Exchange Rate Model Research Articles

Volatility, intermediaries, and exchange rates

We propose and estimate a quantitative model of exchange rates in which participants in the foreign exchange market are intermediaries subject to value-at-risk (VaR) constraints. Higher volatility translates into tighter VaR constraints, and intermediaries require higher returns to hold foreign assets. Therefore, the foreign currency is expected to appreciate. The model quantitatively resolves the Backus–Smith puzzle, the forward premium puzzle, and the exchange rate volatility puzzle and explains deviations from the covered interest rate parity. Moreover, the model implies both contemporaneous and predictive relations between proxies of leverage constraint tightness and exchange rates. These implications are supported in the data.

How frequent a BEER? Assessing the impact of data frequency on real exchange rate misalignment estimation

AbstractThis article explores the robustness of Behavioural Equilibrium Exchange Rate models, employed to estimate real effective exchange rate misalignments, to the frequency of the underlying data. It compares misalignments stemming from an annual model, estimated since 1980, and a comparable quarterly model, estimated since 1999. The two sets of estimates are similar. Moreover, the in‐sample power of quarterly REER misalignments in explaining subsequent REER developments is higher than that of the annual estimates. This article therefore suggests that the “optimal” frequency of a BEER model depends on whether its resulting estimates are employed for research purposes or for policy‐making activities.

HDXmodeller: an online webserver for high-resolution HDX-MS with auto-validation

The extent to which proteins are protected from hydrogen deuterium exchange (HDX) provides valuable insight into their folding, dynamics and interactions. Characterised by mass spectrometry (MS), HDX benefits from negligible mass restrictions and exceptional throughput and sensitivity but at the expense of resolution. Exchange mechanisms which naturally transpire for individual residues cannot be accurately located or understood because amino acids are characterised in differently sized groups depending on the extent of proteolytic digestion. Here we report HDXmodeller, the world’s first online webserver for high-resolution HDX-MS. HDXmodeller accepts low-resolution HDX-MS input data and returns high-resolution exchange rates quantified for each residue. Crucially, HDXmodeller also returns a set of unique statistics that can correctly validate exchange rate models to an accuracy of 99%. Remarkably, these statistics are derived without any prior knowledge of the individual exchange rates and facilitate unparallel user confidence and the capacity to evaluate different data optimisation strategies.

New “News” for the news model of the spot exchange rate

The “News” model of the exchange rate, that received only weak support in the 1980s, is shown to be a verifiable model of the bilateral spot rate once the “news” is appropriately measured. Using market sentiment and policy uncertainty indices derived from big data for Japan, as “news” and survey data of agents’ expectations of the spot rate one month ahead, the “News” model of the exchange rate is shown not to be rejected for the bilateral JPY/USD rate from June 2009 to December 2017.

In Search of Exchange Rate Undershooting in Pakistan

Exchange rate behaviour does not follow very obvious and predicted pattern. Many attempts have been made to predict its behaviour as much as possible. This research re-examines the Dornbusch’s model of exchange rate overshooting caused by price rigidities. Dornbusch’s assumption of full employment in economy has been violated in this research which creates the possibility of exchange rate undershooting. In response to positive monetary shock, interest rate decreases and exchange rate undershoots its long run equilibrium. This research explains the dynamics of anti-intuitive exchange rate undershooting. Apart from theoretical formations of exchange rate undershooting, this research also analyses Pakistani data for exchange rate undershooting or overshooting in response to increase in money supply. Quarterly data of twenty three years for exchange rate, nominal interest rate, price, real output and money have been taken and vector autoregressive technique has been used. Evidence of exchange rate undershooting in response to positive money supply shock was found. It also gives an important insight into policy making by identifying some probable behaviour of exchange rate.

The long-run effects of geopolitical risk on foreign exchange markets: evidence from some ASEAN countries

PurposeThe purpose of this paper is to analyse the long-run relationship between geopolitical risk and exchange rates in four ASEAN countries.Design/methodology/approachWe augment theoretical nominal exchange rate models available in the literature with the geopolitical risk index developed by Caldara and Iacoviello (2019), and then estimate these models using the ARDL approach to Cointegration.FindingsOur analysis uncovers evidence of Cointegration in the exchange rate models when the MYR-USD, IDR-USD, THB-USD and PHP-USD exchange rates are used as dependent variable. Next, geopolitical risk is a significant long-run driver for these exchange rates. Third, in all countries higher geopolitical risk leads to a depreciation of domestic currency.Research limitations/implicationsThere are implications for entrepreneurs, central banks, portfolio managers and arbitrageurs who actively trade in financial markets. Financial market players can benefit from a better understanding of how geopolitical events affect the portfolio of financial assets across various countries, while entrepreneurs can work out hedging strategies.Originality/valueThis is a contribution to the study of interlinkages between political risk and foreign exchange markets. It is the first study to adopt the geopolitical risk index of Caldara and Iacoviello (2019) to the study the foreign exchange markets of ASEAN countries.

Nonlinearities and Asymmetric Adjustment to PPP in an Exchange Rate Model with Inflation Expectations

This paper estimates a model of the real exchange rate including standard fundamentals as well as two alternative measures of inflation expectations for five inflation targeting countries (UK, Canada, Australia, New Zealand, Sweden) over the period January 1993-July 2019. Both a benchmark linear ARDL model and a nonlinear ARDL (NARDL) specification are considered. The results suggest that the nonlinear framework is more appropriate to capture the behaviour of real exchange rates given the presence of asymmetries both in the long- and short-run. In particular, the speed of adjustment towards the PPP-implied long-run equilibrium is three times faster in a nonlinear framework, which provides much stronger evidence in support of PPP. Moreover, inflation expectations play an important role, with survey-based ones having a more sizable effect than market-based ones. Monetary authorities should aim to achieve a high degree of credibility to manage them and thus currency fluctuations effectively. The inflation targeting framework might be especially appropriate for this purpose.

Modelling exchange rate movements in South Africa: an ARDL application

This study examined the monetary, unbiased forward rate hypothesis (UFRH), and random walk models of exchange rate in South Africa using the auto regressive distributed lag (ARDL) approach. The estimated results show that there is a long-run relationship between variables in each model, suggesting the examined models are able to predict the exchange rate. In the long run, this paper found a sticky-price monetary model with correct signs but restrictions on money supply are violated. However, both the UFRH restrictions and random walk model were upheld. The models were then used to predict forecasts of the out-of-sample period as compared to the random walk. We found that UFRH outperformed other models. In this context, forward rate is predominantly an unbiased estimator of the future spot exchange rate, suggesting that the South African exchange rate market is efficient.

Modelling exchange rate movements in South Africa: an ARDL application

This study examined the monetary, unbiased forward rate hypothesis (UFRH), and random walk models of exchange rate in South Africa using the auto regressive distributed lag (ARDL) approach. The estimated results show that there is a long-run relationship between variables in each model, suggesting the examined models are able to predict the exchange rate. In the long run, this paper found a sticky-price monetary model with correct signs but restrictions on money supply are violated. However, both the UFRH restrictions and random walk model were upheld. The models were then used to predict forecasts of the out-of-sample period as compared to the random walk. We found that UFRH outperformed other models. In this context, forward rate is predominantly an unbiased estimator of the future spot exchange rate, suggesting that the South African exchange rate market is efficient.

Beyond Incomplete Spanning: Convenience Yields and Exchange Rate Disconnect

We introduce convenience yields on dollar bonds into an incomplete-market equilibrium model of exchange rates and interest rates. The convenience yield enters as a stochastic wedge in the Euler equation for exchange rate determination. The model identifies a novel safe-asset convenience yield channel by which quantitative easing impacts the dollar exchange rate. Our model addresses three exchange rate puzzles. (1) The model can rationalize the low pass-through of SDF shocks to exchange rates and hence low exchange rate volatility. (2) It helps address but does not fully resolve the exchange rate disconnect puzzle. (3) The model generates an unconditional log currency expected return on the dollar that is in line with the data.

Deep Reinforcement Learning for Algorithmic Trading

We employ reinforcement learning (RL) techniques to devise statistical arbitrage strategies in electronic markets. In particular, double deep Q network learning (DDQN) and a new variant of reinforced deep Markov models (RDMMs) are used to derive the optimal strategies for an agent who trades in a foreign exchange (FX) triplet. An FX triplet consists of three currency pairs where the exchange rate of one pair is redundant because, by no-arbitrage, it is determined by the exchange rates of the other two pairs. We use simulations of a co-integrated model of exchange rates to implement the strategies and show their financial performance.

Convenience Yield and Real Exchange Rate Dynamics: A Present-Value Interpretation

In this paper, we empirically investigate the relationship between the convenience yield of government bonds and the real exchange rates using monthly data spanning from 1999 to 2018. We extend the conventional models, based on present-value relationship between real exchange rate and economic fundamentals, while explicitly considering the role of the convenience yield. Empirical results suggest that our present-value models are able to capture the dynamic properties of the real exchange rate documented in literature, including high persistence, and excess volatility and co-movement compared with real interest rate differentials. We also find the sum of expected convenience yields significantly drives real exchange rate movements. Moreover, we found that foreign exchange swap market friction also plays a role in explaining real exchange rates. Finally, we identified a deviation with regard to the monetary policy rule, and found that monetary policy at the zero lower bound is also significant for yen--dollar real exchange rate modeling.

Real exchange rate effect on economic growth: comparison of fundamental equilibrium exchange rate and Balassa–Samuelson based Rodrik approach

ABSTRACT This study reproduces the work of Dani Rodrik on real exchange rate undervaluation and economic growth for 93 countries over the period 1990–2018. While the empirical literature on the dynamics between the real exchange rate1 1 It is a measure of the bilateral real exchange rate with respect to the US dollar. and economic growth is relatively comprehensive, little has been done to compare these dynamics within economies using the Balassa–Samuelson-based Rodrik approach (BS) and the fundamental equilibrium exchange rate model (FEER). This research, to the best of the authors’ knowledge, is one of the first to compare the fundamental equilibrium exchange rate model and Balassa–Samuelson-based Rodrik approach and use dynamic estimation on the Rodrik approach. The findings of the study support Rodrik’s conclusion that undervaluation has a significant impact on economic growth, although the results of FEER are more significant than those of BS. Furthermore, the first lag of undervaluation has a significant effect on economic growth.

A Panel Data Analysis of Uncovered Interest Parity and Time-Varying Risk Premium

There exist several exchange rate models that associate macroeconomic variables with the exchanges rates. In this article, we focus on uncovered interest parity (UIP) which relates the expected exchange rate changes to the intercountry interest rate differential. We apply various panel econometric methods to test UIP for a wide range of data covering numerous cross currency rates as well as the U.S. Dollar based exchange rates. The results for UIP are mainly unfavorable. We utilize an augmented version of UIP containing time-varying risk premium (proxy: sovereign credit default swap) for a similar analysis to observe whether it makes any improvement. Nevertheless, this version does not get much support too. Although it is common to presume that deviations from UIP are mostly due to a time-varying risk premium, our analysis indicates that this is not true.

Research on matching design method of waste heat reuse system of fuel cell vehicle considering system energy consumption and waste heat exchange rate

SummaryThe research of hydrogen fuel cell waste heat recovery is a new field of fuel cell thermal management, which helps to improve the overall fuel utilization. At present, the research on the waste heat utilization of fuel cell vehicles is less, and it is more based on the traditional vehicle waste heat management ideas without systematic summary of the factors affecting the system performance. This study takes the waste heat reuse system of fuel cell vehicle as the research object, and established the waste heat exchange rate model and energy consumption model of the waste heat reuse system. Through the study of relevant parameter variables, the effects of parameters on system energy consumption and waste heat exchange rate at low temperature were obtained. The matching design of waste heat reuse system of 60 kW fuel cell vehicle was carried out, and the simulation analysis and experimental verification were done. The results showed that when the ambient temperature is −3°C and −6°C, the waste heat exchange rate of the new matching scheme is 16.2% and 16.3% higher than that of the original scheme, respectively. Also, the total energy consumption of the system is reduced by 62.02% and 60.62%, respectively.

Modeling of Residential Indoor Concentrations of Ambient PM2.5, Ozone for the Catheterization Genetics (CATHGEN) Study

Air pollution epidemiological studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors as exposure surrogates, which can add bias or uncertainty in health effect estimates. The goal of this study was to improve exposure assessments of ambient PM2.5 and O3 for a 10-year epidemiological study with 2,271 participants with coronary artery disease in central North Carolina called the Catheterization Genetics (CATHGEN) study. We developed an exposure modeling approach to estimate three tiers of individual-level exposure metrics for ambient PM2.5 and O3. We used a hybrid outdoor air quality model (based on satellite- and ground-based air pollution measurements, chemical transport and land-use models) linked to a residential air exchange rate model (based on building characteristics, indoor-outdoor temperatures, wind speed) and mass-balance infiltration model to determine residential air exchange rates (AER, Tier 1), infiltration factors (Finf, Tier 2), and indoor concentrations (Cin, Tier 3). For each of the 2,271 participant homes, we applied the exposure model to determine daily house-specific PM2.5 and O3 exposure metrics (Tiers 1-3) for the 365 days before each participant’s cardiac catheterization date. The daily modeled exposure metrics for all 828,915 participant days showed considerable temporal and house-to-house variability of AER, Finf and Cout (Tiers 1-3). Our study demonstrates the ability to apply an outdoor air quality model linked to a residential infiltration model to determine individual-level ambient PM2.5 and O3 exposure metrics for a large, long-term epidemiological study, in support of improving risk estimation.

Foreign Currency Power Option Pricing Based on Esscher Transform

In this paper, we introduce a dynamic model for the spot foreign exchange rate which is driven by a standard Brownian motion and a stationary compound Poisson process under the domestic real measure. In order to price the derivatives on the foreign exchange rate, we need to find an equivalent probability measure under which the discounted process of the foreign exchange rate by the domestic free interest rate minus the foreign free interest rate is a martingale. The Esscher transform is an efficient technique to find an equivalent martingale measure. Applying the tool of the characteristic function, we derive some Esscher transform parameters with respect to the spot foreign exchange rate. At the same time, we get the corresponding Esscher martingale measure which is the domestic risk-neutral measure Q equivalent to the domestic real measure. Moreover, we reconsider the dynamic process of the spot foreign exchange rate under the measure Q. Furthermore, we hope that the exchange rate fluctuates within a certain range, since too large fluctuation will bring a series of serious problems. In fact, the foreign exchange rate is usually stable in a certain range. Thus, studying the pricing of foreign exchange rate derivatives, we often assume that the foreign exchange rate fluctuates within a certain range. Based on the above work, we combine European option with the power option to propose a new type of the foreign exchange power option whose payoff function is controlled by multiplying an indicative function on the interval of the foreign exchange rate and further obtain the pricing formulas under this model. At last, we utilize the actual market data of the foreign exchange rate of USD/CNY to obtain the value of the foreign exchange power option and investigate the implied volatility.

A Kalman filter-based hybridization model of statistical and intelligent approaches for exchange rate forecasting

Purpose The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in order to yield a more general and more accurate hybrid model for exchange rates forecasting. For this purpose, the Kalman filter technique is used in the proposed model to preprocess and detect the trend of raw data. It is basically done to reduce the existing noise in the underlying data and better modeling, respectively. Design/methodology/approach In this paper, ARIMA models are applied to construct a new hybrid model to overcome the above-mentioned limitations of ANNs and to yield a more general and more accurate model than traditional hybrid ARIMA and ANNs models. In our proposed model, a time series is considered as a function of a linear and nonlinear component, so, in the first phase, an ARIMA model is first used to identify and magnify the existing linear structures in data. In the second phase, a multilayer perceptron is used as a nonlinear neural network to model the preprocessed data, in which the existing linear structures are identified and magnified by ARIMA and to predict the future value of time series. Findings In this paper, a new Kalman filter based hybrid artificial neural network and ARIMA model are proposed as an alternate forecasting technique to the traditional hybrid ARIMA/ANNs models. In the proposed model, similar to the traditional hybrid ARIMA/ANNs models, the unique strengths of ARIMA and ANN in linear and nonlinear modeling are jointly used, aiming to capture different forms of relationship in the data; especially, in complex problems that have both linear and nonlinear correlation structures. However, there are no aforementioned assumptions in the modeling process of the proposed model. Therefore, in the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be generally guaranteed that the performance of the proposed model will not be worse than either of their components used separately. In addition, empirical results in both weekly and daily exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Originality/value In the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components used separately. In addition, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternate model for forecasting in exchange ratemarkets, especially when higher forecasting accuracy is needed.

Determinants of Thai Baht Exchange Rate and Asian Currencies Exchange Rate

The study hypothesises that the exchange rate is a random walk series. Besides, the study incorporates main macroeconomic factors as a structural exchange rate determination. The Vector Error Correction Model (VECM) is applied in the model estimation for Thai baht. Moreover, the panel data model of the Asian exchange rate is estimated and analysed. The exchange rate of Thai baht is found to be a random walk process. The long-run equilibrium of the estimated cointegrating relation indicates all coefficients of the determining factors are statistically significant. An increase in the real interest rate and the foreign reserve has significant appreciation effects on the Thai baht. An increase in the income per capita has a significant depreciation effect on the Thai baht. External debt causes a depreciation in the Thai baht. The most substantial impact on the value of Thai baht is the income per capita. It follows by the foreign reserve, the real interest rate, and the external debt, respectively. During 2017 and 2018, the estimated exchange rate is appreciated by 4.21 per cent that is close to the actual appreciated value. The estimated Asian model is found consistent with the model of the Thai baht. The highest impact on the local Asian currencies is the income per capita. It follows by the foreign reserve and the real interest rate, respectively, with both quite close by their sizes. However, the foreign reserve has a more appreciated influence than that of the real interest rate for Thai baht.

Testing monetary exchange rate models with the Westerlund panel cointegration test

Time series testing of long-run monetary models of exchange rate determination in most cases fails to support the conjectures of the theory. The empirical literature increasingly uses the panel technique when testing monetary exchange rate models because the power of the panel unit root and panel cointegration tests seems higher than the pure time series tests. In this paper we examine the validity of the monetary exchange rate models over the period 1996Q1-2011Q4 for US dollar exchange rates of 15 OECD countries using Westerlund’s 2007 panel cointegration tests. We found moderate empirical support for monetary exchange rate models.