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.     

Predictions of short-term rates and the expectations hypothesis

This paper emphasizes that traditional tests of the EH are based on two assumptions: the expectations hypothesis (EH) per se and an assumption about the expectations generating process (EGP) for the short-term rate. Arguing that conventional tests of the EH need to assume EGPs that may be significantly at odds with the true EGP, we investigate this possibility by analyzing the out-of-sample predictive performances of several models for predicting interest rates, including a few models which assume that the EH holds in its functional form that relates long- to short-term yields. Using US riskless yield data for a 1970–2016 monthly sample and testing methods that take into account the parameter uncertainty, the null hypothesis of an equal predictive accuracy of each model relative to the random walk alternative is hardly ever rejected at intermediate and long horizons. This confirms that, at least at a practical level, the main difficulty with the EH is represented by the effective prediction of short-term rates. We discuss the relevance of these findings for central banks’ use of forward guidance.     

The expectations hypothesis and decoupling of short- and long-term US interest rates: A pairwise approach

The link between short-term policy rates and long-term rates elucidate the potential effectiveness of monetary policy. We examine the US term structure of interest rates using a pairwise econometric approach advocated by Pesaran (2007). Our empirical modelling strategy employs a probabilistic test statistic for the expectations hypothesis of the term structure based on the percentage of unit root rejections among all interest rate differentials. We find support for the expectations hypothesis and provide new insights into the nature of interest rate decoupling which are of value to policymakers. The maturity gap associated with interest rate pairs negatively impacts on the probability of stationarity, and also on the speed of adjustment towards long-run equilibrium. We further show that the speed of adjustment has become more sensitive to the maturity gap over time.     

Post-euro EU and US interest rates and foreign exchange rates: Are they in harmony or in disarray?

This paper focuses on: (1) how a select set of financial and economic factors could set the path for interest rates and foreign exchange rates, and (2) whether the resultant realized interest and exchange rates would be in harmony or in disarray. Using post-euro data for the EU and the US, an array of monetary rules is examined. In particular, the paper investigates whether the original and the extended Taylor rules provide an explanation of the dynamics of the EU monetary system since the inception of the euro. Our findings indicate that the EU and the US monetary responses are not the same and that exchange rates play a significant role.     

Degrees of tax indexation and nominal interest rates: Effects of inflation on incentives to save and invest

This paper examines the effects of inflation expectations on nominal interest rates, and incentives to save and invest under partial and complete tax indexation schemes. One would expect that a partially indexed structure would be better than a non-indexed system. However, this is not the case—it reduces the adverse effects of inflation on the incentives to save, but accentuates them on the incentives to invest. Moreover, a change from a non-indexed tax structure to a fully-indexed structure will, ceteris paribus, lead to lower equilibrium interest rates, whereas a switch to a partially indexed system will imply higher rates.

Time-frequency co-movements between oil prices and interest rates: Evidence from a wavelet-based approach

This paper investigates time–frequency co-movements between crude oil prices and interest rates. To test this relationship, the study applied a continuous wavelet and cross wavelet approaches to data from West Texas Intermediate (WTI) crude oil prices and interest rates in the United States (U.S.). Results from the sample period revealed significant relationships, in the intermediate term, between WTI crude oil prices and U.S. interest rates. Moreover, co-movements between oil price and interest rate variables were especially sensitive during abnormal political events and periods of financial ‘meltdown’. We further use Partial Wavelet Coherence (PWC) and Multiple Wavelet Coherence (MWC) methods to investigate the impacts of five major control variables namely GDP growth, unemployment, three-month Treasury bill, CPI index and industrial production index. The results show a powerful impact of control variables on oil-interest rates co-movements under different frequencies. Finally, we show evidence of co-integrating long run relationship between oil markets and control variables. These results have important implications for energy investors and policy makers.     

A panel data approach to economic forecasting: The bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zero-mean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.     

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.     

The comparative forecast performance of univariate and multivariate models: an application to real interest rate forecasting

Does the use of information on the past history of the nominal interest rates and inflation entail improvement in forecasts of the ex ante real interest rate over its forecasts obtained from using just the past history of the realized real interest rates? To answer this question we set up a univariate unobserved components model for the realized real interest rates and a bivariate model for the nominal rate and inflation which imposes cointegration restrictions between them. The two models are estimated under normality with the Kalman filter. It is found that the error-correction model provides more accurate one-period ahead forecasts of the real rate within the estimation sample whereas the unobserved components model yields forecasts with smaller forecast variances. In the post-sample period, the forecasts from the bivariate model are not only more accurate but also have tighter confidence bounds than the forecasts from the unobserved components model.     

The effects of FX-interventions on forecasters disagreement: A mixed data sampling view

We analyse the heterogeneity of exchange rate forecasts by a panel of professional forecasters. Adopting the view that forecasters’ economic behaviour is such that they constantly collect, process and analyse relevant information when producing forecasts, we apply a Mixed-Data Sampling (MIDAS) regression approach. This enables us to explore the roles played by key drivers for which available data are at different frequencies from forecast disagreement. Examining the Colombian peso/U.S. dollar exchange rate, we find that central bank intervention is most effective in reducing heterogeneity in the very short-run, and when conducted against a background of high exchange rate volatility.     

Forecasting crude oil futures market returns: A principal component analysis combination approach

To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.     

A Model Confidence Set approach to the combination of multivariate volatility forecasts

In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably poses additional sources of uncertainty related to the selection of the optimal intradaily sampling frequency and to the construction of the best realized estimator. Likewise, the choices of model structure and estimation method also have a critical role. In order to alleviate the impact of these sources of uncertainty, we propose a forecast combination strategy based on the Model Confidence Set [MCS] to adaptively identify the set of most accurate predictors. The combined predictor is shown to achieve superior performance with respect to the whole model universe plus three additional competitors, independently of the MCS or portfolio settings.     

Voluntary turnover rates and organizational performance in the US federal government: the moderating role of high-commitment human resource practices

Despite the growing body of literature on antecedents of employee turnover, little attention has been paid to the effect of voluntary turnover on organizational outcomes in public administration. Using panel data from the US federal government, this article tests the proposition that the relationship between voluntary turnover rates and organizational performance is negative but becomes curvilinear as turnover rates increase. Based on the contingency perspective of the turnover–performance link, this article further examines the moderating role of high-commitment human resource practices (HCHRP) in the relationships. Findings indicate that voluntary turnover has a positive relationship with organizational performance, but it turns out to be an inverted U-shaped curve as turnover rates increase from low to high levels. Furthermore, the moderating effect on the curvilinear relationship is especially pronounced for federal agencies with high levels of HCHRP.     

Models of exchange rates : A comparison of forecasting results

In this note we compare the results of several published papers on exchange rate forecasting. With regard to univariate time series models, we confirm the result that such models, on average, do not outperform the simple random walk forecasting rule. This conclusion corrects results reported in this Journal by Alexander and Thomas (1987).     

Determinants of bank interest margins in Central and Eastern Europe: A comparison with the West

We investigate the determinants of bank interest margins in the Central and Eastern European countries (CEEC). We assess to what extent the relatively high bank margins in CEEC can be attributed to low efficiency or non-competitive market conditions, controlling for the macroeconomic environment and the influence of foreign and state-owned banks. We systematically compare CEEC banks with Western European banks. Our results indicate that banking in the CEEC is on a virtuous path, at least in the EU accession countries: Increased efficiency benefits customers, while capital adequacy supports systemic stability. In the non-accession countries, important policy actions are required.     

The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State

This paper explores the relationship between institutional change and forecast accuracy via an analysis of the entitlement caseload forecasting process in Washington State. This research extends the politics of forecasting literature beyond the current area of government revenue forecasting to include expenditure forecasting and introduces an in-depth longitudinal study to the existing set of cross-sectional studies. Employing a fixed-effects model and ordinary least squares regression analysis, this paper concludes that the establishment of an independent forecasting agency and subsequent formation of technical workgroups improve forecast accuracy. Additionally, this study finds that more frequent forecast revisions and structured domain knowledge improve forecast accuracy.     

The costs of suboptimal dynamic asset allocation: General results and applications to interest rate risk, stock volatility risk, and growth/value tilts

The recent theoretical asset allocation literature has derived optimal dynamic investment strategies in various advanced models of asset returns. But how sensitive is investor welfare to deviations from the theoretically optimal strategy? Will unsophisticated investors do almost as well as sophisticated investors? This paper develops a general theoretical framework for answering such questions and applies it to three specific models of interest rate risk, stochastic stock volatility, and mean reversion and growth/value tilts of stock portfolios. Among other things, we find that growth/value tilts are highly valuable, but the hedging of time-varying stock risk premia is less important.     

Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines dimensionality reduction, regime-switching models, and forecast combination to predict excess returns on the S&P 500. First, we aggregate the weekly information of 146 popular macroeconomic and financial variables using different principal component analysis techniques. Second, we estimate Markov-switching models with time-varying transition probabilities using the principal components as predictors. Third, we pool the models in forecast clusters to hedge against model risk and to evaluate the usefulness of different specifications. Our weekly forecasts respond to regime changes in a timely manner to participate in recoveries or to prevent losses. This is also reflected in an improvement of risk-adjusted performance measures as compared to several benchmarks. However, when considering stock market returns, our forecasts do not outperform common benchmarks. Nevertheless, they do add statistical and, in particular, economic value during recessions or in declining markets.     

A tale of two yield curves: Modeling the joint term structure of dollar and euro interest rates

Modeling the joint term structure of interest rates in the United States and the European Union, the two largest economies in the world, is extremely important in international finance. In this article, we provide both theoretical and empirical analysis of multi-factor joint affine term structure models (ATSM) for dollar and euro interest rates. In particular, we provide a systematic classification of multi-factor joint ATSM similar to that of Dai and Singleton (2000). A principal component analysis of daily dollar and euro interest rates reveals four factors in the data. We estimate four-factor joint ATSM using the approximate maximum likelihood method of [A?t-Sahalia, 2002] and [A?t-Sahalia, forthcoming] and compare the in-sample and out-of-sample performances of these models using some of the latest nonparametric methods. We find that a new four-factor model with two common and two local factors captures the joint term structure dynamics in the US and the EU reasonably well.     

The impact of the US on Latin American business cycles: A new approach

Business cycles in Latin America have tended to be more volatile than those in wealthier nations such as the US. Accordingly, much research has been conducted on Latin business cycles, as well as the impact of the US on such fluctuations. Some research seeks to find how “integrated” cycles are in the US and Latin America, yielding conflicting results. We apply a new method to the question of business cycle synchronization between the US and nine Latin nations. We find that in the majority of cases integration has been rising in recent years. We also find, contrary to some previous studies, that integration does not appear to be affected by either the level of trade or of capital account openness. Finally, we find that the two countries that are dollarized – Ecuador and El Salvador – appear least integrated with the US. This last finding has potentially troubling implications in terms of the ability of these nations to adjust to asymmetric shocks vis-à-vis the US.