Exchange Rate Model Research Articles

Modeling and Prediction of Exchange Rates Using Topp-Leone Burr Type X, Machine Learning and Deep Learning Models

Modeling and Prediction of Exchange Rates Using Topp-Leone Burr Type X, Machine Learning and Deep Learning Models

The macro driving factors of co-movement of RMB with other currencies in FX markets

The co-movement of the RMB exchange rate with other currencies highlights the global standing of the RMB and its significance in FX markets. This paper employs the rational expectations present-value model of exchange rates to identify the macroeconomic factors influencing this co-movement. Our finding reveals a general increase in the RMB's co-movement with other currencies, especially after the 2015 reform of the RMB exchange rate regime, known as the “811 Reform”. Factors such as the synchronizations of output and monetary policy, as well as global market sentiment significantly, can affect this co-movement significantly. However, this explanatory power is relatively weaker compared to trade linkage. The market-oriented reforms in the RMB exchange rate regime and the liberalization of capital account in China have indeed strengthened this co-movement. Additionally, various international cooperation policies taken by the Chinese government have demonstrated promotional effects. This study echoes the traditional exchange rate determination theory and offers some policy implications for the internationalization of the RMB.

Forecasting the daily exchange rate of the UK pound sterling against the US dollar

This paper is the first to use an economic theory-based model—the monetary model of exchange rates within a rational expectations present value framework—to forecast the daily exchange rate of a major currency. Our out-of-sample forecast evaluation period, spanning from 1990 to 2024, is longer than that of any other exchange rate forecasting study. We find that our model's forecasts outperform the random walk across all forecasting horizons, ranging from one day to five years. Moreover, a trading strategy based on our model's forecasts yields economically and statistically significant excess returns, surpassing those of the carry trade strategy.

Value-at-risk modeling of naira exchange rates

The naira exchange rate has experienced extreme volatility, and the use of derivatives to control interest rates and currency risks has grown quickly. These developments have made risk management research necessary. To this effect, this paper investigated the optimal volatility model that offers the best VaR forecasts and minimizes expected loss in the Naira exchange rate. The data was collected from FX time and consisted of the Euro to Naira exchange and the daily closing rate of the US dollar to Naira exchange rate from 24/10/2016 to 15/06/2018. These data were estimated using different symmetric and asymmetric GARCH models. The finding shows that there is less volatility in the USD to Naira exchange rate compared to the Euro to Naira exchange rate. The study shows that SPLINE- GJR under skewed student t distribution is the optimal model for the Euro to Naira exchange rate while GARCH is the optimal model for the US dollar to Naira exchange rate. This study suggests that model risk assessments be given careful consideration by regulators, banks, and other stakeholders and integrated into the process of operational and regulatory design.

Impact of asymmetry on exchange rate determination: The role of fundamentals

This study tries to take a new look at the exchange rate determination model by employing recent developments in time series estimation methodologies. For this purpose, the validity of the sticky price monetary exchange rate model has been searched for the Turkish lira / US dollar case. Estimation results considering both linear and non–linear modeling approaches highly support the theoretical foundations and reveal explicitly the asymmetric nature of the model. The paper infers that since the nominal exchange rate seems to be determined through the economic fundamentals, it should not be used as a policy tool with a long–term perspective.

CHEER and markets’ integration: evidence from selected East Asian economies

PurposeThis paper aims to investigate markets’ integration using the capital enhanced equilibrium exchange rate (CHEER) model for seven, highly competitive, East Asian countries.Design/methodology/approachThe sample consists of monthly observations, whereas unit root and cointegration techniques with structural shifts have been used.FindingsThe evidence shows that the weak form of the CHEER approach holds for Malaysia and Thailand. For China, Japan, Korea, Singapore and Taipei, only the uncovered interest parity condition is validated, implying capital markets integration. In contrast, for these five countries, the results indicate absence of goods’ markets integration. This outcome can be attributed to the impact of quite high non-tariff barriers and the Balassa–Samuelson effect.Originality/valueTo the best of the author’s knowledge, this is the first study that investigate markets’ integration in several East Asian economies, using the CHEER approach and more accurate price indices.

Exchange Rate Models and the Management of Forex Losses in Ghana: Modelling Exchange Rate Volatilities for Businesses

Using the Self-exciting Threshold Autoregressive Model (SETAR_M) and linear models such as the vector error correction model (VECM), and univariate models, this article specifies forecasting models for exchange rate volatilities in Ghana and compares their forecasts accuracy using Diebold–Mariano and Pesaran-Timmermann tests statistics. The relevance of this research is to equip business owners and businesses on managing forex losses and to reduce their impact on profits, productivity and employment in high volatile and unstable currency environments. The research concludes that the non-linear SETAR model is superior to the linear models in predicting short-term volatilities in exchange rates, while the fundamentally based linear model is superior for predicting long-term volatility in exchange rates. Therefore, short-term business commitments or transactions such as raw material purchases, cash expenses or incomes in foreign currencies should be planned or managed using SETAR or a non-linear model, whereas long-term contractual obligations like futures and forward contracts should be planned with a fundamentally based multivariate linear model.

Stochastic Exchange Rate Dynamics, Intervention Dynamics and the Market Efficiency Hypothesis

AbstractSince currency price fluctuations hinder economic activity, exchange rate dynamics have an effect on national economies. To have a proper exchange rate policy in place, these dynamics are essential for nations with a trade economy. This study presents and examines a distinctive stochastic dynamics exchange rate model (ESI) in order to address the challenges associated with predicting the behavior of participants in some complex economic systems, which might lead to the system’s collapse. To address the issue of ESI stability by central bank interventions (managed currency) in a specified target value, a target value technique is also provided and tested. Last but not least, we examine the noise traders’ role as a major source of market uncertainty as we look at the market efficiency hypothesis for the foreign exchange market (FX).

Perapan Metode Double Exponential Smoothing pada Prediksi Nilai Tukar Petani di Provinsi Sumatra Barat Tahun 2023

West Sumatra is one of the provinces in Indonesia where the agricultural sector is very important for the regional economy. To encourage the development of the agricultural sector, an appropriate strategy is needed for each location. One of the efforts to encourage the development of the agricultural sector is to pay attention to the availability of agricultural facilities and infrastructure, so that there are no obstacles for producers that increase production costs and harm farmers. The purpose of this study was to determine the model and prediction results of Farmer Exchange Rate in West Sumatra using the Double Exponential Smoothing Method from January 2023 to June 2023. The prediction equation for NTP in West Sumatra Province in 2023 obtained is 〖 F〗_(t+m)=110,5049+(m)0,1773with a value of α = 0.99, β = 0.0115 and MAPE value = 0.99%. The Exchange Rate in West Sumatra from January 2023 to June 2023 tends to increase. The increasing Farmer Exchange Rate in West Sumatra can have a positive impact on the agricultural sector and improve the economic welfare of farmers.

A labor value model for exchange rates in a comprehensive economy

This paper establishes a labor value model of exchange rate. According to the labor theory of value, the exchange rate is decomposed into the exchange rate of “a country’s currency and a unit of product” to obtain the main body of the model, and the capital market adjustment items measured by official foreign exchange reserves are added to obtain the complete model. In the empirical part, the appropriate index was selected, and the cointegration relationship test was carried out, and the ARDL model was established according to the test. The regression coefficient R2 of the model is 98%, and the correlation coefficient between the fitted exchange rate value and the actual exchange rate value is 0.95.

Explaining the Stylized Facts of Foreign Exchange Markets with a Simple Agent-based Version of Paul de Grauwe’s Chaotic Exchange Rate Model

AbstractWe propose a simple agent-based version of Paul de Grauwe’s chaotic exchange rate model. In particular, we assume that each speculator follows his own technical and fundamental trading rule. Moreover, a speculator’s choice between these two trading philosophies depends on his individual assessment of current market circumstances. Our agent-based model setup is able to explain a number of important stylized facts of foreign exchange markets, including bubbles and crashes, excess volatility, fat-tailed return distributions, serially uncorrelated returns and volatility clustering. A stability and bifurcation analysis of its deterministic skeleton provides us with useful insights that foster our understanding of exchange rate dynamics.

Exchange rate prediction research based on LSTM-ELM hybrid model

The fluctuation of exchange rates holds paramount importance for a country's economic and trade activities. Due to the non-stationary and nonlinear structural characteristics of exchange rate time series, accurately predicting exchange rate movements is a challenging task. Single machine learning models often exhibit lower precision in exchange rate prediction compared to combined machine learning models. Hence, employing a combined model approach aims to enhance the predictive performance of exchange rate models. Both Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) exhibit intricate structures, making their direct integration challenging. To address this issue, an innovative weighted approach is adopted in this study, combining LSTM and ELM models and further refining the combination weights using an improved Marine Predators Algorithm. This paper encompasses both univariate and multivariate prediction scenarios, employing two distinct allocation strategies for training and testing datasets. This is done to investigate the influence of different dataset allocations on exchange rate prediction. Finally, the proposed LSTM-ELM weighted combination exchange rate prediction model is compared with SVM, Random Forest, ELM, LSTM, and LSTM-ELM average combination models. Experimental results demonstrate that the LSTM-ELM weighted combination exchange rate prediction model outperforms the others in both univariate and multivariate prediction settings, yielding higher predictive accuracy and superior fitting performance. Consequently, the LSTM-ELM weighted combination prediction model proves to be effective in exchange rate forecasting.

Multi-regime foreign exchange rate model: Calibration and pricing

To price exotic foreign exchange (FX) options, a model needs to be selected for FX spot rate dynamics. The classic approach of modelling spot rates with Black–Scholes framework makes inappropriate assumptions of constant drift and volatility, resulting in mispricing. In this article, we investigate multi-regime Black–Scholes (MRBS) model for FX rate, with regime-switching behaviour of drift and volatility governed by a Markov chain. We derive an analytic formula for European FX call options via Fourier transform and present a Monte Carlo simulation algorithm for FX barrier option pricing. Further, we propose a calibration strategy with penalty. Finally, the empirical study shows that volatility smile can be recovered by MRBS model; calibrating MRBS with penalty gives stable calibrated parameters and small out-of-sample mean squared errors; combined use of our calibration methods and pricing algorithms provides reasonable prices for FX barrier options. Thus, MRBS model, together with proposed calibration-with-penalty strategy and pricing algorithm, provides a promising approach for FX option pricing.

Exchange rates and political uncertainty: the Brexit case

AbstractThis paper studies the impact of political risk on exchange rates. We focus on the Brexit Referendum as it provides a natural experiment where both exchange rate expectations and a time‐varying political risk factor can be measured directly. We build a portfolio model that relates changes in the Leave probability to changes of the British pound's market price, both via expectations and via a political risk factor. We estimate the model for multilateral and bilateral British pound exchange rates. We find that the Leave probability predicts a depreciation of the pound, consistent with the outcome post‐referendum, and that the time‐varying political risk affects exchange rates independently.

Modeling and Predicting the Egyptian Pound's Exchange Rate Per the American Dollar on a Short-Term Scale by Using ARIMA – Probability Distributions

A time series is an ordered sequence of data points that are chronologically indexed. By evaluating the values in a time series both presently and retrospectively, it is possible to predict the future values of most time series with a reasonable degree of accuracy. In this paper modeling of Egyptian pound exchange rate per US dollar in the short term by using the ARIMA model and many probability distributions. The ARIMA is the best ARIMA that assumed in this study in modeling the data set of the exchange rate of the pound in Egypt per US dollar and the Burr probability distribution is the best probability distribution in modeling the same data set.

Exchange Rate Models are Better than You Think, and Why They Didn't Work in the Old Days

Exchange Rate Models are Better than You Think, and Why They Didn't Work in the Old Days

Applied Financial Mathematical Model for Derivative Instruments and Hedging Exchange Rate

Applied Financial Mathematical Model for Derivative Instruments and Hedging Exchange Rate

Applied Financial Mathematical Model for Derivative Instruments and Hedging Exchange Rate

Applied Financial Mathematical Model for Derivative Instruments and Hedging Exchange Rate

An Intermediation-Based Model of Exchange Rates

An Intermediation-Based Model of Exchange Rates

On modeling the log-returns of Bitcoin and Ethereum prices against the USA Dollar

The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates.