Constrained Random Walk Models for Euro/Swiss Franc Exchange Rates: Theory and Empirics

Pak-rupee exchange rates vis-à-vis many currencies of the industrial world have weakened continuously and persistently since Pakistan abandoned fixed exchange rates in April 1982. This proposition is strongly supported by descriptive test statistics, as shown in Table 1, such as mean, standard deviation and coefficient of variation of six Pak rupee exchange rates—against the U.S. dollar, British pound, German mark, Japanese yen, Swiss franc and French franc—over the period 1982q1-2000q4. Based on these descriptive statistics, it is evident that Pak rupee has depreciated persistently against all currencies of the industrial countries in question over the period under investigation; for example, it has depreciated by 324.05 percent against the British pound, 406.360 percent against the U.S. dollar, 344.53 percent against the French franc, 498.48 percent against the Swiss franc, 477.78 percent against the German mark and 986.25 percent against the Japanese yen since April 1982. As evidenced by coefficient of variation, Pak rupee has weakened enormously against all currencies of the industrial world, while it has weakened relatively more alarmingly against the Japanese yen, Swiss franc and German mark.

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

Journal of Risk and Financial Management ◽

10.3390/jrfm13030048 ◽

2020 ◽

Vol 13 (3) ◽

pp. 48 ◽

Cited By ~ 3

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.

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EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

New Mathematics and Natural Computation ◽

10.1142/s1793005705000056 ◽

2005 ◽

Vol 01 (01) ◽

pp. 79-107 ◽

Cited By ~ 12

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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ARFIMA Tests for Random Walks in Exchange Rates in Asian, Latin American and African-Middle Eastern Markets

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s0219091510001846 ◽

2010 ◽

Vol 13 (01) ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

David Karemera ◽

John Cole

Keyword(s):

Random Walk ◽

Exchange Rates ◽

Random Walks ◽

Latin American ◽

Emerging Market ◽

Middle Eastern ◽

Fractional Integration ◽

Market Exchange ◽

Forecast Performance ◽

Integrated Processes

This article examines fractional processes as alternatives to random walks in emerging foreign exchange rate markets. Sowell's (1992) joint maximum likelihood is used to estimate the ARFIMA parameters and test for random walks. The results show that, in most cases, the emerging market exchange rates follow fractionally integrated processes. Forecasts of exchange rates based on the fractionally integrated autoregressive moving average models are compared to those from the benchmark random walk models. A Harvey, Leybourne and Newbold (1997) test of equality of forecast performance indicates that the ARFIMA forecasts are more efficient in the multi-step-ahead forecasts than the random walk model forecasts. The presence of fractional integration is seen to be associated with market inefficiency in the exchange markets examined. The evidence suggests that fractional integrated processes are viable alternatives to random walks for describing and forecasting exchange rates in the emerging markets.

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The Quanto Theory of Exchange Rates

The American Economic Review ◽

10.1257/aer.20180019 ◽

2019 ◽

Vol 109 (3) ◽

pp. 810-843 ◽

Cited By ~ 18

Author(s):

Lukas Kremens ◽

Ian Martin

Keyword(s):

Random Walk ◽

Exchange Rates ◽

Purchasing Power Parity ◽

Uncovered Interest Parity ◽

Power Parity ◽

Panel Regressions ◽

Out Of Sample ◽

Covariance Term ◽

Interest Parity ◽

Forecast Variable

We present a new identity that relates expected exchange rate appreciation to a risk-neutral covariance term, and use it to motivate a currency forecasting variable based on the prices of quanto index contracts. We show via panel regressions that the quanto forecast variable is an economically and statistically significant predictor of currency appreciation and of excess returns on currency trades. Out of sample, the quanto variable outperforms predictions based on uncovered interest parity, on purchasing power parity, and on a random walk as a forecaster of differential (dollar-neutral) currency appreciation. (JEL C53, E43, F31, F37, G12, G15)

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