Research

Published papers

Return Predictability, Dividend Growth and the Persistence of the Price-Dividend Ratio (with Joao Madeira and Dooruj Rambaccussing), forthcoming in International Journal of Forecasting.

Abstract: Empirical evidence shows that the order of integration of returns and dividend growth is approximately equal to the order of integration of the first differenced price-dividend ratio, which is about 0.8. Yet, the present-value identity implies the three series should be integrated of the same order. We reconcile this puzzle by showing that the aggregation of antipersistent expected returns and expected dividends gives rise to the price-dividend ratio with properties that mimic long memory in finite samples. In the empirical implementation, we extend and estimate the state-space present-value model by allowing for fractional integration in expected returns and expected dividend growth. This extension improves the model's forecasting power in-sample and out-of-sample. In addition, expected returns and expected dividend growth modelled as ARFIMA processes are more closely related to future macroeconomic variables, which makes them suitable as leading business cycle indicators.

Unconventional monetary policies and the yield curve: Estimating non-affine term structure models with unspanned macro risk by factor extraction (with Peter Spencer), 2024, Review of Asset Pricing Studies, Vol. 14, Issue 1. Matlab code is available here.

Abstract: We show how the Joslin, Singleton, and Zhu (2011) factor extraction approach to estimating the Gaussian term structure model can be modified to handle the interest rate lower bound without the approximations used in other approaches. This drastically reduces the computation time and produces more robust estimates of the lower bound parameter and the shadow rate. It makes feasible the extensive specification search necessary to allow for unspanned factors as in Joslin, Priebsch, and Singleton (2014), allowing the term structure model to be used to better assess the effects of policy on the term premium and market expectations.

Modeling the Covid-19 epidemic using time series econometrics (with Peter Spencer), Health Economics, 1-21, 2021. Matlab code is available here.

Abstract: The classic 'logistic' model has provided a realistic model of the behaviour of Covid-19 in China and many East Asian countries. Once these countries passed the peak, the daily case count fell back, mirroring its initial climb in a symmetric way, just as the classic model predicts. However, in Italy and Spain and most other Western countries, the first wave of the epidemic was very different. The daily count fell back gradually from the peak but remained stubbornly high. The reasons for the divergence from the classical model remain unclear. We take an empirical stance on this issue and develop a model framework based upon the statistical characteristics of the time series. With the possible exception of China, the workhorse logistic model is decisively rejected against more flexible alternatives.

Monetary policy at the zero lower bound: Information in the Federal Reserve’s balance sheet, European Economic Review, Vol. 131, January 2021.

Abstract: I examine the impact of the actual purchases of Treasury securities by the Federal Reserve on the Treasury yields. Using structural stability tests we find significant breaks in the relation between these variables. I find that in the zero lower bound period following the first phase of quantitative easing, May 2010 to December 2015, the actual purchases of Treasury securities by the Federal Reserve are positively related to changes in Treasury yields. This effect is driven primarily by the positive relation of the Treasury purchases with the bond risk premium, but they are also positively related to the expected inflation rate and the real rate of interest. The evidence is consistent with the liquidity channel hypothesis as put forward by Krishnamurthy and Vissing-Jorgensen (2011), since the Federal Reserve's Treasury purchases also strongly predict a lower corporate yield spread. Using a macro-finance term structure model I provide counterfactual estimates of the Treasury yields in the zero lower bound period. 

Estimating the term structure with linear regressions: Getting to the root of the problem (with Peter Spencer), Journal of Financial Econometrics, Vol. 19, Issue 5, 2021. Matlab code available here.

Abstract: Linear estimators of the affine term structure model are inconsistent since they cannot reproduce the factors used in estimation. This is a serious handicap empirically, giving a worse fit than the conventional ML estimator that ensures consistency. We show that a simple self-consistent estimator can be constructed using the eigenvalue decomposition of a regression estimator. The remaining parameters of the model follow analytically. Estimates from this model are virtually indistinguishable from that of the ML estimator. We apply the method to estimate various models of U.S. Treasury yields. These exercises greatly extend the range of models that can be estimated. 

The advantages of using excess returns to model the term structure (with Peter Spencer), Journal of Financial Economics, Vol. 125, Issue 1, 163-181, 2017. Matlab code available here.

Abstract: We advocate the use of excess returns rather than yields or log prices in analysing the risk neutral dynamics of the term structure. We show that under standard assumptions, excess returns are affine in the risk neutral innovations in the factors. This framework has several important advantages. First, it allows for an easy estimation of models that are more flexible than the AR(1). Indeed, we estimate models with more general dynamics, like ARFIMA(p, d, q), almost as easily as AR(1). Second, within our framework the dimension of the unrestricted model is the same for the AR(1) as it is for the richer models, and does not expand in line with the state vector as it does in a yield or log price framework. This makes it appropriate to test all of these risk neutral dynamic specifications against the same OLS unrestricted alternative. Our results for the US Treasury bond market show that the unrestricted model is preferred to the AR(1) by the Bayesian Information Criterion, but the opposite conclusion is reached for more flexible models. A final advantage of the excess returns framework is that the pricing errors are much lower than for the equivalent log price system. 

Long memory affine term structure models (with Paolo Zaffaroni), Journal of Econometrics, Vol. 191, Issue 1, 33-56, 2016.

Abstract: We develop a Gaussian discrete time essentially affine term structure model with long memory state variables. This feature reconciles the strong persistence observed in nominal yields and inflation with the theoretical implications of affine models, especially for long maturities. We characterize in closed-form the dynamic and cross-sectional implications of long memory for our model. We explain how long memory can naturally arise within the term structure of interest rates, providing a theoretical underpinning for our model. Despite the infinite-dimensional structure that long memory implies, we show how to cast the model in state space and estimate it by maximum likelihood. An empirical application of our model is presented. 

Working papers under review and in preparation

Information in (and not in) interest rates surveys (with Laura Coroneo).

Abstract: We show that standard term structure models for observed interest rates fail to capture interest rate survey expectations. We therefore propose a joint term structure model for observed interest rates and interest rate surveys that allows for separate objective and subjective probability measures. Our results contradict the previous term structure literature and provide evidence that interest rate surveys do not help identify observed interest rate dynamics. Yet, despite this evidence against the rational expectation hypothesis, we find that surveys provide valuable information as a priced risk factor that is not spanned by observed interest rates.