Jumps in Bond Yields at Known Times

There is strong evidence that interest rates and bond yield movements exhibit both stochastic volatility and unanticipated jumps. The presence of frequent jumps makes it natural to ask whether there is a premium for jump risk embedded in observed bond yields. This paper identifies a class of jump-diffusion models that are successful in approximating the term structure of interest rates of emerging markets. The parameters of the term structure of interest rates are reconciled with the associated bond yields by estimating the volatility and jump risk premia in highly volatile markets. Using the simulated method of moments (SMM), results suggest that all variants of models which do not take into account stochastic volatility and unanticipated jumps cannot generate the non-normalities consistent with the observed interest rates. Jumps occur (8,10) times a year in Argentina and Brazil, respectively. The size and variance of these jumps is also of statistical significance.

Multifractal Modeling of the US Treasury Term Structure and Fed Funds Rate

Bond Risk Premia in Emerging Markets: Evidence from Brazil and Uruguay

An E-ARCH Model for the Term Structure of Implied Volatility of FX Options

We present a pricing kernel that summarizes well the main features of the dynamics of interest rates and risk in postwar U.S. data and use it to uncover how the pricing kernel has moved with the short rate in this data. Our findings imply that standard monetary models miss an essential link between the central bank instrument and the economic activity that monetary policy is intended to affect, and thus we call for a new approach to monetary policy analysis. We sketch a new approach using an economic model based on our pricing kernel. The model incorporates the key relationships between policy and risk movements in an unconventional way: the central bank's policy changes are viewed as primarily intended to compensate for exogenous business cycle fluctuations in risk that threaten to push inflation off target. This model, while an improvement on standard models, is considered just a starting point for their revision. It leads to critical questions that researchers need to answer as they continue to revise their approach to monetary policy analysis. Copyright © 2009 by the National Bureau of Economic Research.

ABSTRACTWe extract the short-, medium-, and long-term factors from the term structure of the option-implied volatility (OIV) of the S&P 500, the FTSE 100, and the Chinese 50 Exchange-Traded Funds (ETF), using an extension of the Nelson-Siegel (N-S) model and use estimated factors to predict future realized volatility (FRV) in the US, UK, and Chinese markets. Several interesting findings emerged from our study. First, we confirmed that the VIX is more informative than historical realized volatility (HRV) in predicting FRV. Second, we find that the volatility term structure contains some additional information compared with the VIX and HRV. Third, we verify that the three factors extracted from the N-S model are strongly cointegrated, related to volatilities. Moreover, based on the normalized error term of the cointegrated pairs, we construct straddles and delta-hedging option trading strategies. Without taking transaction costs into account, the straddle call trading strategy achieves a mean return of 37.59% monthly, and, at the same time, the exponential cumulative returns for the straddle call strategies are 4.2411 at a threshold of 1.1 in the S&P 500. As the threshold increases, the volume of transactions declines, leading to a fall in cumulative mean returns.

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.

The Price and Quantity of Interest Rate Risk

Disaster recovery and the term structure of dividend strips

Many researchers have dealt with and contributed to the literature on bond risk and risk premium. This paper analyzes components of bond risk premium using market valuation theory. We introduce a concept of “yield risk.” The “yield risk” of a bond is shown to be the sum of systematic risk and default risk. We successfully bridge the theoretical gap between what is commonly known (and contained in most investment textbooks) that yield to maturity of a bond may be an upward biased measure of cost of debt, and the theory of valuing risky debt in capital market equilibrium.

Endogenous monetary policy shifts and the term structure: Evidence from Japanese government bond yields

Empirical evidence suggests that excess bond returns are forecastable by financial indicators such as forward spreads and yield spreads, a violation of the expectations hypothesis based on constant risk premia. But existing evidence does not tie the forecastable variation in excess bond returns to underlying macroeconomic fundamentals, as would be expected if the forecastability were attributable to time variation in risk premia. We use the methodology of dynamic factor analysis for large datasets to investigate possible empirical linkages between forecastable variation in excess bond returns and macroeconomic fundamentals. We find that several common factors estimated from a large dataset on U.S. economic activity have important forecasting power for future excess returns on U.S. government bonds. Following Cochrane and Piazzesi (2005), we also construct single predictor state variables by forming linear combinations of either five or six estimated common factors. The single state variables forecast excess bond returns at maturities from two to five years, and do so virtually as well as an unrestricted regression model that includes each common factor as a separate predictor variable. The linear combinations we form are driven by both "real" and "inflation" macro factors, in addition to financial factors, and contain important information about one year ahead excess bond returns that is not captured by forward spreads, yield spreads, or the principal components of the yield covariance matrix.

Real estate yields are thought to reflect risk free rates of return, a risk premium and expected growth in cash flows. Modelling of real estate yields can then be conducted using proxies for each of these factors. A common proxy for risk free rate is nominal government bond yields, but these compensate investors for expected inflation and inflation risk, whereas real government bond yields should track changes in the underlying real risk free rate. This paper examines whether real bond yields provide more explanation than nominal bond yields of real estate yields for a panel of 20 office markets in five European countries over 1997-2016. Past bond yields, disequilibrium in rents and levels of investment activity are also used to explain real estate yields. The initial results suggest that the use of real bond yields does not improve empirical models much for this sample of data. However, past bond yields and lagged transaction volumes exhibit statistically significant relationships with office yields, raising questions as to how investors adjust target rates of return and required yields in the face of changing macroeconomic and real estate market conditions.

This paper investigates the implications of mixtures of affine, quadratic, and nonlinear models for the term structure of volatility. The dynamics of the term structure of interest rates appear to exhibit pronounced time-varying or stochastic volatility. Ahn, Dittmar, and Gallant (2000) provide evidence suggesting that term structure models incorporating a set of quadratic state variables are better able to reproduce yield dynamics than affine models, though none of the models is able to fully capture the term structure of volatility. In this study, we combine affine, quadratic and nonlinear factors in order to maximize the strengths of a term structure model in generating heteroskedastic volatility. We show that this combination entails a tradeoff between specification of heteroskedastic volatility and correlations among the state variables. By combining these factors, we are able to gauge the cost of this tradeoff. Using the Efficient Method of Moments [Gallant and Tauchen (1996)], we find that augmenting a quadratic model with a nonlinear factor results in improvement in fit over a model characterized only by quadratic factors. Since the nonlinear factor is characterized by stronger dependence of volatility on the level of the factor, we conclude that flexibility in the specification of both level dependence and correlation structure are important for describing term structure dynamics.

Understanding Index Option Returns