A re-examination of Libor rigging: a time-varying cointegration perspective

References

• Abrantes-Metz, R. and Bajari, P., Screens for conspiracies and their multiple applications-extended. Compet. Policy Int. J., 2010, 6(2).
   Google Scholar
• Abrantes-Metz, R.M., Villas-Boas, S.B. and Judge, G.G., Tracking the Libor rate. Appl. Econ. Lett., 2011, 18, 893–899.
   Web of Science ®Google Scholar
• Abrantes-Metz, R., Kraten, M., Metz, A.D. and Seow, G.S., LIBOR manipulation? J. Banking Finance, 2012, 36, 136–150.
   Web of Science ®Google Scholar
• Bai, J. and Collin-Dufresne, P., The CDS-Bond basis. The American Finance Association 2013 San Diego Meetings Paper, 2013. Available online at SSRN: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2024531.
   Google Scholar
• Bollerslev, T., Engle, R.F. and Kroner, K.F., ARCH modeling in finance. J. Econ., 1992, 52, 5–59.
   Web of Science ®Google Scholar
• British Bankers’ Association. BBA Libor explained: The basics, 2012. Available online at: http://www.bbalibor.com/bbalibor-explained/the-basics.
   Google Scholar
• Brunnemeier, M.K., Deciphering the liquidity and credit crunch 2007–08. J. Econ. Perspect., 2009, 23, 77–100.
   PubMed Web of Science ®Google Scholar
• Carter, C.K. and Kohn, R., On Gibbs sampling for state space models. Biometrika, 1994, 81, 541–553.
   Web of Science ®Google Scholar
• Chan, J. and Jeliazkov, I., Efficient simulation and integrated likelihood estimation in state space models. Int. J. Math. Model. Numer. Optim., 2009, 1, 101–120.
   Google Scholar
• Chan, J., Koop, G., Leon-Gonzalez, R. and Strachan, R., Time varying dimension models. J. Bus. Econ. Stat., 2012, 30, 358–367.
   Web of Science ®Google Scholar
• Chib, S., Markov chain monte carlo methods: Computation and inference. In Handbook of Econometrics, edited by J.J. Heckman, and E. Leamer, Vol. 5, 2001 (North Holland).
   Google Scholar
• Chua, C.L., Suardi, S. and Tsiaplias, S., Predicting short-term interest rates using Bayesian model averaging: Evidence from weekly and high frequency data. Int. J. Forecasting, 2013, 29, 442–455.
   Web of Science ®Google Scholar
• Durbin, J. and Koopman, S.J., A simple and efficient simulation smoother for state space time series analysis. Biometrika, 2002, 89, 603–616.
   Web of Science ®Google Scholar
• Duffie, D. and Stein, J., Reforming Libor and other financial-market benchmarks. Working Paper No. 3170, Stanford University, 2014.
   Google Scholar
• Eisl, A., Jankowitsch, R. and Subramanyam, M.G., Areinterest rate fixings fixed? An analysis of Libor and Euribor. Working Paper, 2013.
   Google Scholar
• Engle, R.F. and Granger, C.W.J., Co-integration and error correction: Representation, estimation, and testing. Econometrica, 1987, 55, 251–276.
   Web of Science ®Google Scholar
• Fouquau, J. and Spieser, P.K., Statistical evidence about Libor manipulation: A “Sherlock Holmes” investigation. J. Banking Finance, 2015, 50, 632–643.
   Web of Science ®Google Scholar
• Garz, M., Unemployment expectations, excessive pessimism, and news coverage. J. Econ. Psychol., 2013, 34, 156–168.
   Web of Science ®Google Scholar
• Gefang, D., Koop, G. and Potter, S.M., Understanding liquidity and credit risks in the financial crisis. J. Empirical Finance, 2011, 18, 903–914.
   Web of Science ®Google Scholar
• Geweke, J., Bayesian treatment of the independent Student’s t linear model. Supplement: Special issue on econometric inference using simulation techniques. J. Appl. Econ. 1993, 8, S19–S40.
   Web of Science ®Google Scholar
• Geweke, J., Bayesian comparison of econometric models. Federal Reserve Bank of Minneapolis Working Paper, 1994.
   Google Scholar
• Geweke, J. and Amisano, G., Comparing and evaluating Bayesian predictive distributions of asset returns. Int. J. Forecasting, 2010, 26, 216–230.
   Web of Science ®Google Scholar
• Glosten, L.R., Jagannathan, R. and Runkle, D.E., On the relation between the expected value and the nominal excess return on stocks. J. Finance, 1993, 48, 1779–1801.
   Web of Science ®Google Scholar
• Hassler, U. and Wolters, J., Autoregressive distributed lag models and cointegration. Adv. Stat. Anal., 2006, 90, 59–74.
   Google Scholar
• Hou, A. and Suardi, S., Modelling and forecasting short-term interest rate volatility: A semiparametric approach. J. Empirical Finance, 2011, 18, 692–710.
   Web of Science ®Google Scholar
• Imakubo, K., Kimura, T. and Nagano, T., Cross-currency transmission of money market tensions, Bank of Japan Review E-2, 2008.
   Google Scholar
• Ji, P., Time-varying financial stress linkages: Evidence from the LIBOR-OIS spreads. J. Int. Financ. Markets Inst. Money, 2012, 22, 647–657.
   Web of Science ®Google Scholar
• Ji, P. and In, F., The impact of the global financial crisis on the cross-currency linkage of LIBOR-OIS spreads. J. Int. Financ. Markets Inst. Money, 2010, 20, 575–589.
   Google Scholar
• Katrakilidis, C. and Trachanas, E., What drives housing price dynamics in Greece: New evidence from asymmetric ARDL cointegration. Econ. Modell., 2012, 29, 1064–1069.
   Web of Science ®Google Scholar
• Kitagawa, G., Non-Gaussian state-space modeling of nonstationarity time series. J. Am. Stat. Assoc., 1987, 82, 1032–1041.
   Web of Science ®Google Scholar
• Kotecha, J.H. and Djuric, P.M., Gibbs sampling approach for generation of truncated multivariate Gaussian random variables. In ICASSP ’99: Proceedings of the Acoustics, Speech, and Signal Processing on IEEE International Conference, IEEE Computer Society, Washington, DC, 1999, 1757–1760.
   Google Scholar
• Kuo, D., Skeie, D. and Vickery, J., A comparison of Libor to other measures of bank borrowing costs. Federal Reserve Bank of New York Working Paper, 2012.
   Google Scholar
• Lee, J. and Strazicich, M.C., Break point estimation and spurious rejections with endogenous unit root tests. Oxford Bull. Econ. Stat., 2001, 63, 535–558.
   Web of Science ®Google Scholar
• Lee, J. and Strazicich, M.C., Minimum Lagrange multiplier unit root test with two structural breaks. Rev. Econ. Stat., 2003, 85, 1082–1089.
   Web of Science ®Google Scholar
• MacKinnon, J.G., Critical values for cointegration tests. Queen’s Economics Department Working Paper, 1227, 1991.
   Google Scholar
• McCausland, W.J., Miller, S. and Pelletier, D., Simulation smoothing for state-space models: A computational efficiency analysis. Comput. Stat. Data Anal., 2011, 55, 199–212.
   Web of Science ®Google Scholar
• Michaud, F. and Upper, C., What drives interest rates? Evidence from the Libor panel. BIS Q. Rev., 2008, 47–58.
   Google Scholar
• Mollenkamp, C. and Whitehouse, M., Study casts doubt on key rate; WSJ analysis suggests banks may have reported flawed interest data for Libor. Wall Street Journal, 2008, (May 29) A1.
   Google Scholar
• Monticini, A. and Thornton, D., The effect of underreporting on LIBOR rates. J. Macroecon., 2013, 37, 345–348.
   Web of Science ®Google Scholar
• Nunes, L., Newbold, P. and Kuan, C., Testing for unit roots with structural breaks: evidence on the great crash and the unit root hypothesis reconsidered. Oxford Bull. Econ. Stat., 1997, 59, 435–48.
   Web of Science ®Google Scholar
• Park, J.Y. and Hahn, S.B., Cointegrating regressions with time varying coefficients. Econ. Theory, 1999, 15, 664–703.
   Web of Science ®Google Scholar
• Perron, P., Further evidence on breaking trend functions in macroeconomic variables. J. Econometrics, 1997, 80, 355–385.
   Web of Science ®Google Scholar
• Pesaran, M.H. and Shin, Y., An autoregressive distributed lag modelling approach to cointegration analysis. In Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, edited by S. Strom, 1999 (Cambridge University Press: Cambridge).
   Google Scholar
• Pesaran, M.H., Shin, Y. and Smith, R.J., Bounds testing approaches to the analysis of level relationships. J. Appl. Econ., 2001, 16, 289–326.
   Web of Science ®Google Scholar
• Polson, N., Convergence of Markov chain Monte Carlo algorithms. In Bayesian Statistics edited by J.M. Bernardo, et al., Vol. 5, pp. 297–323, 1996 (Oxford University Press: Oxford).
   Google Scholar
• Robert, C.P., Simulation of truncated normal variables. Stat. Comput., 1995, 5, 121–125.
   Web of Science ®Google Scholar
• Schwarz, K., Mind the gap: Disetangling credit and liquidity in risk spreads. Working Paper, University of Pennsylvania, Philadelphia, PA, 2016.
   Google Scholar
• Sengupta, R. and Tam, Y., The LIBOR-OIS spread as a summary indicator. Monetary Trends, Federal Reserve Bank of St. Louis. November 25 2008.
   Google Scholar
• Snider, C.A. and Youle, T., Does the Libor reflect banks’ borrowing costs?. Working Paper, 2010.
   Google Scholar
• Stenfors, A., Libor deception and central bank forward (mis-)guidance: Evidence from Norway during 2007–2011. J. Int. Financ. Markets Inst. Money, 2014, 32, 452–472.
   Web of Science ®Google Scholar
• Suardi, S., Nonstationarity, cointegration and structural breaks in the Australian term structure of interest rates. Appl. Econ., 2010, 42, 2865–2879.
   Web of Science ®Google Scholar
• Verhenyen, F., Exchange rate nonlinearities in EMU exports to the U.S. Econ. Modell., 2013, 32, 66–76.
   Web of Science ®Google Scholar
• Zivot, E. and Andrews, D., Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. J. Bus. Econ. Stat., 1992, 10, 251–270.
   Web of Science ®Google Scholar