Bond Return Volatility Research Articles

An Investigation of the Nonlinear and Asymmetric Spillover Effects of U.S. Economic Policy Uncertainty on Bond Return Volatility of Emerging Markets

ABSTRACT This paper investigates the nonlinear and asymmetric relationships between the U.S. economic policy uncertainty (EPU) index and the volatility of bond returns in 36 emerging markets (EMs) using panel quantile regression methods. Our findings indicate that the spillover effects of the U.S. EPU on the volatility of bond returns are strong in EMs, particularly in the upper quantiles of volatility, and are substantial in magnitude. We also explore the impact of the U.S. monetary policy uncertainty (MPU) index and fiscal policy uncertainty (FPU) indices on the volatility of bond returns in 36 EMs, and observe a positive correlation between both the U.S. MPU and the U.S. FPU and bond return volatility in EMs. Of all the spillover effects, the U.S. MPU has the largest magnitude. Additionally, our results demonstrate that the spillover effects induced by three U.S. policy uncertainties are sensitive to the macroeconomic environment, with a stronger impact during financial crises and in countries with high degrees of financial openness.

Hunting the quicksilver: Using textual news and causality analysis to predict market volatility

This paper proposes that the dynamics of bond volatility may be understood by studying textual news sentiments. In this new approach, a modified framework is used to understand the atypical characteristics of bond market news. The paper proceeds in two steps. First, a word list of sentiment terms is generated using three sentiment word lists to determine negative and positive news sentiment scores. Second, four measures of volatility are estimated and combined with a nonlinear technique adapted from information theory to understand the correlation and direction of causality between sentiment scores and measures of volatility. This paper shows that sentiments extracted from textual news published in the newspapers can explain bond returns volatility or the quicksilver. The empirical results support that news sentiment is highly correlated with the measures of volatility and that information flows unidirectionally from news to volatility. This study, perhaps the earliest work in text mining to examine the run of causality between news signals and bond return volatility, adapts a nonlinear technique from information theory to describe the nonlinear behavior of Indian debt markets and understand the volatility dynamics of the benchmark bond.

The Price and Quantity of Interest Rate Risk

Studies of the dynamics of bond risk premia that do not account for the corresponding dynamics of bond risk are hard to interpret. We propose a new approach to modeling bond risk and risk premia. For each of the US and China, we reduce the government bond market to its first two principal-component bond-factor portfolios. For each bond-factor portfolio, we estimate the joint dynamics of its volatility and Sharpe ratio as functions of yield curve variables, and of VIX in the US. We have three main findings. (1) There is an important second factor in bond risk premia. (2) Time variation in bond return volatility is as important as time variation in bond Sharpe ratios. (3) Bond risk premia are solely compensation for bond risk, as no-arbitrage theory predicts. Our approach also allows us to document interesting cyclical and secular time-variation in the term structure of bond risk premia in both the US and China. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

A three-stage model of the volatility-volume relation in the Junk bond market during the 2007-2008 financial crisis

PurposeThe purpose of this paper is to examine the joint dynamics of volatility–volume relation in the high-yield (junk) corporate bond market during the 2007–2008 financial crisis.Design/methodology/approachThe author proposes a new empirical model of three-stage equations to better estimate the volume–volatility relation that helps in alleviating three econometrical problems. In Stage 1, the author estimates the fitted values of trading volume using a censored regression model, to alleviate the truncation problems of using Transaction Reporting and Compliance Engine data. In Stage 2, the author calculates the fitted values of bond return volatility using asymmetric Sign-GARCH model, to control for the asymmetric volatility in return series. In Stage 3, the author uses the fitted values of trading volume from the censored regression model (Stage 1) and the fitted values of return volatility from the GARCH model (Stage 2), to better alleviate the endogeneity problems between both variables.FindingsThe central finding is that conclusions about the statistical significance and the direction of the volume–volatility relationship in the junk bond market are dependent on the econometric methodology used.Originality/valueFrom a practitioner perspective, it is important for professional traders holding positions in fixed income securities in their trading accounts to be aware of their asymmetric time-varying volume–volatility shifting trends. Such knowledge helps traders diversify their positions and manage their portfolios more appropriately.

Bond Volatility and CDS Auctions

We document a higher bond return volatility around the time of default for bonds included in CDS auctions (especially cheapest-to-deliver bonds) versus those that are not, while controlling for firm fundamentals and bond illiquidity. This finding does not extend to time periods far ahead of default, and there is no significant difference between the idiosyncratic stock return volatility of CDS firms and non-CDS firms around the time of default. These results are more consistent with CDS buyers and sellers manipulating bond prices to achieve favorable CDS auction outcomes, rather than a spillover of price discovery by CDS traders into the stock and bond markets.

CRA Reputation and Bond Yield: Evidence from the Chinese Bond Market

AbstractThe paper examines the reputation effect of credit rating agencies (CRAs) in China. We find a negative association between CRAs’ reputation and public bonds’ offering yield spreads after controlling for endogeneity. Analyses of the correlation between CRA reputation and bond return volatility in the aftermarket and cross‐sectional variations of reputation effects alongside a host of information environment proxies for issuers, we identify the information channel through which CRA reputation plays its role. This is further supported when we utilize an exogenous event, the introduction of an independent CRA (the China Bond Rating Co. Ltd), to explore the potential changes of the reputation effect.

The Behaviour of the Equity Yield and Its Relation with the Bond Yield: The Role of Inflation

Understanding the behaviour of the equity yield and its relation to the bond yield is important for portfolio managers and those engaged in modelling the interaction between asset classes. During the mid-1900s, the equity yield—which was previously greater than the bond yield—declined, while the bond yield rose and became higher. Research, seeking to understand this, put forward the view that stock and bond return volatility is key. Evidence from the 2000s suggest that the relative values of the equity and bond yield have flipped again, with the former now greater. Empirical evidence presented here shows that during such periods, the hypothesised relation between the equity yield and the bond yield and the two volatilities also changes, with the signs reversed. Moreover, there is noticeable variation in the coefficient values. This paper argues that the relative equity and bond yield values are, to a large extent, driven by inflation volatility. High inflation volatility persisted during the first half of the twentieth century when the equity yield was higher. This was followed by more benign inflation volatility when the bond yield became higher. Evidence for a long span of US data, and shorter German, Japanese, and UK data, suggests the recent rise in the equity yield is accompanied by an uptick in inflation volatility relative to its recent tranquillity.

The Volatility of Long-Term Bond Returns: Persistent Interest Shocks and Time-Varying Risk Premiums

We develop an almost affine term-structure model with a closed-form solution for factor loadings in which the spot rate and the risk price are fractionally integrated processes with different integration orders. This model is used to explain two stylized facts. First, predictability of longterm excess bond returns requires sufficient volatility and persistence in the risk price. Second, the large volatility of long-term bond returns requires persistence in the spot rate. Decomposing long-term bond returns, we find that the expectations component from the level factor is more volatile than returns themselves and that the risk premium correlates negatively with level-factor innovations.

Do economic variables improve bond return volatility forecasts?

This paper explores whether various economic variables improve monthly bond return volatility forecasts using the 1963–2012 data. In-sample analysis indicates that stock return or Federal Funds rate difference Granger causes bond volatility of all maturities. The forecasting ability of other variables mainly appears at the short end of the term structure or during the relatively turbulent time. Out-of-sample analysis suggests little evidence of forecast improvement, though forecast combination does improve the performance. Decomposing the out-of-sample forecasts indicates that the poor performance is primarily attributed to overfitting, and variable reduction by principal components does not change the results.

The Term Structure of Interest Rates in India

We examine the term structure of interest rates in India to see if the yield curve can be rationalized based on the ‘expectations hypothesis’. Although we find evidence of predictability for holding period returns, we reject the null hypothesis that the expectations hypothesis holds for the period under consideration. Contrary to the finding in the US, the volatility of Indian bond returns is consistent with the expectations hypothesis. Returns on long-term bonds are less volatile than those of short-term bonds. The volatility puzzle documented by Shiller on US data is not observed in Indian bond returns.

Structural break tests and the Greek sovereign debt crisis: revisited

Amidst the lack of consensus from previous academic studies, this paper contributes to existing literature by further examining the commencement date of the Sovereign Debt Crisis for the Greek economy. The contribution of this paper purports that the contentious issue of the start of the Greek crisis was taking place much earlier than reported by previous research. Empirical results from this paper challenge earlier studies that may have underestimated the impact of the degree of persistence in the volatility of bond returns. This analysis uses monthly 10-year Greek government bond data and three independent structural break model tests which allow for the detection of possible endogenous break dates to capture the beginning of the crisis. Each model provides empirically plausible and robust frameworks for examining the volatility of bond returns in an evolving time series behaviour. Ultimate results from a series of autoregressive EGARCH estimations, with and without dummy variables for break dates are compared. The dummy variables are incorporated within the coefficients of the mean and variance equations to validate the structural breaks in each series. Overall results show a significant presence of nonconsistent parameters capturing a structural break in the time series sample. The detection of this break, November 2009, represents a major regime change triggered by the start of the debt crisis for the Greek economy. Crucially, research implications of such excess volatilities in sovereign bond markets have poignant implications for regulators, investors and portfolio risk managers alike.

The Volatility of Long-Term Bond Returns: Persistent Interest Shocks and Time-Varying Risk Premiums

We develop a model that can match two stylized facts of the term-structure. The first stylized fact is the predictability of excess returns on long-term bonds. Modeling this requires sufficient volatility and persistence in the price of risk. The second stylized fact is that long-term yields are dominated by a level factor, which requires persistence in the spot interest rate. We find that a fractionally integrated process for the short rate plus a fractionally integrated specification for the price of risk leads to an analytically tractable almost affine term structure model that can explain the stylized facts. In a decomposition of long-term bond returns we find that the expectations component from the level factor is more volatile than the returns themselves. It therefore takes a volatile risk premium that is negatively correlated with innovations in the level factor to explain the volatility of long-term bond returns. The model also implies that excess bond returns do not exhibit mean reversion, consistent with the empirical evidence.

What determines the stock market's reaction to monetary policy statements?

We find that information communicated through monetary policy statements has important business cycle dependent implications for stock prices. For example, during periods of economic expansion, stocks tend to respond negatively to announcements of higher rates ahead. In recessions, however, we find a strong positive reaction of stocks to seemingly similar signals of future monetary tightening. We provide evidence that the state dependence in the stock market's response is explained by information about the expected equity premium and future corporate cash flows contained in monetary policy statements. We also show state dependence in the average stock returns on days of scheduled FOMC meetings and in the impact of monetary policy statements on stock and bond return volatility.

What Determines the Stock Market's Reaction to Monetary Policy Statements?

We find that information communicated through monetary policy statements has important business cycle dependent implications for stock prices. For example, during periods of economic expansion, stocks tend to respond negatively to announcements of higher rates ahead. In recessions, however, we find a strong positive reaction of stocks to seemingly similar signals of future monetary tightening. We provide evidence that the state dependence in the stock market’s response is explained by information about the expected equity premium and future corporate cash flows contained in monetary policy statements. We also show state dependence in the average stock returns on days of scheduled FOMC meetings and in the impact of monetary policy statements on stock and bond return volatility.

The predictability of excess returns in the emerging bond markets

This study examines the relationships that exist between excess bond returns and global and country-specific factors, focusing on a sample of 12 developing countries. Our results show a significantly negative autocorrelation with regard to the excess returns of bonds in the emerging markets; with growth in the size of the local bond market, there is a corresponding increase in the excess bond returns. For most of the developing economies, with an increase in emerging market bond returns, there are discernible reductions in the level of domestic interest rate and increases in the volatility of bond returns. A higher sovereign bond spread predicts higher excess returns for emerging market bonds. Overall, we find that world factors have relatively less predictive power in the emerging market bonds.

A comment on bond risk, bond return volatility, and the term structure of interest rates

A comment on bond risk, bond return volatility, and the term structure of interest rates

The Volatility of Long-Term Bond Returns: Persistent Interest Shocks and Time-Varying Risk Premiums

We develop a model that can match two stylized facts of the term-structure. The first stylized fact is the predictability of excess returns on long-term bonds. Modeling this requires sufficient volatility and persistence in the price of risk. The second stylized fact is that long-term yields are dominated by a level factor, which requires persistence in the spot interest rate. We find that a fractionally integrated process for the short rate plus a fractionally integrated specification for the price of risk leads to an analytically tractable almost affine term structure model that can explain the stylized facts. In a decomposition of long-term bond returns we find that the expectations component from the level factor is more volatile than the returns themselves. It therefore takes a volatile risk premium that is negatively correlated with innovations in the level factor to explain the volatility of long-term bond returns. The model also implies that excess bond returns do not exhibit mean reversion, consistent with the empirical evidence.

Bond risk, bond return volatility, and the term structure of interest rates

Abstract This paper explores the time variation in the bond risk, as measured by the covariation of bond returns with stock returns and consumption growth, and in the volatility of bond returns. A robust stylized fact in empirical finance is that the spread between the yields on long- and short-term bonds forecasts future excess returns on bonds at varying horizons positively; in addition, the short-term nominal interest rate forecasts both the stock return volatility and the exchange rate volatility positively. This paper presents evidence that movements in both the short-term nominal interest rate and the yield spread are positively related to changes in the subsequent realized bond risk and bond return volatility. The yield spread appears to proxy for business conditions, while the short rate appears to proxy for inflation and economic uncertainty. A decomposition of bond betas into a real cash flow risk component and a discount rate risk component shows that yield spreads have offsetting effects in each component. A widening yield spread is correlated with a reduced cash-flow (or inflationary) risk for bonds, but it is also correlated with a larger discount rate risk for bonds. The short rate only forecasts the discount rate component of the bond beta.

The economic value of range-based covariance between stock and bond returns with dynamic copulas

Abstract The covariance between stock and bond returns plays important roles in the setting up of asset allocation strategies and portfolio diversification. In the present study, we propose a multivariate range-based volatility model incorporating dynamic copulas into a range-based volatility model to describe the volatility and dependence structures of stock and bond returns. We then go on to assess the economic value of the covariance forecasts based on our proposed model under a mean-variance framework. The out-of-sample forecasting performance reveals that investors would be willing to pay between 39 and 2081 basis points per year to switch from a dynamic trading strategy under the return-based volatility model to a dynamic trading strategy under the range-based volatility model, with more risk-averse investors being willing to pay even higher switching fees. Furthermore, additional economic gains of between 33 and 1471 annualized basis points are achieved when taking the leverage effect into consideration.

The Informational Efficiency of the Corporate Bond Market: What is the Role of Trading Volume?

This article investigates the informational efficiency of the corporate bond market by examining whether the technical analysis of volume data can help in predicting the bond return volatility as well as the bond returns itself. To this end, the researcher uses prices and volume data obtained from TRACE for a sample of continuously traded corporate bonds from January 2007 to June 2009. To model volatility, we use a variant of GARCH model and the results show that it is an attractive representation of the daily corporate bond return and volatility. The overall conclusion from the results is that the trading volume data does not help in predicting future bond return volatility. Although such result is consistent with the informational efficiency of the corporate bond market, there is still a possibility that this study suffers from some data problems related to the liquidity issue in the corporate bond market.