scholarly journals ARE EXCHANGE RATES REALLY RANDOM WALKS? SOME EVIDENCE ROBUST TO PARAMETER INSTABILITY

2005 ◽  
Vol 10 (1) ◽  
pp. 20-38 ◽  
Author(s):  
BARBARA ROSSI
Keyword(s):  
Exchange Rate ◽  
Random Walk ◽  
Exchange Rates ◽  
Random Walks ◽  
Parameter Instability ◽  
Nested Models ◽  
Exchange Rate Fluctuations ◽  
Causality Tests ◽  
The Relationship ◽  
Better Than

Many authors have documented that it is challenging to explain exchange rate fluctuations with macroeconomic fundamentals: a random walk forecasts future exchange rates better than existing macroeconomic models. This paper applies newly developed tests for nested models that are robust to the presence of parameter instability. The empirical evidence shows that for some countries we can reject the hypothesis that exchange rates are random walks. This raises the possibility that economic models were previously rejected not because the fundamentals are completely unrelated to exchange rate fluctuations, but because the relationship is unstable over time and, thus, difficult to capture by Granger causality tests or by forecast comparisons. We also analyze forecasts that exploit the time variation in the parameters and find that, in some cases, they can improve over the random walk.

scholarly journals Exchange Rate Predictability

2013 ◽  
Vol 51 (4) ◽  
pp. 1063-1119 ◽  
Author(s):  
Barbara Rossi
Keyword(s):  
Exchange Rate ◽  
Random Walk ◽  
Exchange Rates ◽  
Evaluation Method ◽  
Recent Literature ◽  
Forecast Horizon ◽  
Exchange Rate Fluctuations ◽  
New Methodologies ◽  
Exchange Rate Predictability ◽  
Period Model

The main goal of this article is to provide an answer to the question: does anything forecast exchange rates, and if so, which variables? It is well known that exchange rate fluctuations are very difficult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogoff puzzle). However, the recent literature has identified a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an up-to-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: “Are exchange rates predictable?” is, “It depends”—on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift. (JEL C53, F31, F37, E43, E52)

scholarly journals Analisis Lindung Nilai Dengan Menggunakan Swap Dan Forward Untuk Mengurangi Resiko Transaction Eksposure

2018 ◽  
Vol 5 (1) ◽  
pp. 48-57
Author(s):  
Farhana . ◽  
Ratih Puspitasari
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Company Performance ◽  
Exchange Rate Fluctuations ◽  
The One ◽  
The Relationship ◽  
A Company ◽  
Simple Regression

Hedging is the one of activities is performed by a company or a bank to protect their business from the risk of changes caused by exchange rates or exchange rate fluctuations. There are some things that should be considered by companies whom often perform the transactions related to interest rate and exchange rate. For example, banks should monitor the company performance against losses caused by foreign exchange and also arrange the strategy, because it is necessary for the company to perform hedging. The research is purposed to learn how to minimize the risks of losses experienced by banks due to hedging techniques usage. The hedging techniques performed are swap and forward hedging. The method used in this study correlation coefficient and simple regression to determine the relationship between the variables in question. The result of this research shows that hedging technique in the buying and selling transactions of foreign exchange has more benefit rather than not. It can avoid the risks of transaction exposures. It can be seen from the significant level, Forward has the significant level in the amount of 0,000. It means the Forward Hedging Technique has an effect to the transaction exposure and Hedging transaction using Swap has the benefit rather than not. The significant level in the amount of 0,007 is smaller than 0,05. It means that Swap Hedging Technique has an effect to the transaction exposure. Accordingly, Forward and Swap deserve to be considered by companies or banks to avoid the risks of transaction exposures.

Testing Structural Break in the Relationship Between Exchange Rate and Macroeconomic Variables

2019 ◽  
Vol 23 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Saidia Jeelani ◽  
Joity Tomar ◽  
Tapas Das ◽  
Seshanwita Das
Keyword(s):  
Exchange Rate ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Series Data ◽  
Ripple Effect ◽  
Macroeconomic Factors ◽  
Parameter Instability ◽  
Macroeconomic Variables ◽  
The Relationship

The article aims to study the relationship between those macroeconomic factors that the affect (INR/USD) exchange rate (ER). Time series data of 40 years on ER, GDP, inflation, interest rate (IR), FDI, money supply, trade balance (TB) and terms of trade (ToT) have been collected from the RBI website. The considered model has suggested that only inflation, TB and ToT have influenced the ER significantly during the study period. Other macroeconomic variables such as GDP, FDI and IR have not significantly influenced the ER during the study period. The model is robust and does not suffer from residual heteroscedasticity, autocorrelation and non-normality. Sometimes the relationship between ER and macroeconomic variables gets affected by major economic events. For example, the Southeast Asian crisis caused by currency depreciation in 1997 and sub-prime loan crisis of 2008 severely strained the national economies. Any global economic turmoil will affect different economic variables through ripple effect and this, in turn, will affect the ER of different economies differently. The article has also diagnosed whether there is any structural break or not in the model by applying Chow’s Breakpoint Test and have obtained multiple breaks between 2003 and 2009. The existence of structural breaks during 2003–2009 is explained by the fact that volume of crude oil imported by India is high and oil price rise led to a deficit in the TB alarmingly, which caused a structural break or parameter instability.

Monetary and Exchange Rate Policies for International Financial Stability: A Proposal

1988 ◽  
Vol 2 (1) ◽  
pp. 83-103 ◽  
Author(s):  
Ronald I McKinnon
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Purchasing Power Parity ◽  
Financial Stability ◽  
Foreign Exchange Market ◽  
The United States ◽  
Power Parity ◽  
Monetary Reform ◽  
Exchange Rate Fluctuations

What keeps the three major industrial blocs -- Western Europe, North America, and industrialized Asia -- from developing a common monetary standard to prevent exchange-rate fluctuations? One important reason is the differing theoretical perspectives of economic advisers. The first issue is whether or not a floating foreign exchange market -- where governments do not systematically target exchange rates -- is “efficient.” Many economists believe that exchange risk can be effectively hedged in forward markets so international monetary reform is unnecessary. Second, after a decade and a half of unremitting turbulence in the foreign exchange markets, economists cannot agree on “equilibrium” or desirable official targets for exchange rates if they were to be stabilized. The contending principles of purchasing power parity and of balanced trade yield very different estimates for the “correct” yen/dollar and mark/dollar exchange rates. Third, if the three major blocs can agree to fix nominal exchange rates within narrow bands, by what working rule should the new monetary standard be anchored to prevent worldwide inflation or deflation? After considering the magnitude of exchange-rate fluctuations since floating began in the early 1970s, I analyze these conceptual issues in the course of demonstrating how the central banks of Japan, the United States, and Germany (representing the continental European bloc) can establish fixed exchange rates and international monetary stability.

scholarly journals Vehicle Currency Pricing and Exchange Rate Pass-Through

2021 ◽  
Author(s):  
Natalie Chen ◽  
Wanyu Chung ◽  
Dennis Novy
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Great Recession ◽  
Firm Level ◽  
Bilateral Exchange ◽  
The Great Recession ◽  
Pass Through ◽  
Exchange Rate Changes ◽  
Exchange Rate Pass Through ◽  
The Relationship

Abstract Using detailed firm-level transactions data for UK imports, we find that invoicing in a vehicle currency is pervasive, with more than half of the transactions in our sample invoiced in neither sterling nor the exporter’s currency. We then study the relationship between invoicing currencies and the response of import unit values to exchange rate changes. We find that for transactions invoiced in a vehicle currency, import unit values are much more sensitive to changes in the vehicle currency than in the bilateral exchange rate. Pass-through therefore substantially increases once we account for vehicle currencies. This result helps to explain why UK inflation turned out higher than expected when sterling depreciated during the Great Recession and after the Brexit referendum. Finally, within a conceptual framework we show why bilateral exchange rates are not suitable for capturing exchange rate pass-through under vehicle currency pricing. Overall, our results help to clarify why the literature often finds a disconnect between exchange rates and prices when vehicle currencies are not accounted for.

An Application of Genetic Programming to Forecasting Foreign Exchange Rates

2010 ◽  
pp. 26-48
Author(s):  
Muneer Buckley ◽  
Zbigniew Michalewicz ◽  
Ralf Zurbruegg
Keyword(s):  
Exchange Rate ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Economic Model ◽  
Dynamic Environments ◽  
Foreign Exchange Rates ◽  
Better Than ◽  
Made In

There is a great need for accurate predictions of foreign exchange rates. Many industries participate in foreign exchange scenarios with little idea where the exchange rate is moving, and what the optimum decision to make at any given time is. Although current economic models do exist for this purpose, improvements could be made in both their flexibility and adaptability. This provides much room for models that do not suffer from such constraints. This chapter proposes the use of a genetic program (GP) to predict future foreign exchange rates. The GP is an extension of the DyFor GP tailored for forecasting in dynamic environments. The GP is tested on the Australian / US (AUD/USD) exchange rate and compared against a basic economic model. The results show that the system has potential in forecasting long term values, and may do so better than established models. Further improvements are also suggested.

An Empirical Analysis of FII Movement and Currency Value in India

2015 ◽  
pp. 207-217
Author(s):  
Saurabh Sen ◽  
Ruchi L. Sen
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Critical Role ◽  
Institutional Investment ◽  
Currency Futures ◽  
Overall Performance ◽  
Foreign Institutional Investors ◽  
Us Dollar ◽  
The Impact ◽  
The Relationship

India opened its stock market to foreign investors in September 1992 and has received portfolio investment from foreigners in the form of foreign institutional investment in equities and other markets including derivatives. It has emerged as one of the most influential groups to play a critical role in the overall performance of the Indian economy. The liberalization of FII flows into the Indian capital market since 1993 has had a significant impact on the economy. With increased volatility in exchange rate and to mitigate the risk arising out of excess volatility, currency futures were introduced in India in 2008, which is considered a second important structural change. This chapter examines the impact of the Foreign Institutional Investors (FIIs) on the exchange rate and analyzes the relationship between FII and Indian Rupee-US Dollar exchange rates.

scholarly journals The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models

2020 ◽  
Vol 13 (3) ◽  
pp. 48 ◽  
Author(s):  
Yuchen Zhang ◽  
Shigeyuki Hamori
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Random Walk ◽  
Exchange Rates ◽  
Price Index ◽  
Economic Models ◽  
Superior Performance ◽  
Support Vector ◽  
Out Of Sample ◽  
Modern Machine

In 1983, Meese and Rogoff showed that traditional economic models developed since the 1970s do not perform better than the random walk in predicting out-of-sample exchange rates when using data obtained after the beginning of the floating rate system. Subsequently, whether traditional economical models can ever outperform the random walk in forecasting out-of-sample exchange rates has received scholarly attention. Recently, a combination of fundamental models with machine learning methodologies was found to outcompete the predictability of random walk (Amat et al. 2018). This paper focuses on combining modern machine learning methodologies with traditional economic models and examines whether such combinations can outperform the prediction performance of random walk without drift. More specifically, this paper applies the random forest, support vector machine, and neural network models to four fundamental theories (uncovered interest rate parity, purchase power parity, the monetary model, and the Taylor rule models). We performed a thorough robustness check using six government bonds with different maturities and four price indexes, which demonstrated the superior performance of fundamental models combined with modern machine learning in predicting future exchange rates in comparison with the results of random walk. These results were examined using a root mean squared error (RMSE) and a Diebold–Mariano (DM) test. The main findings are as follows. First, when comparing the performance of fundamental models combined with machine learning with the performance of random walk, the RMSE results show that the fundamental models with machine learning outperform the random walk. In the DM test, the results are mixed as most of the results show significantly different predictive accuracies compared with the random walk. Second, when comparing the performance of fundamental models combined with machine learning, the models using the producer price index (PPI) consistently show good predictability. Meanwhile, the consumer price index (CPI) appears to be comparatively poor in predicting exchange rate, based on its poor results in the RMSE test and the DM test.

EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

2005 ◽  
Vol 01 (01) ◽  
pp. 79-107 ◽  
Author(s):  
MAK KABOUDAN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Us Dollar ◽  
Artificial Neural ◽  
Japanese Yen ◽  
The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

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