Time-varying relationship between conventional and unconventional monetary policies and risk aversion: international evidence from time- and frequency-domains

Abstract

This paper analyzes the time-varying relationship between risk aversion and both conventional and unconventional monetary policy in an international context and at different frequencies using a wavelet coherency analysis. Our main results suggest the existence of a dynamic relationship between the two variables depending on timescales and on the periods. Thus, a short-run negative relationship leading from the risk aversion variable to the monetary policy measure is found for most of the period, suggesting that monetary policy reacts more aggressively in periods of high risk aversion. Furthermore, during the financial crisis, we find a long-run negative relationship leading from the monetary policy to the risk aversion index, suggesting that a lax monetary policy could lead to financial instability. US monetary policy has also significant effects on the risk aversion rates in the Euro Area, Japan and the UK.

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Notes

  1. 1.

    See Torrence and Compo (1998) for more details about cross-wavelet spectrum hypothesis and confidence levels.

  2. 2.

    We refer the reader to Ho et al. (2010) and Tiwari et al. (2020) for definition of these technical issues.

  3. 3.

    The main principles underlying SSR estimates are: Yield curve data, i.e., market-quoted interest rates of different times to maturity, are influenced by the policy interest rate (PIR) and unconventional monetary policy (UMP) tools (when the latter are in use). The yield curve may be considered as two components: a shadow yield curve without a near-ZLB constraint that can therefore freely take on negative values; and a physical currency option effect that results in a near-ZLB constraint on interest rates. Applying a shadow or lower bound model to yield curve data allows the shadow yield curve and the option effect components to be separately estimated. The SSR is then the shortest-maturity interest rate on the shadow yield curve, just like the PIR is the shortest-maturity interest rate on the actual yield curve. Because the shadow or lower bound model is estimated from yield curve data across UMP and conventional monetary policy (CMP) periods, the resulting SSR series provides a consistent and comparable metric for the stance of monetary policy over both periods.

  4. 4.

    The data can be downloaded from the following link: https://www.rbnz.govt.nz/research-and-publications/research-programme/additional-research/measures-of-the-stance-of-united-states-monetary-policy/comparison-of-international-monetary-policy-measures.

  5. 5.

    As a preliminary analysis, we estimate a quantile-on-quantile regression as outlined in Sim and Zhou (2015), to understand the underlying relationship between the time-dependent phases of the two variables, i.e., the risk aversion and the interest rate. The results have been plotted in Figs. A2 and A3 in the online Appendix, and, in general, tend to suggest a theory-consistent negative relationship between the two variables across the various quantiles.

  6. 6.

    When the phase-difference is converted to an angle in the interval\(\left[ { - \pi ,\pi } \right]\), an absolute value less (larger) than $$\pi /2$$ indicates that the two series move in phase (anti-phase).

  7. 7.

    Given that the risk aversion data is available monthly as well over the period of 1986:06–2015:02, in Fig. A3 in the online Appendix, we analyzed the impact of an interest rate (i.e., the Shadow Short Rate) shock on risk aversion, and the feedback from risk aversion on to the interest rate in a vector autoregressive (VAR) model, with growth in industrial production and inflation as additional variables. The data on industrial production and consumer price index are derived from the FRED database of the Federal Reserve Bank of St. Louis. The variables in the VAR (12) are ordered as industrial production growth, inflation, interest rate and risk aversion. As can be seen, from the impulse responses, interest rate and risk aversion moves in opposite direction following a positive shock to either of these two variables. When a robustness test is conducted by replacing risk aversion with the variance risk premium (VRP), as developed by Zhou (2018) (with the data downloadable from: https://sites.google.com/site/haozhouspersonalhomepage/?authuser=1), in the VAR(12) estimated over 1990:01 to 2018:12, the earlier results continue to hold, as seen from Fig. A5 in the online Appendix.

  8. 8.

    In fact, preliminary evidence, based on the simultaneous test of causality-in-mean and causality-in-variance using a factor double autoregressive (FDAR) model, as developed by Guo et al. (2014), highlights that the null of simultaneous non-causality is strongly rejected in both directions for the US, Euro Area, Japan and the UK, as reported in Table A1 in the Online Appendix of the paper.

References

  1. Adrian T, Shin HS (2009) Money, liquidity, and monetary policy. Am Econ Rev 99(2):600–605

    Article  Google Scholar 

  2. Adrian T, Shin HS (2010) Financial intermediaries and monetary economics. In: Friedman BM, Woodford M (eds) Handbook of monetary economics. Elsevier, New York, pp 601–650

    Google Scholar 

  3. Aguiar-Conraria L, Azevedo N, Soares MJ (2008) Using wavelets to decompose the time–frequency effects of monetary policy. Phys A 387(12):2863–2878

    Article  Google Scholar 

  4. Antonakakis N, Gabauer D, Gupta R, Plakandaras V (2018) Dynamic connectedness of uncertainty across developed economies: a time-varying approach. Econ Lett 166:63–75

    Article  Google Scholar 

  5. Baruník J, Vácha L, Krištoufek L (2011) Comovement of Central European stock markets using wavelet coherence: evidence from high-frequency data. IES Working paper 22/2011, IESFSV. Charles University

  6. Bekaert G, Hoerova M, Lo Duca M (2013) Risk, uncertainty and monetary policy. J Monet Econ 60:771–788

    Article  Google Scholar 

  7. Bekaert G, Engstrom EC, Xu NR (2017) The time variation in risk appetite and uncertainty. Columbia Business School research paper no. 17-108

  8. Bonfim D, Soares C (2018) The risk-taking channel of monetary policy: exploring all avenues. J Money Credit Bank 50:1507–1541

    Article  Google Scholar 

  9. Borio C, Zhu H (2012) Capital regulation, risk-taking and monetary policy: a missing link in the transmission mechanism? J. Financ Stabil 8:236–251

    Article  Google Scholar 

  10. Bruno V, Shin HS (2015) Capital flow and the risk-taking channel of monetary policy. J Monet Econ 71:119–132

    Article  Google Scholar 

  11. Christou C, Naraidoo R, Gupta R (2020a) Conventional and unconventional monetary policy reaction to uncertainty in advanced economies: evidence from quantile regressions. Stud Nonlinear Dyn Econ 24(3):1–17

    Google Scholar 

  12. Christou C, Naraidoo R, Gupta R, Hassapis C (2020b) Monetary policy reaction to uncertainty in Japan: evidence from a quantile-on-quantile interest rate rule. Int J Finance Econ. https://doi.org/10.1002/ijfe.2258

    Article  Google Scholar 

  13. Dajcman S (2016) The bank lending channel of monetary policy and its macroeconomic effects: evidence from a sample of selected Euro area countries. Eng Econ 27(2):124–133

    Article  Google Scholar 

  14. Demirer R, Omay T, Yuksel A, Yuksel A (2018) Global risk aversion and emerging market return comovements. Econ Lett 173:118–121

    Article  Google Scholar 

  15. Evans C, Fisher J, Gourio F, Krane S (2015) Risk management for monetary policy near the Zero Lower Bound. Brook Pap Econ Act Spring 2015:141–219

    Article  Google Scholar 

  16. Gambacorta L (2009) Monetary policy and the risk-taking cannel. BIS Quarterly Review, Bank for International Settings

  17. Gambacorta L, Marqués-Ibáñez D (2011) The bank lending cannel: lessons from the crisis. Econ Policy 26:135–182

    Article  Google Scholar 

  18. Gnabo JY, Moccero DN (2015) The risk management approach to monetary policy, nonlinearity and aggressiveness: the case of the US Fed. European Central Bank WP 1792

  19. Grinsted A, Moore JC, Jevrejeva S (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 11(5/6):561–566

    Article  Google Scholar 

  20. Guiso L, Sapienza P, Zingales L (2018) Time varying risk aversion. J Financ Econ 128:403–421

    Article  Google Scholar 

  21. Guo S, Ling S, Zhu K (2014) Factor double autoregressive models with application to simultaneous causality testing. J Stat Plan Infer 148:82–94

    Article  Google Scholar 

  22. Hahn J, Jang WW, Kim S (2017) Risk aversion, uncertainty, and monetary policy in zero lower bound environments. Econ Lett 156(118):122

    Google Scholar 

  23. Ho SP, Pan C, Yeh C, Hsu Y (2010) Characteristics of and relations between housing cycles and economic fluctuations: a time-frequency analysis. In: Conference: 2nd ReCapNet conference, Mannheim, Germany. https://doi.org/10.13140/2.1.4478.544

  24. Hudgins L, Friehe CA, Mayer ME (1993) Wavelet transforms and atmopsheric turbulence. Phys Rev Lett 71(20):3279

    Article  Google Scholar 

  25. Inekwe NJ (2016) Financial uncertainty, risk aversion and monetary policy. Empir Econ 51:939–961

    Article  Google Scholar 

  26. International Monetary Fund (2016) Monetary policy and central banking. https://www.imf.org/en/About/Factsheets/Sheets/2016/08/01/16/20/Monetary-Policy-and-Central-Banking

  27. Jiménez G, Ongena S, Peydró JL, Saurina J (2014) Hazardous times for monetary policy: what do twenty-three million bank loans say about the effects of monetary policy on credit risk-taking? Econometrica 82:463–505

    Article  Google Scholar 

  28. Krippner L (2013) A tractable framework for zero lower bound Gaussian term structure models. Discussion paper, Reserve Bank of New Zealand, 2013/02

  29. Kurov A, Stan R (2018) Monetary policy uncertainty and the market reaction to macroeconomic news. J Bank Finance 86:127–142

    Article  Google Scholar 

  30. Mishkin FS (2009) Is monetary policy effective during financial crises. Am Econ Rev 99:573–577

    Article  Google Scholar 

  31. Mishkin FS (2011) Monetary policy strategy: lessons from the crisis. National Bureau of Economic Research, NBER 16755

  32. Mumtaz H, Theodoridis K (2019) Dynamic effects of monetary policy shocks on macroeconomic volatility. J Monet Econ. https://doi.org/10.1016/j.jmoneco.2019.03.011

    Article  Google Scholar 

  33. Nakamura E, Steinsson J (2018a) High frequency identification of monetary non-neutrality: the information effect. Q J Econ 133:1283–1330

    Article  Google Scholar 

  34. Nakamura E, Steinsson J (2018b) Identification in macroeconomics. J Econ Perspect 32:59–86

    Article  Google Scholar 

  35. Nave JM, Ruiz J (2015) Risk aversion and monetary policy in a global context. J Financ Stabil 20:14–35

    Article  Google Scholar 

  36. Rajan R (2006) Has finance made the world riskier? Eur Financ Manag 12:499–533

    Article  Google Scholar 

  37. Rua A, Nunes LC (2009) International comovement of stock market returns: a wavelet analysis. J Empir Finance 16(4):632–639

    Article  Google Scholar 

  38. Sim N, Zhou A (2015) Oil prices, US stock return, and the dependence between their quantiles. J Bank Finance 55:1–8

    Article  Google Scholar 

  39. Tiwari AK, Nasreen S, Shahbaz M, Hammoudeh S (2020) Time-frequency causality and connectedness between international prices of energy, food, industry, agriculture and metals. Energy Econ 85:104529

    Article  Google Scholar 

  40. Torrence C, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteor Soc 79(1):61–78

    Article  Google Scholar 

  41. Torrence C, Webster PJ (1999) Interdecadal changes in the ENSO–monsoon system. J Clim 12(8):2679–2690

    Article  Google Scholar 

  42. Zhou H (2018) Variance risk premia, asset predictability puzzles, and macroeconomic uncertainty. Ann Rev Financ Econ 10:481–497

    Article  Google Scholar 

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Acknowledgements

We would like to thank two anonymous referee for many helpful comments. However, any remaining errors are solely ours.

Funding

Juncal Cuñado gratefully acknowledges financial support from the Ministerio de Economía y Competitividad (ECO2017-83183-R).

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Correspondence to Juncal Cunado.

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Hkiri, B., Cunado, J., Balcilar, M. et al. Time-varying relationship between conventional and unconventional monetary policies and risk aversion: international evidence from time- and frequency-domains. Empir Econ (2021). https://doi.org/10.1007/s00181-020-02004-0

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Keywords

  • Risk aversion
  • Monetary policy
  • Wavelet coherency

JEL Classification

  • C49
  • E44
  • E52