Working Papers
URI permanente desta comunidadehttps://repositorio.insper.edu.br/handle/11224/3232
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Working Paper A macro-financial analysis of the corporate bond market(2018) Dewachter, Hans; Iania, Leonardo; Lemke, Wolfgang; Lyrio, Marco Túlio PereiraWe assess the contribution of economic and Önancial factors in the determination of euro area corporate bond spreads over the period 2001-2015. The proposed multi-market, no arbitrage a¢ ne term structure model is based on the methodology proposed by Dewachter, Iania, Lyrio, and Perea (2015). We model jointly the ërisk-free curveí, measured by overnight index swap (OIS) rates, and the corporate yield curves for two rating classes (A and BBB). The model includes four spanned and six unspanned factors. We Önd that, in general, both economic (real activity and ináation) and Önancial factors (proxying risk aversion, áight to liquidity and general Önancial market stress) play a signiÖcant role in the determination of the spanned factors and hence in the dynamics of the risk-free yield curve and corporate bond spreads. Across the risk-free OIS curve, macroeconomic and Önancial factors are each responsible on average for explaining 30 and 65 percent of yield varation, respectively. For A and BBB-rated corporate debt, the selected Önancial variables explain on average 50 percent of the variation in corporate spreads during the last decade.Working Paper A New-Keynesian Model of the Yield Curve with Learning Dynamics: A Bayesian Evaluation(2011) Dewachter, Hans; Iania, Leonardo; Lyrio, Marco Túlio PereiraWe estimate a New-Keynesian macro-finance model of the yield curve incorporating learning by private agents with respect to the long-run expectation of ináation and the equilibrium real interest rate. A preliminary analysis shows that some liquidity premia, expressed as a degree of mispricing relative to no-arbitrage restrictions, and time variation in the prices of risk are important features of the data. These features are, therefore, included in our learning model. The model is estimated on U.S. data using Bayesian techniques. The learning model succeeds in explaining the yield curve movements in terms of macroeconomic shocks. The results also show that the introduction of a learning dynamics is not sufficient to explain the rejection of the extended expectations hypothesis. The learning mechanism, however, reveals some interesting points. We observe an important diference between the estimated ináation target of the central bank and the perceived long-run ináation expectation of private agents, implying the latter were weakly anchored. This is especially the case for the period from mid-1970s to mid-1990s. The learning model also allows a new interpretation of the standard level, slope, and curvature factors based on macroeconomic variables. In line with standard macro-finance models, the slope and curvature factors are mainly driven by exogenous monetary policy shocks. Most of the variation in the level factor, however, is due to shocks to the output-neutral real rate, in contrast to the mentioned literature which attributes most of its variation to long-run ináation expectations.Working Paper Information in the Yield Curve: A Macro-Finance Approach(2011) Dewachter, Hans; Iania, Leonardo; Lyrio, Marco Túlio PereiraThis paper uses an affine term structure model that incorporates macroeconomic and financial factors to study the term premium in the U.S. bond market. The results corroborate the known rejection of the expectation hypothesis and indicate that one factor, closely related to the Cochrane and Piazzesi (2005) factor (the CP factor), is responsible for most of then variation in bond premia. Furthermore, the model-implied bond premia are able to explain around 32% and 40% of the variability of one- and two-year excess returns and their out-of-sample performance is comparable to the one obtained with the CP factor. The model is also used to decompose yield spreads into an expectations and a term premium componente in order to forecast GDP growth and ináation. Although this decomposition does not seem important to forecast GDP growth it is crucial to forecast ináation for most forecasting horizons. Also, the inclusion of control variables such as the short-term interest rate and lagged variables does not drive out the predictive power of the yield spread decomposition.