Private information, capital flows, and exchange rates

Gyntelberg, Jacob; Loretan, Mico; Subhanij, Tientip

doi:10.1016/j.jimonfin.2017.10.005

Cited by 12 publications

(4 citation statements)

References 49 publications

(12 reference statements)

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“…Regarding the relationship between a financial account and the nominal exchange rate, Siourounis (2003) implemented an unrestricted VAR and found causality flowing from the financial account to the exchange rate in the UK, Germany, Switzerland and Japan. Gyntelberg et al (2015) reached the same conclusion for the economy of Thailand. On the other hand, Kandil (2009) proved that fluctuations in the exchange rate are an important determinant of the financial balance in developing countries, but fluctuations in capital flows appear, in general, to be random in many developing and industrial countries, with limited evidence regarding the systematic correlation with exchange rate fluctuations.…”

Section: Introductionsupporting

confidence: 57%

The Connection between the Exchange Rate and the Balance of Payments Accounts in the Czech Republic: An Econometric Approach

Urbanovský¹

2017

FAI

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Relationships between the nominal exchange rate, the current account and the financial account of the balance of payments in the Czech Republic are investigated in this presented paper. The implemented cointegration analysis and vector error correction model suggest one pair of Granger causality. It has been discovered that change in the current account balance Granger-causes a change in financial account balance. This relationship has the nature of two-way Granger causality, which means that a reversed relationship holds as well. Other relationships implying Granger causality were not found. Error terms were significant only in regressions with both accounts as dependent variables, which imply that only these variables return to their long-term equilibria. Because an increase in financial account surplus leads to a decrease in current account surplus (or deepening the current account deficit), excessive liberalization of the Czech financial system can lead to a large capital inflow, jeopardizes current account sustainability and results in a currency crisis in the Czech economy.

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Section: Introductionsupporting

confidence: 57%

The Connection between the Exchange Rate and the Balance of Payments Accounts in the Czech Republic: An Econometric Approach

Urbanovský¹

2017

FAI

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“…Empirical work has mostly found that capital flows related to equity purchases, rather than bond purchases, are an important determinant of exchange rates (see for example Gyntelberg et al (2015)). 12 The respective tickers are provided in Table 3 in Appendix C. 13 End-of-month is defined as the last day of a month for which data is available.…”

Section: Datamentioning

confidence: 99%

Portfolio Rebalancing in Times of Stress

Fischer

Greminger

Grisse

et al. 2017

gwp

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This paper investigates time variation in the dynamics of international portfolio equity flows. We extend the empirical model of Hau and Rey (2004) by embedding a two-state Markov regime-switching model into the structural VAR. The model is estimated using monthly data, 1995-2015, on equity returns, exchange rate returns and equity flows between the United States and advanced and emerging economies. We find that the data are consistent with portfolio rebalancing. The estimated states match periods of low and high financial stress. Our main result is that for equity flows between the United States and emerging markets, the rebalancing dynamics differ between episodes of high and low levels of financial stress. A switch from the low to the high-stress regime is associated with capital outflows from emerging markets. Once in the high stress regime, the response of capital flows to exchange rate shocks is smaller than in normal (low stress) periods.

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“…Supported by a variety of exchange rate determination theories, we can conclude that the existing exchange rate influencing factors mainly include the following two categories: The first category, traditional theoretical factors, includes balance of payments (Corte et al, 2016), foreign exchange reserves (Chiappini & Lahet, 2020; Kasman & Ayhan, 2008; Pontines & Rajan, 2011), government spending (Lee et al, 2008), interest rates (Kim, 2006; MacDonald & Nagayasu, 2000), price levels (Xu, 2003), and productivity levels (Adusei & Gyapong, 2017; Liu et al, 2019). The second category, nontraditional theoretical factors, includes commodity prices (Ferraro et al, 2015), economic uncertainty (Christou et al, 2018), stock market volatility (Doong & Wang, 2005; Gyntelberg et al, 2018; Liu & Wan, 2012; Li et al, 2018), investor psychological expectations (Heiden et al, 2013), and RMB exchange rate forward (Li et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

RMB exchange rate forecasting using machine learning methods: Can multimodel select powerful predictors?

Yu,

Li,

Wang

2023

Journal of Forecasting

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This paper aims to study the phased influencing factors of renminbi (RMB) exchange rate (CNY against USD) and investigate the predictability of the factors selected by multimodel. We first take the time points when China's main exchange reform policies are launched as the demarcation points and divide the entire sample from July 2005 to December 2020 into three periods. Then, we select the potential predictors using several sources, including all factors (without any selection), the factors selected by each of the five commonly used machine learning methods, the significantly correlated factors selected by traditional regression analysis method, and multimodel‐driven factors. Finally, we predict the exchange rate based on the above selected factors and compare the prediction results. The research results show that the main influencing factors are different in different periods, and the influence of phase events cannot be ignored. Even if their influence on the exchange rate has decreased as a result of the “811” exchange rate reform, the money supply and foreign exchange reserves continue to be the primary drivers of RMB exchange rates during the whole period of the sample. Additionally, RMB exchange rate forward is a robust influencing factor in all periods. By comparing the forecast errors, we find that the prediction accuracy of the factors selected based on multimodel is higher than that of the factors selected based on a single method or the tradition method. The findings of this paper provide the following insights for exchange rate managers: In exchange rate risk management, it is important to pay attention to the impact of macroeconomic factors such as foreign exchange reserves and the impact of staged events, and market expectations of exchange rates are equally important. At the technical level, it is recommended to improve the forecasting accuracy by forecasting exchange rates based on common factors selected by multiple better machine learning methods simultaneously rather than those selected by a single method.

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Private information, capital flows, and exchange rates

Cited by 12 publications

References 49 publications

The Connection between the Exchange Rate and the Balance of Payments Accounts in the Czech Republic: An Econometric Approach

The Connection between the Exchange Rate and the Balance of Payments Accounts in the Czech Republic: An Econometric Approach

Portfolio Rebalancing in Times of Stress

RMB exchange rate forecasting using machine learning methods: Can multimodel select powerful predictors?

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