Home » Research Blog » The 1×1 of risk perception measures

The 1×1 of risk perception measures

There are two reasons why macro traders watch risk perceptions. First, sudden spikes often trigger subsequent flows and macroeconomic change. Second, implausibly high or low values indicate risk premium opportunities or setback risks. Key types of risk and uncertainty measures include [1] keyword-based newspaper article counts that measure policy and geopolitical uncertainty, [2] survey-based economic forecast discrepancies, [3] asset price-based measures of fear and uncertainty and [4] derivatives-implied cost of hedging against directional risk and price volatility.

Datta, Deepa, Juan M. Londono, Bo Sun, Daniel Beltran, Thiago Ferreira, Matteo Iacoviello, Mohammad R. Jahan- Parvar, Canlin Li, Marius Rodriguez, and John Rogers (2017). “Taxonomy of Global Risk, Uncertainty, and Volatility Measures”. International Finance Discussion Papers 1216.

The post ties in with SRSV’s lectures on implicit subsidies (particularly the part on volatility markets) and setback risk (particularly the part on exit risk).
The below are excerpts from the paper. Emphasis and cursive text have been added.

News-based measures

Economic policy uncertainty

One of the most widely used…indicators of uncertainty is the economic policy uncertainty (EPU) index developed by Baker, Bloom, and Davis (2016). For the United States, the EPU index is constructed from three components: The first component quantifies policy related uncertainty by searching the archives of 10 major U.S. newspapers for articles that contain terms related to EPU; the second component gauges uncertainty regarding the federal tax code by counting the number of federal tax code provisions set to expire in future years; and the third component measures disagreement among economic forecasters as an indication of uncertainty. EPU indexes are constructed for almost 20 other countries or country aggregates but are based on only…newspaper articles regarding policy uncertainty.”

“For the United States, the news component of the EPU index is constructed by counting the number of articles in 10 leading U.S. newspapers that contain the [relevant key] words…[The authors] scale the raw monthly counts for each newspaper by the total number of articles in that newspaper and in that month to produce a monthly EPU series for each newspaper. They scale each newspaper-level series to ensure that each has a unit standard deviation…and then take the average of these 10 monthly series.”

“The EPU index shows clear spikes around events and developments that may affect uncertainty, such as the Gulf wars, presidential elections, the terrorist attacks on September 11, 2001, the stimulus debate in early 2008, the Lehman Brothers bankruptcy and the subsequent Troubled Asset Relief Program.”

“Researchers…show that policy uncertainty can affect the economy and asset prices…Uncertainty seems to reduce investment and employment, especially in firms that are more dependent on government spending…Policy uncertainty can increase stock volatility, stock co-movement, and equity premiums as well as default risk and credit spreads and financial intermediation costs.”

Monetary policy uncertainty

“The same text-based methodology [is applied] to construct an index of monetary policy uncertainty (MPU) by tracking the frequency of newspaper articles related to MPU….by searching for keywords related to monetary policy from the following three sets: [1] ‘uncertainty’ or ‘uncertain’; [2] ‘monetary policy(ies),’ ‘interest rate(s),’ ’federal fund(s) rate,’ or ‘fed fund(s) rate’; and (3) ‘Federal Reserve,’ ‘the Fed,’ ‘Federal Open Market Committee,’ or ‘FOMC.’”

“The timing of…spikes…shows the ability of the index to capture the ex post and ex ante uncertainty of different Federal Open Market Committee decisions. Major macroeconomic events… also move the index…U.S. output and inflation fall and credit costs become tighter following positive shocks to the MPU index.”

Geopolitical risk

“Caldara and Iacoviello (2016) construct an index that measures geopolitical risk (GPR) based on a tally of newspaper stories that contain a fairly broad set of terms related to geopolitical tensions. The GPR index measures the risk associated with events, such as wars, political tensions, and terrorist acts that affect the normal course of domestic politics and international relations…The GPR index is constructed by counting the occurrence of words related to geopolitical tensions in leading international newspapers. In particular, the GPR index reflects automated text searches in the electronic archives of 11 national and international newspapers for articles that contain several keywords, including ‘risk of war,’ ‘terrorist threats,’ and ‘geopolitical tensions.’”

Increased geopolitical risk leads to declines in real activity and is associated with increases in the VIX, lower oil prices, and higher corporate credit spreads.”

Forecast uncertainty-based measures

Survey-based uncertainty

“Scotti (2016) uses macroeconomic news and survey forecasts to construct an ex post, realized measure of uncertainty about the state of the economy…based on weighted averages of economic data surprises, which are measured by examining deviations of recent economic data releases from consensus expectations. The index is calculated using the weighted average of the squared surprises from a sample of macroeconomic variables where the surprises are measured as the differences between the actual data and the median Bloomberg forecasts an hour before the data release. A dynamic factor model is employed to estimate monthly business condition indexes and compute the weights representing the contribution of the economic indicators to these business condition indexes. Those weights are then used to average the squared surprises to construct the uncertainty index.”

“A higher uncertainty index is associated with lower real activity.”

Econometric forecast uncertainty

“Jurado, Ludvigson, and Ng (2015) construct indexes of macroeconomic uncertainty using the uncertainty around objective statistical forecasts for hundreds of economic series. They use a monthly dataset comprising the information from hundreds of macroeconomic indicators to construct direct econometric estimates of time-varying macroeconomic uncertainty. Their key insight is that macroeconomic uncertainty can be constructed as an appropriately weighted average of the forecast error variance of all the included macroeconomic indicators.”

“Relative to…EPU measures, the Jurado, Ludvigson, and Ng [JLN] index  is more persistent and exhibits fewer spikes. Moreover, this measure of uncertainty doubles during recessions…Increases in the JLN index are associated with large declines in real activity.”

Market price-based measures

Realized volatility

“Realized volatility (RV)— defined as the scaled sum of squared daily returns—offers a nonparametric alternative to traditional parametric volatility measures. RV estimators are feasible in multivariate applications and can separate the volatility contributions of jumps from continuous changes in asset prices. In addition, they are flexible and easy to implement.”

“While RV-style measures have proved successful in predicting future volatility, their ability to predict financial returns is somewhat limited.”

Distribution of equity returns

“Higher-order moments of the cross-sectional distribution of stock returns can also provide useful information about the economic cycle. In particular, Ferreira (2017) focuses on the skewness of the distribution of log returns across firms and assesses the balance between upside and downside risks. The author shows that financial skewness—the skewness of the cross-sectional distribution of stock returns of financial firms—not only closely tracks the business cycle, but predicts economic activity better than many well-known bond spreads and other cross-sectional moments.”

“[There is] evidence supporting the interpretation of financial skewness as a measure of the balance of risks across economy-wide investment projects as well as vulnerabilities of the financial sector. Moreover, estimates [suggest] that shocks to financial skewness have sizable effects on economic activity, credit growth, and corporate credit spreads.”

Risk-on/off index

“The global risk-on/risk-off index (ROI) is a proxy for risk appetite [changes] in global financial markets. It is constructed from changes in a variety of asset prices, including those of bonds, equities, exchange rates, and gold. During ‘risk-on’ periods, investors typically reallocate their portfolios away from safe-haven assets (for example, Treasury securities, bunds, gold) toward riskier assets (for example, equities, high-yield bonds, emerging market bonds). The ROI measures the extent to which these portfolio shifts lead to increases in the prices of risky assets relative to those of safe assets. Positive values indicate risk-on days, while negative values indicate ‘risk-off’ days. The ROI is global in scope, covers the major asset classes, captures high-frequency changes in risk appetite, and is intuitive and easy to compute.”

The ROI is an equally weighted sum of changes in 15 assets or indexes, with their respective signs aligned such that positive changes are consistent with risk-on behavior and negative changes with risk-off behavior. Changes are scaled by their respective historical standard deviations.”

“[The first figure below] shows the 10-day moving average of the daily ROI between January 2007 and October 2017…[The second figure below] shows the 10-day moving average of the 120-day version of the ROI between July 2002 and October 2017. This measure is useful for gauging gradual improvement or deterioration in risk appetite over longer stretches of time…Extremely bullish risk sentiment is often associated with risky asset prices overshooting their long-run trends. Sudden deteriorations in risk appetite have been associated with contagion in global financial markets.”

Derivatives-based measures

Options-implied equity volatility

Option-implied volatilities are available for headline equity indexes of the following countries: the United States (S&P 500), Germany (DAX 30), Japan (Nikkei 225), the United Kingdom (FTSE 100), Switzerland (SMI), the Netherlands (AEX 25), and France (CAC 100). Implied-volatility is also available for the euro area (Euro Stoxx 50).”

“Although there has been extensive research on the usefulness of the VIX as a tool to monitor equity and other financial asset markets, its informational content is often misunderstood…The VIX and equivalent measures for foreign equity markets are formally defined as the risk-neutral expectation of the volatility of the equity index over the next 30 days…This relatively short horizon implies that this index likely does not capture expected volatility beyond the 30-day horizon.”

“Variance swap contracts with maturities ranging from one month to two years are traded as over-the-counter assets. Variance swap contracts allow us to examine the expectations of changes in market volatility beyond the 30 days captured by the VIX.”

“Option-implied indexes are highly correlated across countries and tend to spike simultaneously…The VIX has been shown to be a useful predictor of future realized volatility.”

Variance risk premium

“The variance risk premium is a measure of the compensation investors demand for bearing volatility risk, or, in other words, a measure of investor preference for volatility. Formally, it is defined as the difference between a risk-neutral measure of expected variance (for example, the squared value of VIX) and a physical measure of expected realized variance. The variance risk premium is often used as a time-varying and state-dependent measure of risk aversion. This measure is also used as a gauge of macroeconomic risk compensation.”

“Empirically, it has been shown that the variance risk premium is one of the most successful short-term (between one month and one quarter ahead) predictors of returns across a broad range of financial assets…the variance risk is highly correlated across countries, which suggests that there is a common or global component in variance risk premiums.”

“In practice, upside and downside variance risk premiums have different properties. The former represents market participants’ interest in being exposed to upside risk and the higher gains it generates, while the latter represents the premium that market participants demand as compensation to bear downside risk and the possible losses it may generate. Upside and downside variance risk premiums may even have different signs. Because the variance risk premium is, by construction, the sum of these two risk premiums, a low variance risk premium may mean calm markets or uncertainty in both positive and negative directions of similar magnitudes.”

The difference between upside and downside variance risk premiums, also known as the signed-jump premium, is a measure of the skewness risk premium. This measure…is a better reflection of the direction of uncertainty and market participant concerns about tail risks.”

Option-implied equity distributions

“Options on equity indexes, unlike those on individual stocks, are fairly liquid and available for a wide range of strikes and time horizons, which facilitates the computation of option-implied probability distributions [allowing use of] a semiparametric method used to calculate option-implied probability distributions for headline equity indexes…This semiparametric method usually yields smooth option-implied distributions that are easy to interpret and are, therefore, suitable as a policy tool to monitor equity markets.”

“[The figure below] shows the cost of insurance against 10 percent changes in the S&P 500 index for the next 30 days (upper panel) and for the next 3 months (lower panel) as calculated from the option-implied probability distribution of the S&P 500 index. This measure is calculated as the interpolated price of a binary option that pays $1 if the price of the index declines or rises beyond 10 percent within the next 30 days or 3 months, and otherwise pays zero. The cost of insurance against 10 percent changes tends to increase in episodes of high uncertainty…In the short term (30 days), investors are usually willing to pay more to hedge a drop in prices than to hedge a potential increase.”

Option-implied interest rate distributions

“Risk-neutral distributions for interest rates can be either continuous or discrete and are typically calculated from interest rate options, such as federal funds futures options, Eurodollar futures options, Treasury futures options, interest rate caps and floors, or swaptions. Risk-neutral moments can be then calculated either by using these risk-neutral distributions or directly by using other distribution-free methods.”

“[The figure below] shows the risk-neutral distribution of the London interbank offered rate (LIBOR). The distribution plots the probabilities associated with various outcomes for the three-month LIBOR, two years ahead, for a horizon of three months.”

Option-implied FX distributions

“The foreign exchange (FX) derivative market is one of the largest and most liquid in the world… The most common strategies are risk reversals…Risk reversals provide information about the cost of insurance against the depreciation of a currency relative to the cost of insurance against the appreciation of such currency. Specifically, a long position in a risk reversal is equivalent to purchasing a call option and selling a put option on a single bilateral exchange rate. Thus, this strategy protects the investor against an unfavorable drop in the exchange rate (for example, a drop in the dollar with respect to another currency for an exporter located in the United States) but limits investor gains if there is a favorable increase in the exchange rate.”

[The figure below] shows an example relating the cost of insurance derived from risk reversals to the uncertainty of some economic events.


Related articles