Monetary policy regimes and the term structure of interest rates

US monetary policy is investigated using a regime-switching no-arbitrage term structure model that relies on inflation, output, and the short interest rate as factors. The model is complemented with a set of assumptions that allow the dynamics of the private sector to be separated from monetary policy. The monetary policy regimes cannot be estimated if the yield curve is ignored during estimation. Counterfactual analysis evaluates importance of regimes in policy and shocks for the great moderation. The low-volatility regime of exogenous shocks plays an important role. Monetary policy contributes by trading off asymmetric responses of output and inflation under different regimes.     

Term structure of interest rates in the Singapore Asian dollar market

This paper investigates empirically the term structure of interest rates in the Singapore Asian Dollar Market. We consider extended versions of the ARCH-M model of Engle, Lilien, and Robins (1987). The extended models permit autocorrelation, skewness and leptokurtosis in the residuals. The robustness of the empirical tests with respect to alternative specifications of the ARCH process is examined. It turns out that there is significant time-varying term premium, and this conclusion is independent of the hypothesized ARCH model.     

Nonlinear mean reversion in the term structure of interest rates

The expectations hypothesis implies that the yield curve provides information on the future change in the short-term interest rate. However, transaction costs exist in the financial market, which prevent investors from realizing the arbitrage opportunity, when the arbitrage does not fully cover the transaction costs. The purpose of this paper is to assess the effect of transaction costs on the predictability of the term structure by using the threshold vector error correction model, which allows for the nonlinear adjustment to the long-run equilibrium relationship. A significant amount of threshold effect is found, and the adjustment coefficients are regime-dependent. The empirical result supports the nonlinear mean reversion in the term structure of interest rates.     

Reexamining the term structure of interest rates and the interwar demand for money

This paper reexamines whether the term structure of interest rates, rather than merely a single interest rate, should be included in the demand for money of the interwar era. In contrast to earlier work, we use cointegration techniques to model the equilibrium/error correction process, and find that a sufficiently rich dynamic model using a single interest rate has considerable explanatory power. Nevertheless, we conclude that the inclusion of the term structure may help to explain the turbulent monetary dynamics of the Depression era.     

Global yield curve dynamics and interactions: A dynamic Nelson–Siegel approach

The popular Nelson–Siegel [Nelson, C.R., Siegel, A.F., 1987. Parsimonious modeling of yield curves. Journal of Business 60, 473–489] yield curve is routinely fit to cross sections of intra-country bond yields, and Diebold–Li [Diebold, F.X., Li, C., 2006. Forecasting the term structure of government bond yields. Journal of Econometrics 130, 337–364] have recently proposed a dynamized version. In this paper we extend Diebold–Li to a global context, modeling a potentially large set of country yield curves in a framework that allows for both global and country-specific factors. In an empirical analysis of term structures of government bond yields for the Germany, Japan, the UK and the US, we find that global yield factors do indeed exist and are economically important, generally explaining significant fractions of country yield curve dynamics, with interesting differences across countries.     

Estimation of objective and risk-neutral distributions based on moments of integrated volatility

In this paper, we present an estimation procedure which uses both option prices and high-frequency spot price feeds to estimate jointly the objective and risk-neutral parameters of stochastic volatility models. The procedure is based on a method of moments that uses analytical expressions for the moments of the integrated volatility and series expansions of option prices and implied volatilities. This results in an easily implementable and rapid estimation technique. An extensive Monte Carlo study compares various procedures and shows the efficiency of our approach. Empirical applications to the Deutsche mark–US dollar exchange rate futures and the S&P 500 index provide evidence that the method delivers results that are in line with the ones obtained in previous studies where much more involved estimation procedures were used.     

Identification and estimation of Gaussian affine term structure models

This paper develops new results for identification and estimation of Gaussian affine term structure models. We establish that three popular canonical representations are unidentified, and demonstrate how unidentified regions can complicate numerical optimization. A separate contribution of the paper is the proposal of minimum-chi-square estimation as an alternative to MLE. We show that, although it is asymptotically equivalent to MLE, it can be much easier to compute. In some cases, MCSE allows researchers to recognize with certainty whether a given estimate represents a global maximum of the likelihood function and makes feasible the computation of small-sample standard errors.     

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

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.     

Property taxes and home prices: A tale of two cities

We explore the influence of property taxes on home prices, taking advantage of a policy experiment of property taxation in Shanghai and in Chongqing starting from January 2011. Using the approach suggested by Hsiao, Ching and Wan (2012) we estimate hypothetical home prices in the absence of property taxation for Shanghai and Chongqing using home prices in other cities and provinces. We show that the OLS generates consistent estimators when the price series are non-stationary I(1) processes. We apply the model to a panel of average home prices of 31 cities and provinces in China, and find the property-tax experiment lowered the Shanghai average home price by 11%–15% but raised the Chongqing average home prices by 10%–12%. An examination of the policy details and data on prices by home types suggests the post-treatment price increase in Chongqing can be driven by a spillover effect from high-end to low-end properties.     

Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates

It has been documented that random walk outperforms most economic structural and time series models in out-of-sample forecasts of the conditional mean dynamics of exchange rates. In this paper, we study whether random walk has similar dominance in out-of-sample forecasts of the conditional probability density of exchange rates given that the probability density forecasts are often needed in many applications in economics and finance. We first develop a nonparametric portmanteau test for optimal density forecasts of univariate time series models in an out-of-sample setting and provide simulation evidence on its finite sample performance. Then we conduct a comprehensive empirical analysis on the out-of-sample performances of a wide variety of nonlinear time series models in forecasting the intraday probability densities of two major exchange rates—Euro/Dollar and Yen/Dollar. It is found that some sophisticated time series models that capture time-varying higher order conditional moments, such as Markov regime-switching models, have better density forecasts for exchange rates than random walk or modified random walk with GARCH and Student-t innovations. This finding dramatically differs from that on mean forecasts and suggests that sophisticated time series models could be useful in out-of-sample applications involving the probability density.     

Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

We extend the class of dynamic factor yield curve models in order to include macroeconomic factors. Our work benefits from recent developments in the dynamic factor literature related to the extraction of the common factors from a large panel of macroeconomic series and the estimation of the parameters in the model. We include these factors in a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study, we use a monthly time series panel of unsmoothed Fama–Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relationship between the macroeconomic factors and the yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.     

Modelling and forecasting government bond spreads in the euro area: A GVAR model

This paper proposes an extension to Global Vector Autoregressive (GVAR) models to capture time-varying interdependence among financial variables. Government bond spreads in the euro area feature a time-varying pattern of co-movement that poses a serious challenge for econometric modelling and forecasting. This pattern of the data is not captured by the standard specification that model spreads as persistent processes reverting to a time-varying mean determined by two factors: a local factor, driven by fiscal fundamentals and growth, and a global world factor, driven by the market’s appetite for risk. This paper argues that a third factor, expectations of exchange rate devaluation, gained traction during the crises. This factor is well captured via a GVAR that models the interdependence among spreads by making each country’s spread function of global European spreads. Global spreads capture the exposure of each country’s spread to other spreads in the euro area in terms of the time-varying ‘distance’ between their fiscal fundamentals. This new specification dominates the standard one in modelling the time-varying pattern of co-movements among spreads and the response of euro area spreads to the Greek debt crisis.     

No-arbitrage macroeconomic determinants of the yield curve

No-arbitrage macro-finance models use variance decompositions to gauge the extent of association between the macro variables and yields. We show that results generated by this approach are sensitive to the order of variables in the recursive identification scheme. In a four-factor model, one may obtain 18 different sets of answers out of 24 possible. We propose an alternative measure that is based on levels of macro variables as opposed to shocks. We account for the correlation between the macro and latent factors via projection of the latter onto the former. As a result, the association between macro variables and yields can be computed uniquely via an R²$R^2$. Macro variables explain 80% of the variation in the short rate and 50% of the slope, and 54% to 68% of the term premia.     

Monotonicity preserving regression techniques for interest rate term structure estimation: A note

Alternative methodologies are compared for measuring the term structure of interest rates via monotone approximations to the discount function. Some empirical comparisons using coupon bond data show that a simple linear methodology based on rational tension spline parametrizations turns out to yield the most efficient compromise between the conflicting goals of goodness of fit and smoothuess of the interpolaut Work supported by M.U.R.S.T. funds.     

A reduced form framework for modeling volatility of speculative prices based on realized variation measures

Building on realized variance and bipower variation measures constructed from high-frequency financial prices, we propose a simple reduced form framework for effectively incorporating intraday data into the modeling of daily return volatility. We decompose the total daily return variability into the continuous sample path variance, the variation arising from discontinuous jumps that occur during the trading day, as well as the overnight return variance. Our empirical results, based on long samples of high-frequency equity and bond futures returns, suggest that the dynamic dependencies in the daily continuous sample path variability are well described by an approximate long-memory HAR–GARCH model, while the overnight returns may be modeled by an augmented GARCH type structure. The dynamic dependencies in the non-parametrically identified significant jumps appear to be well described by the combination of an ACH model for the time-varying jump intensities coupled with a relatively simple log-linear structure for the jump sizes. Finally, we discuss how the resulting reduced form model structure for each of the three components may be used in the construction of out-of-sample forecasts for the total return volatility.     

Term structure models and the zero bound: An empirical investigation of Japanese yields

When Japanese short-term bond yields were near their zero bound, yields on long-term bonds showed substantial fluctuation, and there was a strong positive relationship between the level of interest rates and yield volatilities/risk premiums. We explore whether several families of dynamic term structure models that enforce a zero lower bound on short rates imply conditional distributions of Japanese bond yields consistent with these patterns. Multi-factor “shadow-rate” and quadratic-Gaussian models, evaluated at their maximum likelihood estimates, capture many features of the data. Furthermore, model-implied risk premiums track realized excess returns during extended periods of near-zero short rates. In contrast, the conditional distributions implied by non-negative affine models do not match their sample counterparts, and standard Gaussian affine models generate implausibly large negative risk premiums.     

Sequential conditional correlations: Inference and evaluation

This paper presents a new approach to the modeling of conditional correlation matrices within the multivariate GARCH framework. The procedure, which consists of breaking the matrix into the product of a sequence of matrices with desirable characteristics, in effect converts a highly dimensional and intractable optimization problem into a series of simple and feasible estimations. This in turn allows for richer parameterizations and complex functional forms for the single components. An empirical application involving the conditional second moments of 69 selected stocks from the NASDAQ100 shows how the new procedure results in strikingly accurate measures of the conditional correlations.     

Jumps and betas: A new framework for disentangling and estimating systematic risks

We provide a new theoretical framework for disentangling and estimating the sensitivity towards systematic diffusive and jump risks in the context of factor models. Our estimates of the sensitivities towards systematic risks, or betas, are based on the notion of increasingly finer sampled returns over fixed time intervals. We show consistency and derive the asymptotic distributions of our estimators. In an empirical application of the new procedures involving high-frequency data for forty individual stocks, we find that the estimated monthly diffusive and jump betas with respect to an aggregate market portfolio differ substantially for some of the stocks in the sample.     

On the observational implications of taste-based discrimination in racial profiling

This paper contributes to a growing literature that attempts to determine whether disparities in police stops and searches of potential criminals of different races stem from taste-based discrimination. The key challenge in making this evaluation is that police officers have more information than the econometrician and thus racial disparities in police behavior may result from these unobservable factors rather than discrimination. We develop a general equilibrium model of police and potential criminal behavior that encompasses key models in the literature. We highlight the assumptions needed for existing methods of detecting racial discrimination to hold. In particular, we show that when there are increasing costs to search, existing tests for discrimination can give incorrect results. Given the potential importance of these costs, we then propose some alternate methods for detecting racial bias in police behavior.     

Sources of the great moderation: A time-series analysis of GDP subsectors

Recent work finds evidence that the volatility of the U.S. economy fell dramatically around the first quarter of 1984. We trace the timing of this so-called “Great Moderation” across many subsectors of the economy in order to better understand its root cause. We find that the interest rate sensitive sectors generally experience a much earlier volatility decline than other large sectors of the economy. The changes in Federal Reserve stabilization policies that occurred during the early 1980s support the view that an improved monetary policy played an important role in stabilizing real economic activity. We find only mild evidence that “good luck” was important and little evidence to support the claim that improved inventory management was important.