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This paper aims to investigate the contagion effects of stock and FX markets for the USA and european monetary union (EMU) during the US subprime crisis of 2007-2009.

The data sample is daily comprising a weighted Morgan Stanley Capital Index (MSCI) for US and EMU equity markets, as well as EUR/USD exchange rate and 3-month US and EMU interest rate indices. The authors model, simultaneously, the dynamic conditional correlations (DCC) for the triplet: US, EMU equity markets and euro – USD uncovered interest rate parity (UIP) via a multivariate GARCH(1,1)-DCC model. The authors also test for a level shift increase of DCCs during the crisis period by incorporating a dummy variable in a GARCH(1,1) model.

Our results suggest the presence of contagion for the US stock market and UIP. These results indicate that possibilities for portfolio diversification exist even in periods of severe financial turmoil. This can be explained by the different monetary policies that followed during the crisis. While USA increased liquidity through stimulus packages in early 2009, EMU preferred a strict monetary policy and fiscal austerity measures. Consequently, the EUR/USD exchange rate was less volatile than the EMU equities, resulting in their weak co-movement.

These findings confirm a specific pattern of contagion that provide important implications for international investors and policy-makers.

The stability of exchange rates facilitates international trade, diminishes portfolio risk, and ensures that economic policies are effective. The war in Ukraine is showing that the European financial system is still fragile to external shocks. This paper examines the consequences of the Russian invasion of Ukraine on five Euro exchange rates. The final goal is to empirically test whether the ruble caused the euro to depreciate with the Russian invasion of Ukraine.

The exchange rates analyzed are Euro/Russian Ruble, Euro/US Dollar, Euro/Japanese Yen, Euro/British Pound, and Euro/Chinese Yuan. The data collected are daily and cover the period from November 1, 2021, to May 1, 2022. In this context, the changes in the FX rates reflect two months of the ongoing war in Ukraine. The FX rates used in the study contain 137 observations indicating five months of daily series.

The results from impulse response function, variance decomposition, SVAR, and VECM indicate that the EUR/RUB significantly influenced the Euro devaluation. On the other side, the FX rates used in our work altogether hold long-run cointegration. The situation is different in the short run, where only EUR/RUB, EUR/USD, and EUR/CNY possess significant relations with other parities.

The Ruble is not among hard currencies, but its position strengthened during this period due to the importance of Russian gas to the Eurozone. The results indicate that even weak currencies can be influential depending on the geopolitical and economic situation. To this end, diversification remains a valid concept not only in portfolio construction but also for the preservation of the national economy.

The purpose of this paper is to estimate long-term currency exchange rate and also identify the key factors for decision makers in the currency exchange market. The study is expected to aid decision makers to take positions in the dynamic Forex market.

This study is based on quantitative and fundamental analysis of statistically oriented regression models. The trend of quarterly exchange rates is investigated using 110 variables including economic elements, interest rate and other currencies. This research is based on the same information that banks’ dealers use for the analysis. Ordinary least squares linear regression also known as “least squared errors regression” was used to estimate the value of the dependent variable.

The study concludes that “only Australian economic data” or “only the US economic data” cannot fully reflect the trend of AUD/USD. EUR influences AUD relatively larger than the other main market currencies. Six-month Australian interest rate itself affects AUD/USD trend much more than the six-month interest difference between AUD and USD.

The results indicate that the economic autoregressive moving average model can be used to predict future exchange rate using primary factors identified and not from the generic market or economic view. This helps adjust to the general, common (and possibly wrong) views when making a buy or sell decision.

This is one of the first studies in the context using the information of bank dealers for AUD/USD. This study is highly relevant in the current context, given the significant growth in Forex trade.

The purpose of this paper is to study the lead-lag relationship between the futures and spot foreign exchange (FX) market in India to understand the price discovery mechanism and the relationship between these two markets.

The estimation of lead-lag relationship is realized in three steps. First unit root and stationarity tests (Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski-Phillips-Schmidt-Shin) are applied to check the stationarity of the data. Second, cointegration tests (Engle and Granger’s residual based approach and Johansen’s cointegration test) are applied to determine long run relationship between the markets. Third, error correction estimation is carried out by applying Vector Error Correction Model (VECM) to determine the leading market.

The study finds that there is a long run relationship between the futures and spot market where the futures market has emerged as the leading market for the four currencies studied in the paper.

Majorly, the studies on Indian FX market limit themselves to identifying the efficiency of the market and the studies which talk about the lead-lag relationship focus on the Indian stock market. This paper enhances the existing literature on Indian FX market by exploring the less explored subject of the lead-lag relationship between futures and spot FX market in India.

The 2007 BIS Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity Report shows a substantial increase in turnover in foreign exchange and OTC derivatives markets. Turnover in traditional FX markets increased to reach $3.2 trillion. The largest contributor to this 71% increase between April 2004 and April 2007 occurred in FX swaps. It was like a prelude to the financial crisis of 2007–2008 driven by transactions carried out between banks and other financial institutions due to the significance of hedge funds and major engagement of emerging market currencies which have sought new configurations of portfolio diversification worldwide.

The purpose of this paper is to investigate whether a non-monotonic relationship may exist between financial distress and foreign exchange (FX) exposure. The authors hypothesize that firms with higher FX exposures are those with the lowest levels of financial distress because the costs of hedging exceed the benefits and those with highest levels of financial distress due to the conflict of interest between shareholders and bondholders.

The methodology allows for the possibility of a non-monotonic relation between financial distress and FX exposure for firms known to have ex-ante exposures. The approach is to include a Black-Scholes-Merton financial distress measure and standard accounting-based financial distress measures.

The results support the hypothesis of a non-monotonic relationship between financial distress and exposure; companies with the lowest and highest levels of financial distress are willing to bear greater FX exposures.

The authors examine whether a non-monotonic relationship may exist between distress and FX exposure. Intuition for this non-monotonic relationship is provided by Stulz (1996) as he describes the risk management practices of firms with low, medium, and high default probabilities.

Corporate treasurers manage the currency risk of their organization by hedging through futures contracts. The purpose of this paper is to evaluate the effectiveness of hedging by US currency futures contracts by taking into account the efficiency of the currency market.

The static models for calculating hedge ratio are as popular as dynamic models. But the main disadvantage with the static models is that they do not consider important properties of time series like autocorrelation and heteroskedasticity of the residuals and also ignore the cointegration of the market variables which indicate short-run market disequilibrium. The present study, therefore, measures the hedging effectiveness in the US currency futures market using two dynamic models – constant conditional correlation multivariate generalized ARCH (CCC-MGARCH) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH).

The study finds that both the dynamic models used in the study provide similar results. The relative comparison of CCC-MGARCH and DCC-MGARCH models shows that CCC-MGARCH provides better hedging effectiveness result, and thus, should be preferred over the other model.

The findings of the study are important for the company treasurers since the new updated Indian accounting standards (Ind-AS), applicable from the financial year 2016–2017, make it mandatory for the companies to evaluate the effectiveness of hedges. These standards do not specify a quantitative method of evaluation but provide the flexibility to the companies in choosing an appropriate method which justifies their risk management objective. These results are also useful for the policy makers as they can specify and list the appropriate methods for evaluating the hedge effectiveness in the currency market.

Majorly, the studies on Indian financial market limit themselves to either examining the efficiency of that market or to evaluate the effectiveness of the hedges undertaken. Moreover, most of such works focus on the stock market or the commodity market in India. This is one of the first studies which bring together the concepts of efficiency of the market and effectiveness of the hedges in the Indian currency futures market.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.