Default Spread Research Articles

Assessing forestry-related assets with the intertemporal capital asset pricing model

The intertemporal capital asset pricing model is used to assess the risk–return relationship between forestry-related assets and innovations in state variables using quarterly returns from 1988Q1 to 2011Q4. Market excess returns and innovations in the small-minus-big and high-minus-low factors, interest rate, term spread, default spread and aggregate consumption explain about 80% of the variation in cross-sectional returns of 16 forestry-related assets. Beta loadings on innovations in high-minus-low, interest rate and term spread induce significant risk premiums, and should be priced to determine the cross-sectional expected returns of the forestry-related assets. In general, average excess returns of the forestry-related assets decrease from the period of 1988Q1–1999Q4 to the period of 2000Q1–2011Q4. Significant positive excess returns are obtained in the first sub-period for private- and public-equity timberland assets but not in the second sub-period. Insignificant excess returns are obtained for forest products and timber products in the whole sample period. The results are robust to different specification tests.

A fear index to predict oil futures returns

This paper evaluates the predictability of WTI light sweet crude oil futures by using the variance risk premium, i.e. the difference between model-free measures of implied and realized volatilities. Additional regressors known for their ability to explain crude oil futures prices are also considered, capturing macroeconomic, financial and oil-specific influences. The results indicate that the explanatory power of the (negative) variance risk premium on oil excess returns is particularly strong (up to 25% for the adjusted Rsquared across our regressions). It complements other financial (e.g. default spread) and oil-specific (e.g. US oil stocks) factors highlighted in previous literature.

Interest rate, size and book-to-market effects in Australian financial firms

The Fama–French three-factor model (1993) has been extensively used to study the pricing of nonfinancial stocks. This study provides the first examination of the pricing of Australian financial stocks using the Fama–French framework. The four-factor model (market, size, book-to-market and momentum) augmented with the level, slope and curvature of the interest rate term structure is used to examine the pricing of Australian financial stocks. The interest rate factors have not been previously considered for pricing Australian stocks within the Fama–French framework. Consistent with US evidence, we use a system-based estimation to show that the size and book-to-market factors are not priced in the cross section of the equity returns of Australian financial stocks. Momentum and term spread are priced in the equity returns of both financial and nonfinancial stocks. These findings are robust to the inclusion of control variables such as default spread, the inflation rate and a dummy variable for the global financial crisis.

Dynamic relationship between Turkey and European countries during the global financial crisis

This study examines the relationship between time-varying risk perceptions of investors towards major European countries and Turkey. In that manner, we first obtain the dynamic conditional correlations between the credit default spreads (CDSs) of Turkey and 13 European countries from September 2004 to April 2013. Next, we endogenously detect the shifts in these dynamic correlation levels using a penalized contrast methodology. Accordingly, we find positive level shifts in all correlations following the US crisis. The upward trend in all CDS correlations holds during the eurozone debt crisis, but positive changes in correlations are not flagged as level shifts by the model, except in a few cases. The results suggest that Turkey is not immune to global financial conditions and there is integration between Turkey and the major European economies in terms of risk perception after the global financial crisis.

Fama French factors and US stock return predictability

This article investigates whether the HML, SMB along with the long-term reversal and the momentum factors exhibit both in-sample and out-of-sample forecasting ability for the US stock returns. Our findings suggest that these factors contain significantly more information for future stock market returns than the typically employed financial variables. We also go one step further and test whether these variables can proxy for the aforementioned factors and find that the default spread and to a lesser extent the term spread contain important information for the evolution of the factors examined. Finally, we show that appropriate decompositions of the factors in their size and value components can enhance predictability.

The effect of interest rate volatility and equity volatility on corporate bond yield spreads: A comparison of noncallables and callables

This research investigates the impact of interest rate volatility upon corporate bond yield spreads. We first consider the impact of interest rate volatility upon noncallable bond spreads. Because greater interest rate volatility likely increases the volatility of the firm's debt, we hypothesize that the relation will be positive. Given that we do find a positive relation, we thus investigate whether the positive effect of interest rate volatility on yield spreads is stronger or weaker for callable bonds. We find that the effect is weaker for callable bonds. This result indicates that there is a negative relation between default spreads and call spreads, which is consistent with the theory of Acharya and Carpenter (2002), but in contrast to the theory of King (2002). Furthermore, our results for the relationship between equity volatility and yield spread tend to support Acharya and Carpenter (2002) more than King (2002).

Viewpoint: Boosting Recessions

AbstractThis paper explores the effectiveness of boosting, often regarded as the state of the art classification tool, in giving warning signals of recessions 3, 6, and 12 months ahead. Boosting is used to screen as many as 1,500 potentially relevant predictors consisting of 132 real and financial time series and their lags. Estimation over the full sample 1961:1–2011:12 finds that there are fewer than 10 important predictors and the identity of these variables changes with the forecast horizon. There is a distinct difference in the size and composition of the relevant predictor set before and after mid‐1980. Rolling window estimation reveals that the importance of the term and default spreads are recession specific. The Aaa spread is the most robust predictor of recessions three and 6 months ahead, while the risky bond and 5‐year spreads are important for 12 months ahead predictions. Certain employment variables have predictive power for the two most recent recessions when the interest rate spreads were uninformative. Warning signals for the post‐1990 recessions have been sporadic and easy to miss. The results underscore the challenge that changing characteristics of business cycles pose for predicting recessions.

Nonparametric testing for long-horizon predictability with persistent covariates

We propose a testing procedure for long-horizon predictability via kernel-based nonparametric estimators of long-run covariances between multiperiod returns and persistent covariates. Asymptotic properties of the proposed tests are studied. As for implementation of the test, sieve bootstrap methods are employed to obtain reasonable approximation to the sample distribution of the test statistics. Monte Carlo simulations are conducted to verify the theoretical conjecture. Empirical analysis, using US monthly data from 1929 to 2011, are presented for testing stock return predictability of some forecasting financial variables. Long-term interest rates, unlike default spreads or price-earning ration, are found to show some forecasting power.

The Market Timing Power of Moving Averages: Evidence from US REITs and REIT Indexes

AbstractI present evidence that a moving average (MA) trading strategy dominates buying and holding the underlying asset in a mean‐variance sense using monthly returns of value‐weighted and equal‐weighted US REIT indexes over the period January 1980 until December 2010. The abnormal returns are largely insensitive to the four Carhart factors and produce economically and statistically significant alphas of between 10 and 15% per year after transaction costs. This performance is robust to different lags of the MA and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at‐the‐money protective put strategy relative to the underlying portfolio. The lagged signal to switch has substantial predictive power over the subsequent return of the REIT index. The MA strategy avoids the sharp downturn at the beginning of 2008 and substantially outperforms the cumulative returns of the buy‐and‐hold strategy using all of the 20 REIT indexes. The results from applying the MA strategy with 274 individual REITs largely corroborate the findings for the REIT indexes.

A mean/variance approach to long-term fixed-income portfolio allocation

Long-term investments in bonds offer known returns, but with risks corresponding to defaults of the underwriters. The excess return for a risky bond is measured by the spread between the expected yield and the risk-free rate. Similarly, the risk can be expressed in the form of a default spread, measuring the difference between the yield when no default occurs and the expected yield. For zero-coupon bonds and for actual market data, the default spread is proportional to the probability of default per year. The analysis of market data shows that the yield spread scales as the square root of the default spread. This relation expresses the risk premium over the risk-free rate that the bond market offers, similarly to the risk premium for equities. With these measures for risk and return, an optimal bond allocation scheme can be built following a mean/variance utility function. Straightforward computations allow us to obtain the optimal portfolio, depending on a pre-set risk-aversion level. As for equities, the optimal portfolio is a linear combination of one risk-free bond and a risky portfolio. Using the scaling law for the default spread allows us to obtain simple expressions for the value, yield and risk of the optimal portfolio.

The Default Spread Model: A Practical Method for Measuring Conditional Equity Risk Premium

Until the financial crisis of 2008, most valuation practitioners tended to use a static equity risk premium usually based on historical data. The crisis of 2008 brought with it a “flight to safety” phenomenon, which resulted in record low risk-free rates. Consequently, valuation practitioners faced the odd situation of a declining cost of equity in the context of a significantly higher risk economic environment. To resolve this dilemma, practitioners have started to accept the notion that the equity risk premium (ERP) changes over time. This notion is well supported by academic research, which shows a strong relationship between the ERP and economic cycles. However, as valuators, we still lack consistency in the measurement of a conditional ERP. As a group, we are in need of a practical, reliable, and objective method that would enable us to measure changes in ERP in a somewhat consistent manner. This article attempts to do so by presenting a method to measure the conditional ERP that is simple, objective, and, when tested historically, provides reasonable results. Our method is premised on the well-accepted theory that changes in default spreads correspond to changes in ERP.

Implied Economic Risk Premiums

The literature on economic risk premiums has largely been based on ex post returns. I construct implied ex ante risk premiums for a list of economic factors, using direct expected returns estimates --- i.e., the implied costs of capital (ICCs). For most factors, ICCs support significant nonzero average economic risk premiums which ex post returns fail to uncover, implying many factors are actually priced from an ex ante perspective. The implied factor risk premiums are powerful predictors for the future ex post returns of mimicking portfolios for many economic factors (e.g., value and size factors, default spread, inflation, the growth rate of labor income, and one-month T-bill real return), both in sample and out of sample. Time-varying ex ante economic risk premiums are significantly suggested by ICCs.

A Fear Index to Predict Oil Futures Returns

This paper evaluates the predictability of WTI light sweet crude oil futures by using the variance risk premium, i.e. the difference between model-free measures of implied and realized volatilities. Additional regressors known for their ability to explain crude oil futures prices are also considered, capturing macroeconomic, financial and oil-specific influences. The results indicate that the explanatory power of the (negative) variance risk premium on oil excess returns is particularly strong (up to 25% for the adjusted Rsquared across our regressions). It complements other financial (e.g. default spread) and oil-specific (e.g. US oil stocks) factors highlighted in previous literature.

The Systemic Risk of European Banks During the Financial and Sovereign Debt Crises

We propose a hypothetical distress insurance premium (DIP) as a measure of the European banking systemic risk, which integrates the characteristics of bank size, default probability, and interconnectedness. Based on this measure, the systemic risk of European banks reached its height in late 2011 around € 500 billion. We find that the sovereign default spread is the factor driving this heightened risk in the banking sector during the European debt crisis. The methodology can also be used to identify the individual contributions of over 50 major European banks to the systemic risk measure. This approach captures the large contribution of a number of systemically important European banks, but Italian and Spanish banks as a group have notably increased their systemic importance. We also find that bank-specific fundamentals predict the one-year-ahead systemic risk contribution of our sample of banks in an economically meaningful way.

A Fear Index to Predict Oil Futures Returns

This paper evaluates the predictability of WTI light sweet crude oil futures by using the variance risk premium, i.e. the difference between model-free measures of implied and realized volatilities. Additional regressors known for their ability to explain crude oil futures prices are also considered, capturing macroeconomic, financial and oil-specific influences. The results indicate that the explanatory power of the (negative) variance risk premium on oil excess returns is particularly strong (up to 25% for the adjusted R-squared across our regressions). It complements other financial (e.g. default spread) and oil-specific (e.g. US oil stocks) factors highlighted in previous literature.

Common Factors in the Performance of European Corporate Bonds – Evidence before and after the Financial Crisis

AbstractWe examine monthly excess returns for 23 Euro‐denominated corporate bond indices and propose a new specification for bond asset pricing models. Specifically, we separate level and slope components of term and default risk factors and examine liquidity risk. Our results suggest that level and slope risk factors, derived from complete interest rate and default spread term structures, significantly improve the explanatory power of the Fama and French (1993) 2‐factor model. We also demonstrate different sensitivities of risk factors before and after recent financial crisis. The results are robust to calendar seasonality and the consideration of equity market returns.

Parameter Estimation and Inference with Spatial Lags and Cointegration

We study dynamic panel data models where the long run outcome for a particular cross-section is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies several cointegrating relationships that are nonlinear in the coefficients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator and investigate its small sample distribution in a simulation study. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, firm specific and industry data. A closeness measure for firms is based on input-output matrices. Our estimates show that this particular form of spatial correlation of credit default spreads is substantial and highly significant.

Insurance Stock Returns Sensitivity to Changes in the Default Spread

Default spreads between corporate bonds and government bonds proxy the systematic risk of default. Using insurance monthly stock returns covering a period from 2000 through 2009, this paper investigates empirically the relationship between insurance company stock returns and default spreads. We find evidence of stock return sensitivity to changes in the default spread. The results are consistent for property- liability and life-health insurance companies.

The Conditional CAPM Explains the Value Premium

This paper proposes alternative specifications of the conditional CAPM with dynamic conditional beta and tests the models' performance in explaining the value premium for the period 1963-2011. The conditional alphas on the value-minus-growth portfolio are estimated to be economically and statistically insignificant, indicating superior performance of the conditional CAPM in explaining the value premium. The results also show that the dynamic conditional covariances of book-to-market portfolios with default spread, dividend yield, and unemployment rate are significant predictors of future returns, implying that default risk, dividend and unemployment related shocks contain systematic risks rewarded in the stock market and they can be viewed as state variables proxying for consumption and investment opportunities. Hence, the superior returns to value stocks represent compensation for bearing the risk of unfavorable shifts in the investment opportunity set along the lines of Merton's (1973) ICAPM.

The Market Timing Power of Moving Averages: Evidence from US REIT Indexes

I present evidence that a moving average trading strategy dominates buying and holding the underlying asset in a mean-variance sense using monthly returns of value-weighted and equal-weighted US REIT Indexes over the period January 1980 until December 2010. The abnormal returns are largely insensitive to the four Carhart (1997) factors and produce economically and statistically significant alphas of between 10% and 15% per year after transaction costs. This performance is robust to different lags of the moving average and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. The substantial market timing ability of the moving average strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at-the-money protective put strategy relative to the underlying portfolio. The moving average strategy avoids the sharp downturn at the beginning of 2008 and substantially outperforms the cumulative returns of the buy-and-hold strategy using all of the 20 REIT Indexes. The results from applying the moving average strategy with 274 individual REITs largely corroborate the findings for the REIT Indexes.