Show simple item record

dc.creatorMolinas Sosa, Luis
dc.creatorBinner, Jane
dc.creatorTong, Meng
dc.date.accessioned2021-08-09T13:59:58Z
dc.date.available2021-08-09T13:59:58Z
dc.date.created2021-08-02
dc.date.issued2021-08-02
dc.identifier.citationArteta, C., Kose, M. A., Stocker, M., and Taskin, T. (2016). Negative interest rate policies: Sources and implications
dc.identifier.citationBalassa, B. (1964). The purchasing-power parity doctrine: a reappraisal. Journal of Political Economy, 72(6):584–596.
dc.identifier.citationBall, L., Gagnon, J., Honohan, P., and Krogstrup, S. (2016). What else can central banks do? ICMB International Center for Monetary and Banking Studies.
dc.identifier.citationBanbura, M., Giannone, D., and Reichlin, L. (2007). Bayesian vars with large panels.
dc.identifier.citationBarnett, W. A. (1978). The user cost of money. Economics letters, 1(2):145–149.
dc.identifier.citationBarnett, W. A. (1980). Economic monetary aggregates: An application of aggregation and index number theory. Journal of Econometrics, 14:11–48.
dc.identifier.citationBarnett, W. A. and Binner, J. M. (2004). Functional structure and approximation in econometrics. Emerald Group Publishing Limited
dc.identifier.citationBarnett, W. A. and Kwag, C. (2006). Exchange rate determination from monetary fundamentals: an aggregation theoretic approach. Frontiers in Finance and Economics, page P4.
dc.identifier.citationBarnett, W. A., Offenbacher, E. K., and Spindt, P. A. (1984). The new divisia monetary aggregates. Journal of Political Economy, 92(6):1049–1085.
dc.identifier.citationBarnett, W. A. and Serletis, A. (2000). The theory of monetary aggregation. Emerald Group Publishing Limited.
dc.identifier.citationBarnett, W. A. and Wu, S. (2005). On user costs of risky monetary assets. Annals of Finance, 1(1):35–50.
dc.identifier.citationBelongia, M. T. (2006). The neglected price dual of monetary quantity aggregates. Money, Measurement and Computation. New York: Palgrave Macmillan.
dc.identifier.citationBinner, J. M., Bissoondeeal, R. K., Elger, T., Gazely, A. M., and Mullineux, A. W. (2005). A comparison of linear forecasting models and neural networks: an application to euro inflation and euro divisia. Applied Economics, 37(6):665–680.
dc.identifier.citationBinner, J. M., Chaudhry, S., Kelly, L., and Swofford, J. L. (2018). “risky” monetary aggregates for the uk and us. Journal of International Money and Finance, 89:127–138.
dc.identifier.citationBittner, C., Bonfim, D., Heider, F., Saidi, F., Schepens, G., and Soares, C. (2020). Why so negative? the effect of monetary policy on bank credit supply across the euro area. Unpublished working paper.
dc.identifier.citationBohnet, A., Hong, Z., and M¨uller, F. (1993). China’s open-door policy and its significance for transformation of the economic system. Intereconomics, 28(4):191–197.
dc.identifier.citationChang, D., Mattson, R. S., and Tang, B. (2019). The predictive power of the user cost spread for economic recession in china and the us. International Journal of Financial Studies, 7(2):34.
dc.identifier.citationCheung, Y.-W., Chinn, M. D., Pascual, A. G., and Zhang, Y. (2019). Exchange rate prediction redux: new models, new data, new currencies. Journal of International Money and Finance, 95:332–362.
dc.identifier.citationDas, S. (2019). China’s evolving exchange rate regime. IMF Working Paper.
dc.identifier.citationDiebold, F. X. and Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic statistics, 13(3):253–263.
dc.identifier.citationDornbusch, R. (1976). Expectations and exchange rate dynamics. Journal of Political Economy, 84(6):1161– 1176.
dc.identifier.citationEdge, R. M., Kiley, M. T., and Laforte, J.-P. (2010). A comparison of forecast performance between federal reserve staff forecasts, simple reduced-form models, and a dsge model. Journal of Applied Econometrics, 25(4):720–754.
dc.identifier.citationFaust, J., Rogers, J. H., and Wright, J. H. (2003). Exchange rate forecasting: the errors we’ve really made. Journal of International Economics, 60(1):35–59.
dc.identifier.citationFrankel, J. A. (1979). On the mark: A theory of floating exchange rates based on real interest differentials. The American Economic Review, 69(4):610–622.
dc.identifier.citationFrenkel, J. A. (1976). A monetary approach to the exchange rate: doctrinal aspects and empirical evidence. The Scandinavian Journal of economics, pages 200–224.
dc.identifier.citationGhosh, T. and Bhadury, S. (2018). Money’s causal role in exchange rate: Do divisia monetary aggregates explain more? International Review of Economics & Finance.
dc.identifier.citationHeider, F., Saidi, F., and Schepens, G. (2021). Banks and negative interest rates.
dc.identifier.citationHoesch, L., Rossi, B., and Sekhposyan, T. (2020). Has the information channel of monetary policy disap peared? revisiting the em
dc.identifier.citationHooper, P. and Morton, J. (1982). Fluctuations in the dollar: A model of nominal and real exchange rate determination. Journal of international Money and Finance, 1:39–5
dc.identifier.citationJobst, A. and Lin, H. (2016). Negative interest rate policy (NIRP): implications for monetary transmission and bank profitability in the euro area. International Monetary Fund.
dc.identifier.citationKeating, J. W., Kelly, L. J., Smith, A. L., and Valcarcel, V. J. (2019). A model of monetary policy shocks for financial crises and normal conditions. Journal of Money, Credit and Banking, 51(1):227–259.
dc.identifier.citationLace, N., Maˇcerinskien˙e, I., and Balˇci¯unas, A. (2015). Determining the eur/usd exchange rate with us and german government bond yields in the post-crisis period. Intellectual Economics, 9(2):150–155.
dc.identifier.citationLitterman, R. B. (1986). Forecasting with bayesian vector autoregressions—five years of experience. Journal of Business & Economic Statistics, 4(1):25–38.
dc.identifier.citationLothian, J. R. and Wu, L. (2011). Uncovered interest-rate parity over the past two centuries. Journal of International Money and Finance, 30(3):448–473.
dc.identifier.citationMark, N. C. (1995). Exchange rates and fundamentals: Evidence on long-horizon predictability. The American Economic Review, pages 201–218.
dc.identifier.citationMcMahon, M., Schipke, M. A., and Li, X. (2018). China’s monetary policy communication: Frameworks, impact, and recommendations. International Monetary Fund.
dc.identifier.citationMeese, R. A. and Rogoff, K. (1983). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of international economics, 14(1-2):3–24.
dc.identifier.citationReimers, H.-E. et al. (2002). Analysing divisia aggregates for the euro area. Technical report, Discussion paper Series 1/Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank.
dc.identifier.citationSarantis, N. (2006). On the short-term predictability of exchange rates: A bvar time-varying parameters approach. Journal of Banking & Finance, 30(8):2257–2279
dc.identifier.citationSchunk, D. L. (2001). The relative forecasting performance of the divisia and simple sum monetary aggre gates. Journal of Money, Credit and Banking, pages 272–28
dc.identifier.citationSch¨ussler, R., Beckmann, J., Koop, G., and Korobilis, D. (2018). Exchange rate predictability and dynamic bayesian learning.
dc.identifier.citationWright, J. H. (2008). Bayesian model averaging and exchange rate forecasts. Journal of Econometrics, 146(2):329–341.
dc.identifier.urihttps://repositorio.bcp.gov.py/handle/123456789/183
dc.description.abstractThis paper contributes to the literature as the first work of its kind to examine the role and importance of Divisia monetary aggregates and concomitant user cost price indices as superior monetary policy fore casting tools in a negative interest rate environment. We compare the performance of Divisia monetary aggregates with traditional simple-sum aggregates in several theoretical models and in a Bayesian VAR to forecast the exchange rates between the euro, the dollar and yuan renminbi at various horizons using quarterly data. We evaluate their performance against that of a random-walk using two criteria: Root Mean Square Error ratios and the Diebold-Mariano statistic. We find that, under a free-floating exchange regime, superior Divisia monetary aggregates outperform their simple sum counterparts and the bench mark random walk in negative interest rate environment and non-negative interest rate environments, consistently.
dc.format.extent22
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherBanco Central del Paraguay
dc.relation.ispartofDocumentos de Trabajo
dc.relation.ispartofseriesDocumento de Trabajo
dc.relation.isversionofDocumento de Trabajo; N° 25
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.titleDo Divisia monetary aggregates help forecast exchange rates in a negative interest rate environment?
dc.typeWorking Paper
dc.subject.jelC01
dc.subject.jelspaC01
dc.subject.keywordFORECASTING
dc.subject.keywordEXCHANGE RATES
dc.subject.keywordBAYESIAN VECTOR AUTOREGRESSION
dc.subject.keywordUNCOVERED INTEREST RATE
dc.subject.keywordSTICKY PRICE
dc.rights.accessRightsOpen Access
dc.type.spaDocumento de Trabajo
dc.type.hasversionPublished Version
dc.rights.ccCC0 1.0 Universal
dc.rights.spaAcceso abierto
dc.audience


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

http://creativecommons.org/publicdomain/zero/1.0/This work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit