The US Dollar/Euro Exchange Rate: Structural Modeling and Forecasting During the Recent Financial Crises

Morana, Claudio

doi:10.1002/for.2430

Cited by 11 publications

(3 citation statements)

References 27 publications

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“…That betas are changing over time is a well-established empirical fact (e.g., Fama & French, 1997;Lettau & Ludvigson, 2001;Avramov & Chordia, 2006;Daniel & Moskowitz, 2016). Indeed, Morana (2007) and Becker et al (2021) document that betas clearly have long-memory properties in the time series, rendering a constant approximation over the entire sample period particularly inaccurate. The cross-sectional test can easily account for changing betas.…”

Section: The Cross-sectional Vs the Time-series Testmentioning

confidence: 99%

Testing Factor Models in the Cross-Section

Hollstein¹,

Prokopczuk²

2021

SSRN Journal

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Section: The Cross-sectional Vs the Time-series Testmentioning

confidence: 99%

Testing Factor Models in the Cross-Section

Hollstein¹,

Prokopczuk²

2021

SSRN Journal

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“…The currency exchange rate of an economy remains one of the key parameters in assessing its economic and financial stability. There is a vast literature that discusses exchange rate linkages to major macro variables like interest rates, commodity prices, monetary policy formulation, economic growth, business cycle, and international trade (Calvo & Reinhart, 2002; Goldberg & Tille, 2008; Monacelli & Galí, 2005; Morana, 2017; Rogoff et al, 2003). The significance of exchange rates extends profoundly into financial markets, influencing capital flows, investment decisions, and market returns (Combes et al, 2012; Froot & Stein, 1991; Katechos, 2011).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Markov switching model for BRICS currencies' exchange rates

Kumar,

Ahmad,

Uddin

2024

Journal of Forecasting

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Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time‐varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis‐à‐vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal‐gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime‐specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime‐specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out‐of‐sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.

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“…Concise short-term forecasts support management in decision making regarding personnel planning, warehouse management, and cash flow management [5][6][7]. Numerous studies have been conducted, including forecasting using market models [8], forecasting to study a country's recession, recession forecasting as a core function of many financial institutions [9,10], and volatility forecasting using the GARCH model (1,1).…”

Section: Introductionmentioning

confidence: 99%

Implementation of Forecasting Hedging Model During the Covid-19 Pandemic with the Event Windows Approach to Asean Stock Prices 6

Supriyanto¹,

Sari²,

Pratama³

2022

Proceedings of the Universitas Lampung International Conference on Social Sciences (ULICoSS 2021)

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This study was conducted to analyze the stock exchanges of ASEAN 6 (Indonesia, the Philippines, Malaysia, Singapore, Vietnam, and Thailand) related to the dynamics of daily stock prices including the decline during the Covid-19 Pandemic. This data is described as a time series comprised of daily inventory expenditures, varied heteroscedasticity, and the ASEAN stock dummy hedging variable. The GARCH mannequin is one of the excellent styles used to address the issue of heteroscedasticity (generalized autoregressive conditional heteroscedasticity). As a result, this study seeks to develop the best appropriate model for forecasting 400 days before and 297 days following the Covid-19 epidemic, as well as to offer advice for mitigating the impact of daily stock price swings. The data was previously gathered by analyzing the daily rates of ASEAN 6 world stocks from January 1, 2019, to May 17, 2021. Additionally, the article examines the Event Window, with the ideal model denoted as AR (1) -GARCH (1, 3). The results confirmed that the model with an error of less than 0.0073 is AR (1) -GARCH (1,3), which is an excellent model for forecasting daily inventory costs in ASEAN 6.

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The US Dollar/Euro Exchange Rate: Structural Modeling and Forecasting During the Recent Financial Crises

Cited by 11 publications

References 27 publications

Testing Factor Models in the Cross-Section

Testing Factor Models in the Cross-Section

Bayesian Markov switching model for BRICS currencies' exchange rates

Implementation of Forecasting Hedging Model During the Covid-19 Pandemic with the Event Windows Approach to Asean Stock Prices 6

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