate for tail risk in US and non-US equity mutual funds. In contrast, volatility is not compensated on a risk-adjusted basis in either market. The authors introduce a new left-tail risk measure called “excess conditional value at risk.” The tail risk premium is estimated by using regressions that account for Carhart’s four factors and that are validated by Fama–MacBeth regressions on volatility and tail risk.
• The Return/Volatility Trade-Off of Distressed Corporate Debt Portfolios: According to the CAPM, riskier securities should yield higher returns. The authors assess distressed corporate bond portfolios and conclude that the CAPM holds under a theoretical time-independent framework in that market when overall volatility is not controlled for and in a more practical time-dependent framework where investors are assumed to possess market-timing skills. Investors are better off, however, using a buy-and-hold strategy and investing in low-volatility distressed securities.
• What Does the Corporate Bond Market Know?: Capital markets incorporate new information into asset prices with a certain lag. The authors describe the circumstances under which the bond market could anticipate a more liquid stock market. The past performance of a firm’s most liquid bond can be used to predict price changes of the corresponding stock.
• The Puzzle of Index Option Returns: To test multifactor pricing models, the authors use S&P 500 Index put and call option portfolios. They reduce the skewness and variance of monthly portfolio returns to approximate a near-normal distribution to which they can then apply linear factor models. They find that just four crisis-related factors, among the many they consider, can sufficiently explain index option returns.
• The Devil’s in the Data: To gain an edge, investment analysts need to look at data differently. The best approach is to focus on fewer and better data elements that are uncorrelated with what other market participants analyze. Investors should also try to find their data in off-the-grid economic indicators rather than using traditional research methodologies. By following this approach, they can better connect with society to benefit from early identification of data that optimize their decision making and investment outcomes.
• Dividend-Price Ratios and Stock Returns: International Evidence: In the United States, the dividend–price ratio has the ability to forecast future returns but not future dividend growth. For every 1 percentage point rise in dividend yield, market prices rise by more than 5 percentage points. This relationship is statistically insignificant in other countries, such as Germany, Italy, and Sweden. Empirical research has revealed that the dividend–price ratio can forecast future returns because the ratio is high when expected returns are likely to be high and vice versa. But as the author hypothesized in previous work, the relationship is dependent on the volatility of real dividend growth.

Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.

Photo credit: ©iStockphoto.com/ARTQU