Major Foreign Exchange Rates Research Articles

Do stochastic risks flow between industrial and precious metals, Islamic stocks, green bonds, green stocks, clean investments, major foreign exchange rates, and Bitcoin?

This study examines the interactions between Bitcoin (BTC) and other major financial markets (forex, equity and bond, and industrial and precious metals). This interaction is viewed from the spectrum of spillover analysis, hedging, and portfolio analyses. Due to the high volatility and volatility clustering nature of most financial series, particularly BTC, we rely on a Multivariate Factor Stochastic Volatility (mvFSV) Model of Kastner et al. (2017). In order to capture the influence of extreme market conditions, we use a quantile-based mvFSV Model. Among other things, results confirm the importance of quantile analysis as results are sensitive to market conditions. For instance, currencies and industrial and precious metals are net transmitters of shocks at lower and middle quantiles, while these assets change their status to the net receiver of shocks at the upper quantile. Results of the total connectedness show that there is a high degree of interconnectedness among the assets, as the total connectedness index ranges between 86% and 100%. Besides, Islamic equities perform such as the most currencies and conventional assets in terms of the transmission of shocks and diversification benefits. The hedging potentials of Bitcoin over other assets were also examined, and results confirm that the hedging effectiveness is commodity-specific. Policy implications of these results are discussed.

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.

When do low-frequency measures really measure transaction costs?

We compare popular measures of transaction costs based on daily data with their high-frequency data-based counterparts. We find that for U.S. equities and major foreign exchange rates, (i) the measures based on daily data are highly upward biased and imprecise; (ii) the bias is a function of volatility; and (iii) it is primarily volatility that drives the dynamics of these liquidity proxies both in the cross section as well as over time. We corroborate our results in carefully designed simulations and show that such distortions arise when the true transaction costs are small relative to volatility. Many financial assets exhibit this property, not only in the last two decades, but also in the previous century. We document that using low-frequency measures as liquidity proxies in standard asset pricing tests may produce sizable biases and spurious inferences about the pricing of aggregate volatility or liquidity risk.

Estimation of SVJD Models with Bayesian Methods and Power-Variation Estimators

Methodology is proposed of how to utilize high-frequency power-variation estimators in the Bayesian estimation of Stochastic-Volatility Jump-Diffusion (SVJD) models. Realized variance is used as an additional source of information for the estimation of stochastic variances, while the Z-Estimator is used as an additional source of information for the estimation of asset price jumps. The models are estimated by a combination of a MCMC algorithm and a SIR Particle Filter. The performance of the models is evaluated on simulated times series as well as real world financial time series of the 4 major foreign exchange rates.

Generalized Additive Models for Pair-Copula Constructions

ABSTRACTPair-copula constructions are flexible dependence models that use bivariate copulas as building blocks. In this article, we extend them with generalized additive models to allow covariates effects. Borrowing ideas from a traditionally univariate context, we let each pair-copula parameter depend directly on the covariates in a parametric, semiparametric, or nonparametric way. We propose a sequential estimation method that we study by simulation, and apply it to investigate the time-varying dependence structure between the intraday returns on four major foreign exchange rates. An R package, scripts reproducing the results in this article, and additional simulation results are provided as supplementary material.

Generalized Additive Models for Pair-Copula Constructions

Pair-copula constructions are flexible models for the dependence in a random vector that have attracted a lot of interest in recent years. In this paper, we use generalized additive models to extend pair-copula constructions to allow for effects of covariates on the dependence parameters. We let each pair-copula parameter depend directly on the covariates in a parametric, semi-parametric or non-parametric way. We propose a sequential estimation method that we study by simulation, and apply our method to investigate the time-varying dependence structure between the intraday returns on four major foreign exchange rates.

Value-at-risk estimation with the Carr–Geman–Madan–Yor process: an empirical study on foreign exchange rates

To discover whether the Carr-Geman-Madan-Yor (CGMY) distribution, a Lévy process that can have an infinite number of jumps in a finite time interval, is appropriate for describing the fat tails of the distribution of foreign exchange (FX) rate returns, we investigate its performance in estimating the risk of FX rates. We estimate valu-at-risk (VaR), one of the most popular concepts in the area of risk management, for FX rate returns. To enhance the robustness of the estimation results, we use other VaR models, such as historical simulation, the generalized autoregressive conditional heteroscedasticity (GARCH) model and conditional extreme value theory (EVT). Our four methodologies are applied to three major foreign exchange rates (EUR/JPY, EUR/USD and USD/JPY) by using a different size estimation windows for each model to forecast one-day-ahead VaR. We propose a new procedure to find the best size of estimation window for each VaR model, with which we compare the respective VaR estimates. The conditional EVT and CGMY distributions provide superior forecasting performance for all three rates's daily returns, respectively. Furthermore, the performance of the other methods can be significantly improved by adjusting the size of the estimation window.

Equal risk allocation with carry, value and momentum

In this paper, we analyze an equal-weight portfolio of global cross-asset-class risk factor exposures. Our main finding is that such risk factor allocation largely replaces traditional global equity and bond market premiums as well as allocation to hedge funds (in the expected utility maximization sense). Hence, we question the economic validity of the Alpha-Beta separation paradigm that currently prevails in the industry. We show that our results are robust to transaction costs. The empirical analysis is made for a global portfolio of three well-known risk factors: momentum, value and carry. Each risk factor is broadly diversified across four global asset classes and taken both in cross-sectional and time series contexts. We test our approach using thirty-eight years of daily historical prices in a broad set of futures contracts and major foreign exchange rates.

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.

Exchange-rate return predictability and the adaptive markets hypothesis: Evidence from major foreign exchange rates

This study examines return predictability of major foreign exchange rates by testing for martingale difference hypothesis (MDH) using daily and weekly nominal exchange rates from 1975 to 2009. We use three alternative tests for the MDH, which include the wild bootstrap automatic variance ratio test, generalized spectral test, and Dominguez–Lobato consistent tests. We evaluate time-varying return predictability by applying these tests with fixed-length moving sub-sample windows. While exchange rate returns are found to be unpredictable most of times, we do observe a number of episodes of statistically significant return predictability. They are mostly associated with the major events such as coordinated central bank interventions and financial crises. This finding suggests that return predictability of foreign exchange rates occurs from time to time depending on changing market conditions, consistent with the implications of the adaptive markets hypothesis.

Bayesian Model Choice of Grouped t-Copula

One of the most popular copulas for modeling dependence structures is t-copula. Recently the grouped t-copula was generalized to allow each group to have one member only, so that a priori grouping is not required and the dependence modeling is more flexible. This paper describes a Markov chain Monte Carlo (MCMC) method under the Bayesian inference framework for estimating and choosing t-copula models. Using historical data of foreign exchange (FX) rates as a case study, we found that Bayesian model choice criteria overwhelmingly favor the generalized t-copula. In addition, all the criteria also agree on the second most likely model and these inferences are all consistent with classical likelihood ratio tests. Finally, we demonstrate the impact of model choice on the conditional Value-at-Risk for portfolios of six major FX rates.

Optimal modelling frequency for foreign exchange volatility forecasting

For the major foreign exchange rates, it is found that the optimal modelling frequency of volatility is weekly for forecast horizons ranging from 1 week up to 1 month. Autoregressive modelling is based on realized volatility measures computed from 30 min returns.

Nonparametric inference of discretely sampled stable Lévy processes

We study nonparametric inference of stochastic models driven by stable Lévy processes. We introduce a nonparametric estimator of the stable index that achieves the parametric n rate of convergence. For the volatility function, due to the heavy-tailedness, the classical least-squares method is not applicable. We then propose a nonparametric least-absolute-deviation or median-quantile estimator and study its asymptotic behavior, including asymptotic normality and maximal deviations, by establishing a representation of Bahadur–Kiefer type. The result is applied to several major foreign exchange rates.

Foreign exchange market efficiency, cointegration, and policy coordination

This paper examines market efficiency of the major foreign exchange markets during the post-Bretton Woods era by applying the bivariate and multivariate cointegration estimations to the weekly data of seven major foreign exchange rates. In particular, this study investigates whether the major foreign exchange policy coordinations, such as the Plaza Agreement (1985) and the Louvre Accord (1987), affected the long-run relations of the bilateral spot-forward and multivariate exchange rates. The findings of this study are: first, empirical evidence based on the bivariate and multivariate cointegrations is mostly consistent with the efficient market hypothesis in the major foreign exchange markets during the post-Bretton Woods era; second, the international policy coordinations during the late 1980s have affected acrosscountry market efficiency by introducing common stochastic trends in the system of the seven major foreign exchange markets during the period of the policy implementation, but the market inefficiency did not last long; and third, within-country market efficiency appears to have become stronger in the post-Plaza agreement period than before, evidenced by exhibiting more cointegrating vectors, and have not been affected by the major foreign exchange policy coordinations.

Real-Time Trading Models and the Statistical Properties of Foreign Exchange Rates

Real-time trading models use high frequency live data feeds and their recommendations are transmitted to the traders through data feed lines instantaneously. In this paper, a widely used real-time trading model is considered as a tool to evaluate the statistical properties of foreign exchange rates. This is done by comparing the out-of-sample results of the trading model to those obtained by feeding the algorithm with data simulated from theoretical processes fitted to the real data. The out-of-sample test period is seven years of five-minute series on three major foreign exchange rates against the US Dollar and one cross-rate. Performance of the real-time trading models is measured by the annualized return, two measures of risk corrected annualized return, deal frequency and maximum drawdown. The simulated probability distributions of these performance measures are calculated with three popular processes, the random walk, GARCH and AR-GARCH. The null hypothesis that the real-time performances of the foreign exchange series are generated from these traditional processes is tested under the probability distributions of the performance measures. In other words, we compute the probability of the real performance if the distribution was originated by these processes. All four currencies yield positive annualized returns in the studied sampling period. These annualized returns are net of transaction costs. The results indicate that the excess returns of the real-time trading models, after taking the transaction costs and correcting for market risk, are not spurious. The random walk, GARCH(1,1) and AR-GARCH(1,1) processes are rejected as the data generating mechanisms for the high frequency foreign exchange rates. One important reason for this rejection is the aggregation properties of the GARCH processes. The GARCH process behaves more like a homoskedastic process at lower frequencies. Since the trading frequency is less than two deals per week, the trading model does not pick up the five minute level heteroskedastic structure at the weekly frequency. The results indicate that the foreign exchange series may possess a multi-frequency conditional mean and conditional heteroskedastic dynamics. The traditional heteroskedastic models fail to capture the entire dynamics by only capturing a slice of this dynamics at a given frequency. Therefore, a more realistic processes for foreign exchange returns should give consideration to the scaling behavior of returns at different frequencies and this scaling behavior should be taken into account in the construction of a representative process.

Testing for Nonlinear Dependence in Daily Foreign Exchange Rates

The purpose of this article is to investigate whether daily changes in five major foreign exchange rates contain any nonlinearities. Although the data contain no linear correlation, evidence indicates the presence of substantial nonlinearity in a multiplicative rather than additive form. Further examination reveals that a generalized autoregressive conditional heteroskedasticity model can explain a large part of the nonlinearities for all five exchange rates. Copyright 1989 by the University of Chicago.