Santa Clara University

Research : Arunina Sinha

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  • iconLearning and the Yield Curve

    Two central implications of the Expectations Hypothesis under rational expectations are inconsistent with yield curve data: (i) future expected long yields fall, instead of rising, when the yield spread rises, and (ii) long yields are excessively volatile with respect to short yields.In this paper, I document these puzzles in the U.S. and the U.K. data, for different sub-samples, and for both real and nominal yields. I then propose a micro-founded optimization framework in which boundedly rational agents use adaptive learning to form expectations. The model is successful on both dimensions. First, the belief structure rationalizes the pattern of yields observed in the data so that the first puzzle does not arise with subjective instead of rational expectations. In particular, intertemporal income and substitution effects are amplified relative to the rational expectations case, causing expected long yields to rise when the yield spread falls. The second puzzle is partly accounted for by the extra volatility due to parameter uncertainty. These results suggest that it is the assumption of rational expectations that is at odds with the data, not the (subjective) Expectations Hypothesis. In addition, I find that: the model generates systematic forecast errors in yields and inflation that are consistent with survey data and higher yield volatilities during different monetary policy regimes match the U.S. data.

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  • iconFiscal Policy, Learning, and the Yield Curve
    This paper analyzes the effects of changes in government debt on the term structure of interest rates. A structural vector-autoregression is used to estimate the effects of government debt on the yield curve: a 1% rise in real debt to GDP is found to increase the three-month and ten-year rates by 30 and 21 basis points respectively. These effects are difficult to obtain in rational expectations models. They can, however, be partly derived in a general equilibrium model in which the government issues riskless debt and the optimizing agents are adaptive learners. Long-term exponentially maturing debt in the model is calibrated to match the average maturity of U.S. Treasury debt since the 1980s. To test the empirical consistency of the model, the implied term structure of yields is tested for the Expectations Hypothesis; rejections of the Hypothesis, consistent with the U.S. experience, are obtained. Positive effects of government debt on asset yields are generated since the individual agents do not learn the principle of Ricardian equivalence, although on average, the beliefs are centered around rational expectations beliefs. In this case, increases in holdings of government bonds by agents are perceived as a rise in their net wealth.

     

  • iconExploring the Role of Habit Formation and Inflation Indexation

    in Explaining Deviations from the Expectations Hypothesis

    In this paper, I explore whether infinite-horizon adaptive learning can continue to explain deviations from the Expectations Hypothesis when external habit formation and inflation indexation are introduced in the framework of Sinha (2010). External habits in consumption interact with learning to generate more persistent forecasting errors, leading to a larger downward bias in the Campbell-Shiller (1991) coefficients. Inflation indexation is more important for lowering model implied volatilities of inflation and interest rates. I also investigate the degree to which the learning specification is important for explaining the deviations from the Expectations Hypothesis by using Euler-equation learning. The model is found to be significantly less successful.

 
 
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