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Yield curve responses to market sentiments and monetary policy

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Abstract

Central banks recently started to target longer term interest rates. The empirical failure of the rational expectations theory of the yield curve, however, limits its applicability to monetary policy analysis. The success of agent-based behavioral asset pricing models and behavioral macroeconomic models in replicating statistical regularities of empirical data series motivates to apply them to yield curve modeling. This paper analyses how the interaction of monetary policy and market sentiments shape the yield curve in a behavioral model with heterogeneous and bounded-rational agents. One result is that the behavioral model replicates empirical facts of term structure data. Moreover, it overcomes one major deficiency of rational expectations models of the yield curve in explaining the empirically observed uncertain responses of longer term yields to changes in the central bank rate. These are explained by the behavioral model’s ability to generate different responses of market sentiments to shocks at different times which lead to a variety of interest rate responses. Further results of this paper can be used as policy advice on how central banks can target the level, slope and curvature of the yield curve by targeting market sentiments about inflation and the business cycle.

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Notes

  1. These empirical properties or stylized facts are the clustering of volatility, the leptokurtic return distribution for example. Behavioral asset pricing models are very successful in replicating these stylized facts (see Lux and Marchesi 2000; Westerhoff 2008; Demary 2010, 2011) for example. De Grauwe (2010a, b, c, 2012) shows that macroeconomic models with heterogeneous and bounded-rational agents are successful in replicating the stylized facts of U.S. output gap data, like the persistence of output and its leptokurtic distribution.

  2. There is a growing number of behavioral macroeconomic models like Anufriev et al. 2013; Bofinger et al. 2013; Branch and Evans 2006, 2010; De Grauwe and Macchiarelli 2013; Lengnick and Wohltmann 2012.

  3. Empirical studies have found out that the dynamics of the yield curve can be sufficiently well modeled by three principle components which are highly correlated with measures of the level, slope and curvature of the yield curve (see Rudebusch 2010).

  4. Bekaert et al. (2010) and Kozicki and Tinsley (2001) find that long-term inflation expectations that correspond to the central bank’s inflation target are a level-factor of the yield curve, while monetary policy is a slope-factor of the yield curve.

  5. Ellingsson and Söderström (1998), Kozicki and Sellon (2005) and Rudebusch et al. (2006) document this variety of yield curve responses.

  6. The rich dynamics arise from the behavioral model’s ability to generate different market sentiments for each realization of the macroeconomic shocks as found in De Grauwe (2010a, b, c, 2012).

  7. This result is in line with the findings of De Grauwe (2010a, b, c, 2012).

  8. Behavioral finance models that incorporate short-term and longer-term forecasting rules of heterogeneous and bounded-rational agents can be found in Demary (2008, 2010).

  9. The New Keynesian Phillips Curve is derived from the price setting behavior of forward-looking firms who are constrained in that way, that they are only allowed to set a fraction of their prices in response to new economic developments (De Grauwe 2010a, b, c, 2012). This mechanism was originally proposed by (Calvo 1983).

  10. Following De Grauwe (2010a, b, c, 2012) the aggregate demand equation is derived from the optimization problem of a forward-looking household which is also characterized by habit-persistence in consumption.

  11. Carlstrom and Fuerst (2008) note that the inertial interest rate rule yields empirically a better fit to the Federal Funds Rate data compared to the originally proposed rule by Taylor (1993).

  12. Demary (2008, 2010) contain behavioral finance models that assume heterogeneous and bounded-rational agents with multi-period forecasting horizons.

  13. A similar approach, the evolutionary selection among competing trading rules is widely used in behavioral asset pricing models, e.g. Brock and Hommes (1997), Westerhoff (2008), and Demary (2010, 2011).

  14. The equilibrium real rate, however, is not restricted to be a positive number; it can also be zero or negative, like overlapping generations models with aging and shrinking societies predict under stable prices and in the absence of any sovereign debt. In aging and shrinking societies savings for retirement are high, while investment demand is low. With stable prices and in the absence of any sovereign debt, therefore a negative equilibrium real interest rate has to equate savings and investment (Weizsäcker 2013).

  15. Using the R package ’bean plot’ written by Kampstra (2008). Calculations were done using R (see R Development Core Team 2008).

  16. See De Grauwe (2010a, b, c, 2012) for a comprehensive description of impulse responses in non-linear models.

  17. For the single policy parameters we use a coarse grid for the policy parameters but bean plots in order to get information about the variation between the 50 simulation runs. When we analyze the variation of more than one parameter, we use a finer grid for the policy parameters but only report the averages over 50 simulation runs in order to plot additional curves for a variation of a second policy parameter. As in the subsections before all remaining parameters are fixed at the values given in Table 1. Simulations for all parameter values are again based on the same seed of random variables.

  18. This result is in line with the result of De Grauwe (2010a, b, c, 2012) that some output stabilization by the central bank helps to stabilize inflation as long as the output stabilization parameter is not too large.

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Correspondence to Markus Demary.

Appendix

Appendix

See Figs. 15, 16 and 17.

Fig. 15
figure 15

Yield curve factors, output weight and inflation target persistence. Note: Each point is the average over 50 simulation runs of the standard deviation of the level, slope and curvature of the yield curve that were calculated from simulations of size 2200 from which the first 200 data points were removed. Different colors for different values of the inflation target persistence parameter (blue \(\kappa =0.0\), red \(\kappa =0.5\), green \(\kappa =0.95\)) (color figure online)

Fig. 16
figure 16

Yield curve factors, output weight and inflation target volatility. Note: Each point is the average over 50 simulation runs of the standard deviation of the level, slope and curvature of the yield curve that were calculated from simulations of size 2200 from which the first 200 data points were removed. Different colors for different values of the inflation target volatility (blue \(\sigma ^{IT}=0.05\), red \(\sigma ^{IT}=0.2\), green \(\sigma ^{IT}=0.5\)) (color figure online)

Fig. 17
figure 17

Yield curve factors, output weight and longest maturity. Note: Each point is the average over 50 simulation runs of the standard deviation of the level, slope and curvature of the yield curve that were calculated from simulations of size 2200 from which the first 200 data points were removed. Different colors for different values of the longest maturity of the yield curve (blue \(N=8\), red \(N=12\), green \(N=16\)) (color figure online)

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Demary, M. Yield curve responses to market sentiments and monetary policy. J Econ Interact Coord 12, 309–344 (2017). https://doi.org/10.1007/s11403-015-0167-3

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