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Evolution of the Monetary Transmission Mechanism in the US: the Role of Asset Returns

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Abstract

This paper investigates whether changes in the monetary transmission mechanism as captured by the interest rate respond to variations in asset returns. We distinguish between low-volatility (bull) and high-volatility (bear) markets and employ a TVP-VAR approach with stochastic volatility to assess the evolution of the interest rate in relation to housing and stock returns. We measure the relative importance of housing and stock returns in the movements of the interest rate and their possible feedback effects over both time and horizon and across regimes. Empirical results from annual data on the US spanning the period from 1890 to 2012 indicate that the interest rate responds more strongly to asset returns during low-volatility (bull) regimes. While the bigger interest-rate effect of stock-return shocks occurs prior to the 1970s, the interest rate appears to respond more strongly to housing-return than stock return shocks after the 1970s. Similarly, a higher interest rate exerts a larger effect on both asset categories during low-volatility (bull) markets. Particularly, larger negative responses of housing return to interest-rate shocks occur after the 1980s, corresponding to the low-volatility (bull) regime in the housing market. Conversely, the stock-return effect of interest-rate shocks dominates before the 1980s, where stock-market booms achieved more importance.

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Notes

  1. For example, Kim and Nelson (1999), McConnell and Perez-Quiros (2000), Blanchard and Simon (2001), and Ahmed et al. (2004), among others, document a structural change in the volatility of U.S. GDP growth, finding a rather dramatic reduction in GDP volatility. Stock and Watson (2003), Bhar and Hamori (2003), Mills and Wang (2003), and Summers (2005) show a structural break in the volatility decline of the output growth rate for Japan and other G7 countries, although the break occurs at different times.

  2. Boivin (2005) identifies important changes in the monetary policy rules with a weak response to inflation in 1970s which gradually strengthened from the early 1980s. The evolution of the US monetary policy mechanism received confirmation by Koop et al. (2009) and Sims and Zha (2006).

  3. Based on the Bai and Perron (2003) tests for structural breaks, with 5-percent trimming of the sample and at the 10 % significance level, we detect as many as five breaks in each of the three equations of the VAR model. The dates for the interest rate, real housing returns, and real stock returns equations correspond, respectively, as follows: 1898, 1969, 1975, 1981, and 1987; 1918, 1931, 1939, 1947, and 2007, and; 1900, 1908, 1941, 1947, and 2003. Complete details of these results are available from the authors.

  4. See Nakajima (2011) and Primiceri (2005) for further details on the TVP-VAR methodology.

  5. Random-walk processes allow for both temporary and permanent shifts in the coefficients. The drifting coefficient captures a possible non-linearity, such as a gradual change or a structural break. In practice, this assumption implies a possibility that the time-varying coefficients capture not only the true movement but also some spurious movements, because the parameters can freely move under the random-walk assumption. In other words, a risk exists for the time-varying coefficients to over fit the data, if the relationships between the variables are obscure. To avoid this situation, it may prove better to assume stationarity for the time-varying coefficients. For example, we can model each coefficient to follow an AR(1) process, where the absolute value of the persistence parameter is less than one. In this formulation, however, the estimation of a structural change or a permanent shift of the coefficient will prove difficult even if it exists. In other words, it is important to choose the model specification of the time-varying coefficients such that it is suitable for the data, the economic theories, and the purpose of the analysis (Nakajima 2011).

  6. Based on the suggestion of an anonymous referee, however, we also computed the impulse response functions by ordering the interest rate last with real housing and equity returns ordered first and second, respectively, and then interchanging the order of stock and housing returns, but still retaining interest rate in the third position. The results were qualitatively similar and are available from the authors.

  7. http://www.econ.yale.edu/~shiller/data.htm.

  8. We use standard unit-root tests: Augmented-Dickey-Fuller (ADF) (Dickey and Fuller 1981), Phillips-Perron (PP) (Phillips and Perron 1988), Dickey-Fuller with Generalised-Least-Squares-detrending (DF-GLS) (Elliott et al. 1996), and the Ng-Perron modified version of the PP (NP-MZt) (Ng and Perron 2001) tests to confirm that the log-levels of the asset-price variables under consideration follow an integrated process of order 1 or I(1) processes. The unit-root tests are available from the authors.

  9. The MCMC method assesses the joint posterior distributions of the parameters of interest based on certain prior probability densities that are set in advance. This paper implements the code of Nakajima (2011) by assuming the following priors: Σ β  ∼ IW(25, 0.01I), (Σ a ) − 2 i  ∼ G(4, 0.02), (Σ h ) − 2 i  ∼ G(4, 0.02), where (Σ a ) − 2 i and (Σ h ) − 2 i are the ith diagonal of elements of Σ a and Σ h , respectively. IW and G denote the inverse Wishart and the Gamma distributions, respectively. We use flat priors to set initial values of time-varying parameters such that: \( {\mu_{\beta}}_{{}_0}={\mu_a}_{{}_0}={\mu_h}_{{}_0}=0 \) and \( {\varSigma_{\beta}}_{{}_0}={\varSigma_a}_{{}_0}={\varSigma_h}_{{}_0}=10\times I. \)

  10. Based on the suggestions of an anonymous referee, we used the posterior of the first 10 observations as priors for the sample period covering 1901–2012. Our results were, however, qualitatively similar to the ones obtained based on the prior specifications discussed in Footnote 9. Further details on these results are available from the authors.

  11. Intuitively, high volatility leads to need for a higher return, which the market can accomplish with a lower asset price. Ang et al. (2006) state “Periods of high volatility also tend to coincide with downward market movements (see French et al. 1987; Campbell and Hentschel 1992).” (p. 259).

  12. Refer to the estimation results in Table 2 dealing with the identification of the bull and bear regimes in the housing and stock markets.

  13. For a standard constant-parameter VAR model, the impulse responses are drawn for each set of two variables. By contrast, for the TVP-VAR model, we can draw the impulse responses in an additional dimension (i.e., Compute the responses at all points in time using the estimated time-varying parameters). In that case, several ways exist to simulate the impulse responses based on the parameter estimates of the TVP-VAR model. Considering the comparability over time, we follow Nakajima (2011) and compute the impulse responses by fixing an initial shock size equal to the time-series average of stochastic volatility over the sample period and use the simultaneous relations at each point in time. To compute the recursive innovation of the variable, we use the estimated time-varying coefficients from the current date to future periods. Around the end of the sample period, we set constant the coefficients in future periods for convenience.

  14. Based on the suggestions of an anonymous referee, we also estimated a two-regime Bayesian Markov-Switching (MS) VAR ordered as R, RHP, and RSP. Balcilar et al. (2015) report complete details of the Bayesian MS-VAR. Based on impulse response functions obtained from 2000 posterior draws with a burn-in of 1000 draws, the results suggest that positive interest rate shocks depress asset prices in both regimes, though some evidence exists of stock returns appreciating at shorter horizons, but this effect is statistically insignificant. Further, positive asset returns shocks lead to a positive response of the interest rate. So, in general, our results are similar to the single-regime constant parameter VAR model. Note, however, that we cannot compare the results with our TVP-VAR model, as the number of regimes in the TVP-VAR equals the number of observations (i.e., 122 in our case). Further, the results are time varying and not based on full-sample, two-regime estimation as in the MS-VAR. Complete details of these results are available from the authors.

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Authors and Affiliations

  1. University of Pretoria, Pretoria, 0002, South Africa

    Beatrice D. Simo-Kengne, Rangan Gupta & Mehmet Balcilar

  2. University of Nevada, Las Vegas, Las Vegas, NV, 89154-6005, USA

    Stephen M. Miller

  3. Department of Economics, Eastern Mediterranean University, via Mersin 10, Famagusta, Northern Cyprus, Turkey

    Mehmet Balcilar

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  1. Beatrice D. Simo-Kengne
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Correspondence to Stephen M. Miller.

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We thank an anonymous referee for many helpful comments. Any remaining errors, however, are solely ours

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Simo-Kengne, B.D., Miller, S.M., Gupta, R. et al. Evolution of the Monetary Transmission Mechanism in the US: the Role of Asset Returns. J Real Estate Finan Econ 52, 226–243 (2016). https://doi.org/10.1007/s11146-015-9512-5

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