Exchange Rates Of Major Currencies Research Articles

MOLDOVA CUMHURİYETİ ULUSAL BANKASI`NIN ULUSAL EKONOMİNİN GELİŞMESİ AMACINA YÖNELİK PARA POLİTİKASINI İLERLETME YÖNERGELERİ

The monetary policy of the central bank influences the amount of currency in circulation, the level of interest rates, foreign exchange rates and other economic-monetary indicators in order to achieve the general objectives of the economic policy, such as ensuring price stability by maintaining the inflation at an optimal level, the growth and the economic development of the country by stimulating economic activity, employment, etc.
 The uncertain nature of the evolution of the world economy and the high degree of openness of the economy of the Republic of Moldova denotes a multitude of major risks that endanger the assurance and maintenance of the price stability. In the medium term, these remain to be large fluctuations in prices for food and energy resources, fluctuations in exchange rates of major currencies and of the capital flows. To cope with these situations, an important challenge for the monetary policy promoted by the NBM is to maintain inflationary expectations well anchored within the inflation target range.

Long-Term Relationship Between Prices and Exchange Rates

Purpose: This paper aimed to prove the existence of a long-term relationship between prices and exchange rates like that derived from the theory of purchasing power parity. Particular attention has been paid to the possible reasons behind the deviations of exchange rates from the values implied by the parity and to the theoretical justification of this phenomenon. Design/Methodology/Approach: The research was carried out by detailed analysing of the exchange rates of major currencies of the world, the U.S. dollar, the British pound and the Japanese yen related to individual quarters in the 40 years period. The formal empirical verification was made with the use of econometric tools that allow a long-term study of the relationship between stochastic processes. In analyses there were used the OLS estimator and the appropriate regression of USD/JPY/GBP relative to the logarithms of price levels in these countries. The phenomena of the cointegration of rates and prices and the stationarity of real exchange rates were also examined. Findings: Formal verification did not allow the confirmation of the exact relationship of long-term forecast by purchasing power parity. For all the examined exchange rates, the existence of co-integrating relationship was not shown, the relationship which would be fulfilled in spite of short-term deviations from parity. Despite the fact that the study employed data concerning a forty-year time period, the cycles of deviations from the balance path take too long to be able to demonstrate the stationarity of residuals in the cointegration analysis. This hypothetically makes purchasing power parity a very long-term relationship, which in the short-term and medium-term is subject to relatively strong disturbance. Practical implications: This paper analyses and empirically verifies the widely known long-term relationship of purchasing power parity. Moreover, it comes up with the proper economic justification of the results. Originality/Value: The purchasing power parity theory tends to be used by institutions actively creating macroeconomic policies. The research findings may contribute for better understanding of monetary policies in environment of inflation.

Dynamic link between oil prices and exchange rates: A non-linear approach

We explore the non-linear relationship between crude oil prices and exchange rates of major currencies from quantitative and structural perspectives, by utilizing bivariate normal mixture model. The correlation coefficients between oil prices and exchange rates demonstrate that their dependences typically start from 2004 then dynamically change over time. Then we investigate whether business cycle and oil price shocks as two possible exogenous factors affect the dependence structure of oil-FX linkage. We find significant structural heterogeneity during economic expansion while little evidence of heterogeneity in recession. This finding provides alternative interpretations for the enhanced dependence between oil prices and exchange rates and generates implications of financialization in commodity market. In terms of oil price shocks (Kilian, 2009), the normal mixture model captures significant heterogeneity, implying that unstable oil-FX dependence structure is frequently associated with oil aggregate demand shocks and oil-specific demand shocks. With an emphasis on the dynamic weights of two underlying states, we interestingly find that structural heterogeneities coincide with a variety of geopolitical and economic events. The application of normal mixture not only provide us knowledge of non-linear relationship between oil prices and exchange rates but also guidance for investors in risk management and portfolio diversification complementary to the traditional portfolio theory based on normal distribution.

Exchange rate effects of US government shutdowns: Evidence from both developed and emerging markets

We examine the exchange rate effects of US government shutdowns using historical exchange rate data covering 19 episodes of government shutdowns. We find that major currency exchange rates generally tend to appreciate vis-à-vis the US dollar, and foreign exchange volatility tends to increase in response to shutdowns. We show that the effect of shutdowns is felt most one day after a shutdown and the effect dies out for most currencies within five days of a shutdown. These results pass a range of robustness tests which control for day-of-the-week effects, model specifications, and the Global Financial Crisis.

A survey on exchange rate prediction using neural network based methods

Forecasting exchange rate has always been in demand as it is very important for the international traders to predict how their money will perform against other currencies. So different methods have been applied by the researchers to accurately predict the exchange rates so that it can assist in taking decision while trading. From all the models the Artificial Neural Network (ANN) has given consistent performance in prediction by overcoming the limitations of other models and has outperformed all the models in terms of efficiency. The evolution of ANN is remarkable. In this paper, we have given the performance of different network models used by researchers to predict the exchange rates of major currencies in the future.

Time series forecasting using functional partial least square regression with stochastic volatility, GARCH, and exponential smoothing

AbstractWe propose a method for improving the predictive ability of standard forecasting models used in financial economics. Our approach is based on the functional partial least squares (FPLS) model, which is capable of avoiding multicollinearity in regression by efficiently extracting information from the high‐dimensional market data. By using its well‐known ability, we can incorporate auxiliary variables that improve the predictive accuracy. We provide an empirical application of our proposed methodology in terms of its ability to predict the conditional average log return and the volatility of crude oil prices via exponential smoothing, Bayesian stochastic volatility, and GARCH (generalized autoregressive conditional heteroskedasticity) models, respectively. In particular, what we call functional data analysis (FDA) traces in this article are obtained via the FPLS regression from both the crude oil returns and auxiliary variables of the exchange rates of major currencies. For forecast performance evaluation, we compare out‐of‐sample forecasting accuracy of the standard models with FDA traces to the accuracy of the same forecasting models with the observed crude oil returns, principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) models. We find evidence that the standard models with FDA traces significantly outperform our competing models. Finally, they are also compared with the test for superior predictive ability and the reality check for data snooping. Our empirical results show that our new methodology significantly improves predictive ability of standard models in forecasting the latent average log return and the volatility of financial time series.

Identifying Price Jumps from Daily Data with Bayesian vs. Non-Parametric Methods

Non-parametric approach to financial time series jump estimation, using the L-Estimator, is compared with the parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle Filters to estimate the out-sample evolution of its latent state variables, such as the jump occurrences. The comparison is performed on simulated time series with different kinds of dynamics, including Poisson jumps, self-exciting Hawkes jumps with long-term clustering, as well as co-jumps. In addition to that, a comparison is performed on the real world daily time series of 4 major currency exchange rates. The results from the simulation study show that for the purposes of in-sample estimation does the MCMC based parametric approach significantly outperform the L-Estimator. In the case of the out-sample estimates, based on a combination of MCMC an Particle Filters, used to sequentially estimate the jump occurrences immediately at the times at which the jumps occur, does the parametric approach achieve a similar accuracy as the non-parametric one in the case of the simulations with Poisson jumps that are relatively large, and it outperforms the non-parametric approach in the case of Hawkes jumps when the jumps are large. On the other hand, the L-Estimator provides better results than the parametric approach in all of the cases when the simulated jumps are small (1% or less), regardless of the jump process dynamics. The application of the methods to foreign exchange rate time series further shows that the estimates of the parametric method may be biased in the case when large outlier jumps occur in the time series as well as when the stochastic volatility grows too high (as happened during the crisis). In both of these cases, the non-parametric L-Estimator based approach seems to provide more robust jump estimates, less influenced by the mentioned issues.

Modelling Jump Clustering in the Four Major Foreign Exchange Rates Using High-Frequency Returns and Cross-exciting Jump Processes

Abstract The study analyses the self-exciting (clustering) and cross-exciting (contagion) effects in the 13 year long time series of 4 major currency exchange rates, namely: EUR/USD, GBP/USD, USD/CHF and USD/JPY. The analysis is performed by applying the univariate and multivariate Hawkes processes to the time series of jumps identified non-parametrically using power-variation estimators calculated from high-frequency returns (15 minute frequency is used). The study finds strong evidence of a statistically significant self-exciting behavior in all of the analyzed time series of “large jumps”. For the series containing even the “small jumps” the self-exciting tendencies remain significant only for the USD/CHF and USD/JPY rates. The study further finds evidence of a limited cross-exciting behavior, with significant relationships between jumps in USD/JPY and the future jump intensity in USD/CHF, as well as between jumps in USD/CHF and the future jump intensity of EUR/USD.

Walutowe kredyty mieszkaniowe a kursy walutowe – ocena ryzyka

The aim of this paper is an evaluation of foreign exchange housing loans granted in Poland. In the paper, there was conducted research of portfolio of housing loans, its structure and changes, especially with consideration to the currency of debts. Also, a foreign currency loans profitability accordingly to the time of obtaining the loan by the debtor was analyzed. The author statistically analyzed volatility of foreign exchange rates of major currencies traded in Poland. The results were compared versus swiss franc and euro. The research indicated very high level of swiss franc volatility and risk as well as confirmed high level of risk of financing in swiss currency over euro.

A wavelet transform analysis of the relationship between unexpected macroeconomic news and foreign exchange rates

This study applies the wavelet transform technique to analyse the relationship between exchange rates of major currencies and the unexpected macroeconomic news. Hourly closing exchange rates of major currencies from 2000 to 2009 are utilized for wavelet analysis and those of 2010 are used for ex ante test. It is found that JPY and CHF are the most affected exchange rates, which are followed by EUR and GBP. The least affected exchange rates are AUD and CAD. In general, the most influential news is NFP, which is followed by ISM_M. These two factors have an impact on at least four currency exchange rates in the short, intermediate and long terms. UR is a short-term factor and the effect of NFP lasts for all the three time horizons considered. Ex ante test is carried out and the accuracy rates for EUR, AUD, GBP, CAD, CHF and JPY are 0.75, 0.5, 0.67, 0.83, 0.83 and 0.58, respectively.

How does indian gold price react to the changes in real exchange rates?

This study investigates the relationship between the Indian gold price and the real exchangerates of major international currency and how does Indian gold price reacts to the exchange rates of thesecurrencies. The data set consists of monthly gold prices from Indian market and the real exchange rates ofmajor currencies like USD, Euro, Yen and INR for the period from 1994:01 to 2011:12. The relationship andreaction is tested through the Johansen cointegration test, Granger causality test and VAR models like Impulseresponse function and Variance Decomposition. It is found that the Indian gold prices have long runrelationship with the real exchange rates of major currencies and it is also found that the Indian gold prices arecaused by the real exchange rate of Yen but the vice versa does not exist. The Indian gold prices reactpositively to the shocks from Yen and negatively to the INR.

Forecasting Exchange Rates of Major Currencies with Long Maturity Forward Rates

This paper presents unprecedented exchange rate forecasting results based upon a new model which approximates the gap between the fundamental equilibrium exchange rate and the actual exchange rate with the long-maturity forward exchange rate. The theoretical derivation of our forecasting equation is consistent with the monetary model of exchange rates. Our model outperforms the random walk in out-of-sample forecasting of twelve major currency pairs in both short and long horizons forecasts for the 1990-2020 period. The results are robust for all sub-periods with the exception of years around the collapse of Lehman Brothers in September 2008. Our results are robust to alternative model specifications, single equation and panel estimation, recursive and rolling estimation, and alternate data construction methods. The model performs better when the long-maturity forward exchange rate is assumed to be stationary as opposed to assuming non-stationarity. The improvement in forecast accuracy of our model is economically and statistically significant for almost all exchange rate series. The model is simple, linear, easy to replicate, and the data we use are available in real time and not subject to revisions.

Financial forecasting through unsupervised clustering and neural networks

In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and non-stationarity, a common approach is to combine a method for the partitioning of the input space into a number of subspaces with a local approximation scheme for each subspace. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Artificial neural networks, on the other hand, are powerful computational models that have proved their capabilities on numerous hard real-world problems. The time series that we consider are all daily spot foreign exchange rates of major currencies. The experimental results reported suggest that predictability varies across different regions of the input space, irrespective of clustering algorithm. In all cases, there are regions that are associated with a particularly high forecasting performance. Evaluating the performance of the proposed methodology with respect to its profit generating capability indicates that it compares favorably with that of two other established approaches. Moving from the task of one-step-ahead to multiple-step-ahead prediction, performance deteriorates rapidly.

Is there an optimal duration for observation of play?

The study analyses the self-exciting (clustering) and cross-exciting (contagion) effects in the 13 year long time series of 4 major currency exchange rates, namely: EUR/USD, GBP/USD, USD/CHF and USD/JPY. The analysis is performed by applying the univariate and multivariate Hawkes processes to the time series of jumps identified non-parametrically using power-variation estimators calculated from high-frequency returns (15 minute frequency is used). The study finds strong evidence of a statistically significant self-exciting behavior in all of the analyzed time series of “large jumps”. For the series containing even the “small jumps” the self-exciting tendencies remain significant only for the USD/CHF and USD/JPY rates. The study further finds evidence of a limited cross-exciting behavior, with significant relationships between jumps in USD/JPY and the future jump intensity in USD/CHF, as well as between jumps in USD/CHF and the future jump intensity of EUR/USD.

On the Statistical Properties of Floating Exchange Rates: A Reassessment of Recent Experience and Literature

The paper reviews the statistical behavior of major currency exchange rates during 1975-86. A close inspection indicates small deviations of recent exchange rate behavior from random walks and some systematic movements in monthly data, possibly corresponding to the relatively infrequent arrivals of information concerning major macroeconomic variables. The distributional characteristics of exchange rate changes differ between daily and monthly data and thus imply the possible presence of heterogeneity in underlying factors. These and other observations suggest care in the use of daily data in empirical work and the usefulness of explicit modeling of heterogeneity among market participants and in information structure.