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Private information, stock markets, and exchange rates

Author

Listed:
  • Jacob Gyntelberg
  • Mico Loretan
  • Tientip Subhanij
  • Eric Chan
Abstract
Explaining exchange rates has long been an important but vexing issue in international economics and finance. In recent years, a number of studies have shown that investors' private information plays a central role in determining exchange rates. We demonstrate in this paper that the private information of investors relevant for exchange rates is largely connected to the stock market, and that this information is conveyed to foreign exchange (FX) markets by order flow that is induced by investors' transactions in the stock market. We establish these results by analyzing several novel unused datasets on nearly two years' worth of daily-frequency capital flows of nonresident investors in the foreign exchange, stock, and bond markets of Thailand. We present compelling evidence that FX order flow that is induced by nonresident investors transactions in the Stock Exchange of Thailand - which we show are driven largely by private information - has far greater explanatory power for the exchange rate than other order flow has, both in the short run and the long run. In contrast, FX order flow of nonresident investors that is related to their transactions in Thai government bonds - which we find are not driven appreciably by private information - does not have a statistically significant effect on the exchange rate.

Suggested Citation

  • Jacob Gyntelberg & Mico Loretan & Tientip Subhanij & Eric Chan, 2009. "Private information, stock markets, and exchange rates," BIS Working Papers 271, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:271
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    Cited by:

    1. Abhakorn, Pongrapeeporn & Tantisantiwong, Nongnuch, 2012. "A reexamination of capital controls’ effectiveness: Recent experience of Thailand," Journal of Asian Economics, Elsevier, vol. 23(1), pages 26-38.
    2. Linlin Ye, 2016. "Understanding the Impacts of Dark Pools on Price Discovery," Papers 1612.08486, arXiv.org.
    3. Gyntelberg, Jacob & Loretan, Mico & Subhanij, Tientip, 2018. "Private information, capital flows, and exchange rates," Journal of International Money and Finance, Elsevier, vol. 81(C), pages 40-55.
    4. Hyeyoen Kim & Doojin Ryu, 2013. "Forecasting Exchange Rate from Combination Taylor Rule Fundamental," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 81-92, September.
    5. Riane de Bruyn & Rangan Gupta & Renee van Eyden, 2013. "Forecasting The Rand-Dollar And Rand-Pound Exchange Rates Using Dynamic Model Averaging," Working Papers 201307, University of Pretoria, Department of Economics.
    6. Andrew Filardo & Jason George & Mico Loretan & Guonan Ma & Anella Munro & Ilhyock Shim & Philip Wooldridge & James Yetman & Haibin Zhu, 2010. "The international financial crisis: timeline, impact and policy responses in Asia and the Pacific," BIS Papers chapters, in: Bank for International Settlements (ed.), The international financial crisis and policy challenges in Asia and the Pacific, volume 52, pages 21-82, Bank for International Settlements.
    7. Panagiotis Papaioannnou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Papers 1310.5306, arXiv.org.
    8. Panagiotis Papaioannou & Lucia Russo & George Papaioannou & Constantinos Siettos, 2013. "Can social microblogging be used to forecast intraday exchange rates?," Netnomics, Springer, vol. 14(1), pages 47-68, November.
    9. Eric Girardin & Dijun Tan & Woon K. Wong, 2010. "Information Content of Order Flow and Cross-market Portfolio Rebalancing: Evidence for the Chinese Stock, Treasury and Corporate Bond Markets," Working Papers 022010, Hong Kong Institute for Monetary Research.
    10. Danglun Luo & Qianwei Ying, 2014. "Political Connections and Bank Lines of Credit," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 5-21, May.
    11. Don H Kim & Mico Loretan & Eli M Remolona, 2010. "Contagion and risk premia in the amplification of crisis: evidence from Asian names in the global CDS market," BIS Papers chapters, in: Bank for International Settlements (ed.), The international financial crisis and policy challenges in Asia and the Pacific, volume 52, pages 318-339, Bank for International Settlements.
    12. German Rodriguez, 2008. "Survival models (in Russian)," Quantile, Quantile, issue 5, pages 1-27, September.

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    More about this item

    Keywords

    Exchange rate models; market microstructure approach; asymmetric information; Thailand; generated regressors; impulse response functions; I(1) measurement error;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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