IDEAS home Printed from https://ideas.repec.org/r/eee/jimfin/v24y2005i2p363-385.html
   My bibliography  Save this item

Empirical exchange rate models and currency risk: some evidence from density forecasts

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Jian Wang & Jason J. Wu, 2012. "The Taylor Rule and Forecast Intervals for Exchange Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 103-144, February.
  2. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Tinbergen Institute Discussion Papers 08-050/4, Tinbergen Institute.
  3. Burak Saltoglu & M. Ege Yazgan, 2012. "The Role of Regime Shifts in the Term Structure of Interest Rates: Further Evidence from an Emerging Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(S5), pages 48-63, November.
  4. Chen, Hongyi & Cao, Shuo, 2019. "Exchange Rate Movements and Fundamentals: Impact of Oil Prices and the People’s Republic of China’s Growth," ADBI Working Papers 938, Asian Development Bank Institute.
  5. Gábor Regős & Xibin Zhang, 2015. "Modeling the exchange rate using price levels and country risk," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1056928-105, December.
  6. Sarno, Lucio & Valente, Giorgio, 2006. "Deviations from purchasing power parity under different exchange rate regimes: Do they revert and, if so, how?," Journal of Banking & Finance, Elsevier, vol. 30(11), pages 3147-3169, November.
  7. Lucio Sarno, 2005. "Viewpoint: Towards a solution to the puzzles in exchange rate economics: where do we stand?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 38(3), pages 673-708, August.
  8. Fraire, Francisco & Leatham, David J., 2006. "Decision Making Tool to Hedge Exchange Rate Risk," 2006 Agricultural and Rural Finance Markets in Transition, October 2-3, 2006, Washington, DC 133082, Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition.
  9. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
  10. Charlie X. Cai & Qi Zhang, 2016. "High†Frequency Exchange Rate Forecasting," European Financial Management, European Financial Management Association, vol. 22(1), pages 120-141, January.
  11. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
  12. Ferdinand Dreher & Johannes Gräb & Thomas Kostka, 2020. "From carry trades to curvy trades," The World Economy, Wiley Blackwell, vol. 43(3), pages 758-780, March.
  13. Jian Wang & Jason J. Wu, 2012. "The Taylor Rule and Forecast Intervals for Exchange Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 103-144, February.
  14. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
  15. Naveed, Hafiz Muhammad & HongXing, Yao & Memon, Bilal Ahmed & Ali, Shoaib & Alhussam, Mohammed Ismail & Sohu, Jan Muhammad, 2023. "Artificial neural network (ANN)-based estimation of the influence of COVID-19 pandemic on dynamic and emerging financial markets," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
  16. de Souza Vasconcelos, Camila & Hadad Júnior, Eli, 2023. "Forecasting exchange rate: A bibliometric and content analysis," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 607-628.
  17. Bekiros, Stelios & Avdoulas, Christos & Hassapis, Christis, 2018. "Nonlinear equilibrium adjustment dynamics and predictability of the term structure of interest rates," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 140-155.
  18. Shuo Cao & Hongyi Chen, 2017. "Exchange Rate Movements and Fundamentals: Impact of Oil Prices and China¡¯s Growth," Working Papers 042017, Hong Kong Institute for Monetary Research.
  19. Lee, Hsiang-Tai & Lee, Chien-Chiang, 2022. "A regime-switching real-time copula GARCH model for optimal futures hedging," International Review of Financial Analysis, Elsevier, vol. 84(C).
  20. Donald Lien & Hsiang‐Tai Lee & Her‐Jiun Sheu, 2018. "Hedging systematic risk in the commodity market with a regime‐switching multivariate rotated generalized autoregressive conditional heteroskedasticity model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(12), pages 1514-1532, December.
  21. Xiao-Ming Li & Qing Xu, 2007. "Evaluating density forecasts of the model with a conditional skewed-t distribution for China's stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 18(3), pages 213-227.
  22. José Valentim Machado Vicente & Jaqueline Terra Moura Marins & Wagner Piazza Gaglianone, 2021. "Impacts of the Monetary Policy Committee Decisions on the Foreign Exchange Rate in Brazil," Working Papers Series 552, Central Bank of Brazil, Research Department.
  23. Avdoulas Christos & Bekiros Stelios & Lucey Brian, 2020. "The term structure of Eurozone peripheral bond yields: an asymmetric regime-switching equilibrium correction approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-23, September.
  24. Lee, Hsiang-Tai, 2009. "Optimal futures hedging under jump switching dynamics," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 446-456, June.
  25. Hsiang‐Tai Lee, 2022. "A Markov regime‐switching Cholesky GARCH model for directly estimating the dynamic of optimal hedge ratio," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(3), pages 389-412, March.
  26. Lee, Hsiang-Tai, 2010. "Regime switching correlation hedging," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2728-2741, November.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.