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Record W3123353851

How China to U.S. Foreign Exchange Rate Relates to U.S. Interest Rate and Bank Loans

2013· article· en· W3123353851 on OpenAlexaboutno aff
Nguyễn Văn Hòa

Bibliographic record

VenueThe Global Journal of Business Research · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsGross domestic productPurchasing power parityExchange rateInterest rateCurrencyMonetary economicsPer capitaForeign-exchange reservesBalance of tradeReal gross domestic productLoanInternational economicsFinanceMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACTThis research investigates the interactions of U.S. interest rate, the different types of bank loans at all U.S. commercial banks, production activities and the foreign exchange rate between U.S. and China. This paper uses monthly data from 1981 to 2012 to show that some U.S. bank-loan-related macro-economic indicators are related to exchange rates between U.S. and China. The results demonstrate that U.S. short-term federal funds rate, U.S. manufacturing capacity utilization, and three types of banks loans at all U.S. commercial banks could be good predictors and determinants of the overall exchange rate between these two important international currencies.JEL: F31, F33KEYWORDS: Foreign Exchange, Interest Rate, Loans, U.S., China.INTRODUCTIONThe continued strength and vitality of the US economy continues to attract economics forecasters. According to the International Monetary Fund, the U.S. GDP of $15.1 trillion constitutes 22% of the gross world product at market exchange rates and over 19% of the gross world product at purchasing power parity (PPP). Though larger than any other nation's, its national GDP is about 5% smaller than the GDP of the European Union at PPP in 2008. The country ranks ninth in the world in nominal GDP per capita and sixth in GDP per capita at PPP. The U.S. dollar is the world's primary reserve currency. The United States is the largest importer of goods and third largest exporter, though exports per capita are relatively low. In 2010, the total U.S. trade deficit was $635 billion. Canada, China, Mexico, Japan, and Germany are its top trading partners. In 2010, oil was the largest import commodity, while transportation equipment was the country's largest export. China is the largest foreign holder of U.S. public debt.China has experienced a remarkable period of rapid growth spanning three decades, shifting from a centrally planned to a market based economy with reforms begun in 1978. During this time, it grew at an average rate of about 9.7% per year, with exceptionally strong growth in the period of 2003-2007 averaging about 1 1% per year. Growth remained strong during the recent global financial crisis, reflecting massive stimulus and strong underlying growth drivers. China became the world's second largest economy in 2010. Increasingly, it is playing an important and influential role in the global economy.Research about the relation of US interest rates, and other factors with the foreign exchange rates between US and China plays an important role. However, it is difficult to predict the exchange rate movements since there are many short-term and long-term factors and disconnections between the leading macroeconomic indicators and the nominal exchange rates (Hellerstein, 2008). This study investigates the interactions of some U.S. indicators in the banking system as a whole, such as interest rates and outstanding bank loans to determine the exchange rate between the U.S. dollar and China Yuan. These two countries are top economic entities in the world and their currencies are most traded in the foreign exchange (Forex) market (Wikipedia, 2012). U.S. and China also are important mutual trading partners with significant imports and exports in goods and services.The remainder of the article is organized as follows. The next section reviews the literature development of U.S interest rates, the categories of outstanding banks loans at all U.S. commercial banks and the foreign exchange rate of U.S. and China. It also points out the direction & focused issues of the current research which will contribute to the existing body of literature. Section 3 describes the methodology, data collection procedures and the formation of five hypothesis & final sample. Section 4 discusses the empirical results. Section 5 presents the summary and conclusions focusing on the implications and ideas for further research.LITERATURE REVIEWIn foreign exchange markets, interest rates are an important factor for the exchange rate. …

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.297
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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