Euro-dollar Exchange Rate Research Articles

How does fear spread across asset classes? Evidence from quantile connectedness

PurposeThis study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.Design/methodology/approachThe empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying.FindingsExtreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices.Originality/valueThis is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

Early warning of exchange rate risk based on structural shocks in international oil prices using the LSTM neural network model

The Russia–Ukraine conflict has caused an external shock to the oil market as the exchange rate risk increased with significant depreciation of major currencies relative to the U.S. dollar (USD). In this paper, structural shocks to oil prices calculated by the Ready (2018) method are introduced in addition to traditional exchange rate predictor variables such as oil prices. Utilising a long short-term memory neural network model, we verify the early warning effect of structural shocks to international oil prices on exchange rate risk in the context of the Russia–Ukraine geo-conflict. We have several empirical findings. First, the variance decomposition method indicates that oil price shocks affect the euro-dollar exchange rate risk. After the conflict between Russia and Ukraine, the euro exchange rate risk continued to rise significantly, mainly affected by supply shocks. Second, compared with traditional prediction models, the LSTM neural network model has a higher prediction accuracy, which is conducive to improving the early warning performance of exchange rate risks and maintaining the stability of the foreign exchange market. Third, the introduction of oil price shock can fully extract and use the information from oil price changes, which significantly improves the learning performance of the oil price early warning model of exchange rate risk. Fourth, the introduction of oil price risk shocks in the whole sample, the introduction of oil price demand shocks before the Russia-Ukraine conflict, and the introduction of oil price supply shocks, especially negative supply shocks, after the conflict between Russia and Ukraine, can warn exchange rate risks more effectively. This study proposes a new method for early warning of exchange rate risk that can help cross-border enterprises and government policymakers to respond to unexpected events and effectively mitigate associated risks.

A credit-based theory of the currency risk premium

This paper uncovers a novel component for exchange rate predictability based on the price difference between sovereign credit default swaps denominated in different currencies. This new forecasting variable – the credit-implied risk premium – captures the expected currency depreciation conditional on a severe but rare credit event. Using data for 16 Eurozone countries, we find that the credit-implied risk premium positively forecasts the dollar-euro exchange rate return at various horizons. Moreover, a currency strategy that exploits the informative content of our predictor generates substantial out-of-sample economic value against the naïve random walk benchmark.

A Novel Linear-Model-Based Methodology for Predicting the Directional Movement of the Euro-Dollar Exchange Rate

A Novel Linear-Model-Based Methodology for Predicting the Directional Movement of the Euro-Dollar Exchange Rate

Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters

This paper nowcasts the Euro-Dollar short-run exchange rate by using a MF-TVP-FAVAR model. The FAVAR framework improves forecasting accuracy by expanding the information set of the previously widely used VAR models. We adopt a flexible modelling approach that adjusts for structural breaks in the data and money demand instability; it also prevents information loss due to variables being quoted at mixed frequencies. We estimate our model by using a dual conditionality linear Kalman filtering/smoothing. Our results indicate that the specified model outperforms the Random Walk and other structural models at all forecasting horizons.

Forecasting of WTI crude oil using combined ANN-Whale optimization algorithm

ABSTRACT The current study predicts West Texas Intermediate (WTI) petroleum prices using an artificial neural network (ANN) with a whale optimization algorithm (WOA). In implementing the model, five parameters, including gold price, coal price, natural gas price, Dollar-Euro exchange rate, and Dollar-Yuan exchange rate, have been used as input to the combined model. The intelligent and basic ANN algorithm results compared to finding the ANN-WOA algorithm capacity in predicting the future price of WTI oil. ANN-WOA model improved the WTI price predicting accuracy up to 22% compared to the ANN. The ANN-WOA method with a value of R2 = 0.93 compared to the ANN method with a value of R2 = 0.75 was able to reduce the model error well. According to the significant impact that the input parameters of the combination model had on the WTI oil price prediction, therefore, in studies that predict price or other variables, highly correlated variables can significantly increase the accuracy of the forecast.

Nonlinear Dependence and Spillovers between Currency Markets and Global Economic Variables

The widespread integration and growing systemic dependence among currency, stock, and commodity markets render these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we aim to explore the spillovers and nonlinear dependencies between the seven major foreign exchange rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by employing a directional spillover network approach. We also use a multi-scale decomposition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. The major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the Euro–Dollar exchange rate as the largest contributor of connectedness to the other series.

How the Contagion is Transmitted to the Macedonian Stock Market? an Analysis of Co-Exceedances

Abstract The aim of the paper is to analyze the transmission of shocks from selected developed and Southeastern European stock markets to the stock market of North Macedonia. Using the Bae, Karolyi, and Stulz (2003) co-exceedance methodology, we find that the probability of contagion from the stock markets of United States, Serbia and Bosnia and Herzegovina to the Macedonian stock market increased during the Global Financial Crisis. Regarding the asset classes, we show that contagion is positively associated with the volatility of Eurostoxx50 index, while negatively with the return of the euro dollar exchange rate and the yield of the 10-year US Treasury Note. The results have important implications for portfolio diversification and the asset allocation decisions of investors.

Monetary Policy Shocks in Open Economies and the Inflation Unemployment Trade-Off: The Case of the Euro Area

In this paper, we show that in order to obtain a sound identification of Euro Area monetary policy shocks, one needs to deal with the interaction of the European Central Bank and the US Federal Reserve. In other words, a proper identification of monetary policy shocks for an open economy like the Euro Area requires consideration of the US policy rate. Indeed, when we exclude the Federal Funds Rate from an estimated VAR model including a set of Euro Area variables, i.e., Eonia, inflation and unemployment, we detect a wrong sign in the response of inflation to contractionary monetary policy shocks. Moreover, even adding the world price of oil does not help to overcome the problem. Instead, for a sample covering the period 1999–2019, when the Federal Funds Rate and the Euro–Dollar exchange rate are added to the VAR model inflation shows statistically non-significant effects for two years and thereafter decreases. Under this specification of the model, a clear and significant unemployment inflation trade-off emerges. These conclusions are confirmed by using industrial production instead of the unemployment rate in the VAR model.

The stock implied volatility and the implied dividend volatility

This study compares the information on the implied volatility surface of a stock-index with the corresponding information on the implied volatility surface of the index dividend futures. We outline an optimisation technique for comparing implied volatility estimates based on the Black-Scholes model, Black model and a model-free approach, for stock-index versus dividend-index futures. The implied volatility term-structure of stock-index consistently exceeds that of the dividend index futures thereby confirming the equity volatility puzzle under novel financial data and instruments. However, the magnitude of excess implied volatility reduces as the time-to-maturity increases, suggesting that discrepancies between the two are influenced by investment horizon, and the type of option, call or put. We also show the existence of a profitable trading rule that outperforms a benchmark buy-and-hold strategy. The GDP, dollar euro exchange rate and inflation are strong determinants of the implied volatility difference.

THE RELATIONSHIP BETWEEN CRUDE OIL PRICES, EUR/USD EXCHANGE RATE AND GOLD PRICES

This study aims to analyze and measure the nature of the relationship between crude oil price, EUR/USD exchange rate, and Gold price by using monthly data from January 1999 to October 2019. The result of data analysis using Auto-Regressive Distributed Lag (ARDL) shows that there exists a co-integration relationship between the price of crude oil in U.S. dollars per barrel (C.O.), and the interpreting variables in this study, represented in the Euro Dollar Exchange Rate (E.R.) and the Gold Price in U.S. dollars per ounce (G.P.). The relationship that happened only in a short-term one. Granger causality test result shows one-way causal relationship from the Euro/ Dollar towards oil prices; that is, the variation in the exchange rate causes changes in oil prices. Also a unidirectional causal relationship has been found between the gold price and crude oil price. The relationship between the study variables is positive.Keywords: Crude Oil Price, EUR/USD exchange rate, Gold Price, ARDL,JEL Classifications: C58, Q24, Q43, F31DOI: https://doi.org/10.32479/ijeep.9523

Real Exchange Rates, Oil Price Spillover Effects, and Tripolarity

ABSTRACT The real exchange rates of the currencies of non-Eurozone countries are determined to a large extent by the behavior of the euro, because intra-EU trade accounts for a large proportion of the countries’ balances of payments. Given that their currencies and the euro are also influenced by the EUR/USD exchange rate and oil price changes, the tripolar model appears to provide an appropriate analytical framework. The empirical results of this study support the hypothesis that in the long run the euro-dollar and Polish zloty-euro exchange rates are driven by crude oil prices, and that the parities of the real risk-free interest rates determine the euro-dollar exchange rate in the short-run and the zloty-euro exchange rate in the long-run. They also show that in recent years the euro-dollar exchange rate was long influenced by the ECB’s asset purchase program. The impulse response analysis clearly indicates that in addition to shocks from credit default risk premiums and interest rates innovations oil price shocks too are a significant and pervasive source of the nonstationary dynamics of the exchange rates.

Does a big bazooka matter? Quantitative easing policies and exchange rates

We estimate the effects of quantitative easing (QE) measures by the ECB and the Federal Reserve on the US dollar-euro exchange rate at frequencies and horizons relevant for policymakers. To do so, we derive a theoretically-consistent local projection regression equation from the standard asset pricing formulation of exchange rate determination. We then proxy unobserved QE shocks by future changes in the relative size of central banks’ balance sheets, which we instrument with QE announcements in two-stage least squares regressions in order to account for their endogeneity. We find that QE measures have large and persistent effects on the exchange rate: The typical ECB or Federal Reserve expansionary QE announcement in our sample resulted in an increase in the relative balance sheet of about 20% and, in turn, in a persistent exchange rate depreciation of around 7%. Regarding transmission channels, we find that a relative QE shock that expands the ECB’s balance sheet relative to that of the Federal Reserve depreciates the euro against the US dollar by reducing euro-dollar short-term money market rate differentials, by widening the cross-currency basis and by eliciting adjustments in “residual” deviations from interest parity. Changes in the expectations about the future monetary policy stance, reflecting the “signalling” channel of QE, also contribute to the exchange rate response to QE shocks.

Parsimonious Heterogeneous ARCH Models for High Frequency Modeling

In this work we study a variant of the GARCH model when we consider the arrival of heterogeneous information in high-frequency data. This model is known as HARCH(n). We modify the HARCH(n) model when taking into consideration some market components that we consider important to the modeling process. This model, called parsimonious HARCH(m,p), takes into account the heterogeneous information present in the financial market and the long memory of volatility. Some theoretical properties of this model are studied. We used maximum likelihood and Griddy-Gibbs sampling to estimate the parameters of the proposed model and apply it to model the Euro-Dollar exchange rate series.

Measuring Monetary Policy with Residual Sign Restrictions at Known Shock Dates

We propose a novel identification strategy to measure monetary policy in a structural VAR. It is based exclusively on known past policy shocks, which are uncovered from high-frequency data, and does not rely on any theoretical a-priori restrictions. Our empirical analysis for the euro area reveals that interest rate decisions of the ECB surprised financial markets at least fifteen times since 1999. This information is used to restrict the sign and magnitude of the structural residuals of the policy rule equation at these shock dates accordingly. In spite of its utmost agnostic nature, this approach achieves strong identification, suggesting that unexpected ECB decisions have an immediate impact on the short-term money market rate, the narrow money stock, commodity prices, consumer prices and the Euro-Dollar exchange rate, and that real output responds gradually. Our close to assumption-free approach obtains as an outcome what traditional sign restrictions on impulse responses impose as an assumption.

Analyses on Volatility Clustering in Financial Time-Series Using Clustering Indices, Asymmetry, and Visibility Graph

The volatility clustering has critical implications in financial risk management. This paper aims to analyze the existence and cause of volatility clustering in financial time-series using different measures simultaneously. Specifically, we utilize the clustering indices, asymmetry measures, and the power of the scale freeness in the visibility graph. For the experiment, we utilize four representing financial time-series, including the S&P500, one-year US Treasury Constant Maturity rate, Euro-Dollar exchange rate, and Crude oil for the stock, bond, exchange, and commodity markets, respectively. The duration of the experiment is from 2009 to 2018, which is divided into two sub-periods: crisis and post-crisis periods. At first, we identify the positive and slowly decaying non-linear autocorrelation in all markets, which indicates the power-law decay. Also, the autocorrelation of the simulated time-series suggests that the order of return-series with respect to its magnitude contributes more to the volatility clustering than the heavy-tailed distributions. Secondly, we detect that the scale of the return contributes more to volatility clustering than the sign of the return. Lastly, we observe that the clustering and asymmetry measures are more robust measures to the return distribution changes than the PSVG to analyze the volatility clustering.

A Suggestion for a Dynamic Multi Factor Model (DMFM)

We provide a new way of deriving a number of dynamic unobserved factors from a set of variables. We show how standard principal components may be expressed in state space form and estimated using the Kalman filter. To illustrate our procedure we perform two exercises. First, we use it to estimate a measure of the current-account imbalances among northern and southern euro-area countries that developed during the period leading up to the outbreak of the euro-area crisis, before looking at adjustment in the post-crisis period. Second, we show how these dynamic factors can improve forecasting of the euro-dollar exchange rate.

A Credit-Based Theory of the Currency Risk Premium

This paper extends Kremens and Martin (2019) and uncovers a novel component for exchange rate predictability based on the price difference between sovereign credit default swaps denominated in different currencies. This new forecasting variable - the credit-implied risk premium - captures the expected currency depreciation conditional on a severe but rare credit event. Using data for 16 Eurozone countries, we find that the credit-implied risk premium positively forecasts the dollar-euro exchange rate return at various horizons. Moreover, a currency strategy that exploits the informative content of our predictor generates substantial out-of-sample economic value against the naive random walk benchmark.

Time-varying effects of macroeconomic news on euro-dollar returns

We investigate the intraday reaction of euro-dollar exchange rate returns to the US and European macroeconomic news during a period that spans the global financial crisis and the Euro-zone debt crisis. First, we assess whether announcements’ impact is stable over time. We then use time-varying parameter path analysis to investigate whether the currency return response to macroeconomic news is sensitive to changes in market risk and interest rates. We find that news impact coefficients vary significantly over time. Our results also show that higher market risk measured by VIX dampens the effect of US news on euro-dollar returns.

Exchange rate disconnect and private information: What can we learn from Euro-Dollar tweets?

We use Twitter opinions about the Euro-Dollar exchange rate to estimate the private information model of Bacchetta and van Wincoop (2006) and investigate the disconnect between the exchange rate and macro fundamentals over both short and long horizons. We simulate the model with the estimated parameters and replicate the methodology of three studies that document the disconnect empirically. The model is consistent with the findings of the empirical literature, though for a different reason over short than long horizons. Over short horizons private information generates a true disconnect between exchange rates and macro fundamentals that accounts for empirical findings. Over long horizons the theory shows that exchange rate changes are mostly driven by observed fundamentals, but empirical limitations in identifying this long-run relationship often lead to an appearance of disconnect in the data.