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Volatility spillover effects in interbank money markets

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Abstract

Using daily data from 2006 to 2015, this paper applies alternative multivariate GARCH models and a modified version of the spillover index methodology proposed by Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) to test the existence of shock and volatility contagion effects across interbank money markets. Overall, we find evidence that money markets are highly interrelated, exhibiting dynamic cross market effects. Moreover, we emphasize the pertinence of conditional covariances and we show that volatility spillovers are time-varying and very responsive to the major economic events, increasing in periods of higher turbulence, which reinforces the importance of closely monitoring the evolution of money markets.

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Notes

  1. In this context the acronym VARMA-GARCH stands for a GARCH with the VARMA recursion for the variances. However, this expression is frequently used to mention a standard GARCH model with a VARMA specification for the conditional mean, which can originate a doubt of interpretation or a misunderstanding.

  2. As an example among different alternatives, it is possible to mention the differential between unsecured money market rates and general collateral repurchase agreement (GC Repos) for the same term or the gap between unsecured interbank money market rates with different maturity buckets.

  3. As previously discussed, in general, LIBOR rates trade above OIS rates. However, it has not been observed in the CHF money market because different fixing conventions. In truth, this effect is explained due to the monetary policy implemented by the SNB that, in December 2014, lowered its deposit rate and its target for the 3-month CHF LIBOR into negative territory. Yet, negative deposit rates were only applied to reserves above a specific threshold. So, the LIBOR fixing is established as the interest rate at which banks are willing to borrow, while the OIS fixing is defined as the interest rate at which banks are willing to lend. Given that the rate at which a bank is willing to lend hinges on its level of reserves, its comparison with a benchmark borrowing rate should be read with caution.

  4. Throughout the paper the notation used to describe the different models follows the nomenclature adopted by the respective authors in the original articles.

  5. For further details on this setting, we recommend the reading of Diebold and Yilmaz (2009, 2012, 2014).

  6. All models are estimated with p = q = r = s = 1. In spite of presenting the estimates for the conditional mean, we will focus the analysis on the estimates for the conditional volatility. Additionally, since \(\eta_{t}\) does not follow a joint multivariate normal distribution, we use the Quasi-MLE (QMLE) estimators.

  7. In fact, as the assumption of constant conditional correlations is maintained in all the estimated models, the respective matrices can be compared. Overall, the correlations between the conditional shocks for all specifications do not seem to differ substantially from one another.

  8. As argued by Cappiello et al. (2006), despite being ideal to test each series for a break at all points in time, this procedure is infeasible, whereby it is usual to choose a specific date for this purpose. In order to avoid this process, and in spirit of Blanchard and Perotti (2002), we discard the possible existence of structural breaks occurring in a stationary GARCH process by verifying that the squared residuals from the AG-DCC GARCH model do not lie outside the interval given by their corresponding mean plus/minus 2 standard deviations.

  9. The appropriate number of lags for the VAR model is determined by minimizing the SBC and, similarly to Diebold and Yilmaz (2012), we use a 10 step-ahead forecast error variance.

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Acknowledgements

We thank Editor Cédric Tille and two anonymous reviewers for their comments and suggestions. All remaining errors are our own.

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The views expressed are those of the authors and do not necessarily represent those of the Banco de Portugal or the Eurosystem.

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Authors and Affiliations

  1. Economics and Research Department, Banco de Portugal, Av. Almirante Reis, 71, 6th floor, 1150-012, Lisbon, Portugal

    Pedro Pires Ribeiro

  2. Instituto Universitário de Lisboa, ISCTE–IUL, UNIDE, Complexo INDEG/ISCTE, Av. Prof. Aníbal Bettencourt, 1600-189, Lisbon, Portugal

    Pedro Pires Ribeiro & José Dias Curto

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  1. Pedro Pires Ribeiro
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Ribeiro, P.P., Curto, J.D. Volatility spillover effects in interbank money markets. Rev World Econ 153, 105–136 (2017). https://doi.org/10.1007/s10290-016-0268-7

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