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The purpose of this paper is to analyse the long-term nature of the interrelationship between interest rate and exchange rate.

By employing Mexican data, the authors estimate a non-linear autoregressive distributed lags (NARDL) model to investigate the nature of the changes and the interaction between interest rate and exchange rate in response to monetary authorities’ actions.

The results show that, contrary to simplistic predictions, the real exchange rate causes the real interest rate in an asymmetric way. The bounds testing approach of the NARDL models suggests the presence of co-integration among the variables and the exchange rate variations appear to have significant long-run effects on the interest rate. Most importantly, these effects are asymmetric and positive variations in the exchange rate have a lower impact on the interest rate. It is also interesting to report that the reverse is not true: the interest rate in the long-run exerts no statistical significant impact on the exchange rate.

The asymmetric long-term relationship between real exchange rate and real interest rate is evidence of why monetary authorities are reluctant to free float exchange rate. In Mexico, as in most developing countries, monetary policy strongly responds to exchange rate movements because these have relevant effects on commercial trade. Moreover, in dollarized economies these effects are stronger because of pass-through impacts to inflation, income distribution and balance-sheet equilibrium (the well-known “original sin”).

Under inflation targeting and flexible exchange rate regime, despite central banks pursue the control of short-term interest rate, in the long-run one could observe that it is the exchange rate that influences the interest rate, and that this reverse causality is stronger in emerging economies. This paper contributes by analysing the asymmetric relationship between the variables.

The aim of this study is to determine whether financial contagion is transmitted through macroeconomic fundamentals, not only in weaker countries but also in strong European Monetary Union (EMU) economies.

This study conducts, for the first time, an analysis of the spillover effects resulting from a shock to Italian sovereign risk on the banking systems and credit default swaps (CDS) of five EMU core countries during the period 2012–2018, employing a global vector autoregressive (GVAR) approach. Spatial interdependence is quantified through the cross-country distance in the deficit-to-gross domestic product (GDP) ratio.

The findings reveal the existence of both a “doom-loop” between banks and sovereign bonds and a “bad neighbours” effect. The susceptibility to spillovers is notably higher in economies displaying a larger deficit-to-GDP ratio. These results suggest that differences in fiscal fundamentals could drive financial contagion even within core countries, indicating a need for evaluating the stability of the entire EMU system.

Unlike previous studies, we utilize the cross-country distance in the deficit-to-GDP ratio as a measure of fiscal fundamentals distance for the countries under investigation. To the best of our knowledge, our study is the first to analyse this matter in EMU core countries using a GVAR methodology.

The idea of this study is to provide a solid Financial Condition Index (FCI) that allows the monetary transmission policy to be monitored in a country which in recent decades has suffered from major financial and monetary crises.

The authors construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime. Using monthly data from 1995 to 2017, the authors estimate FCIs with two different methodologies and build the index by taking into account the mechanism of transmission of monetary policy and incorporating the most relevant financial variables.

This study’s results show that, likewise for developing countries as Mexico, an FCI could be a useful tool for managing monetary policy in reducing macroeconomic fluctuations.

Apart from building a predictor of possible financial stress, the authors construct an FCI for a central bank that pursues inflation targeting and to analyse the role of financial asset prices in formulating monetary policy.

1. We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

2. The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

3. FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

4. FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

Risk-sharing is a crucial issue in order to evaluate the performance of a monetary union. By implementing conventional econometric techniques, this paper intends to estimate the degree of risk-sharing through the cross-ownership of assets within 11 European countries in the period 1971–2014. We show that risk-sharing has been increasing after the launch of the euro due to increased cross-ownership of assets. Nevertheless, we also show that despite the extreme needs for adjustment mechanisms as a reaction to asymmetric shocks in the EMU during the crises, the estimated market risk-sharing mechanism seems to have remained marginal in this period. We also show that the degree of asymmetry (potential benefits from risk-sharing) has declined with the start of the EMU, but it has sharply increased during the crises period. This implies that EMU countries have needed good functioning risk-sharing mechanisms during the crisis, while in this period their estimated performance does not seem to have improved. We interpret these results as the evidence of a missing element of the EMU that forced governments to intervene by means of fiscal policy to tackle the imbalances deriving from the financial crisis. Therefore, we conclude that the weakness in the risk-sharing has been one of the channels that allowed the global financial crisis to mutate in a sovereign debt crisis in the EMU.

This research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction. The study is centered on the creation of a new method that would enable the identification of both positive and negative selection criteria and the handling of ambiguous information in the decision-making process.

To do so, we develop an improved method by extending the WASPAS assessment in the context of bipolar complex fuzzy sets, which leads to the bipolar complex fuzzy WASPAS method. The approach also uses Einstein operators to increase the accuracy of aggregation and manage complicated decision-making parameters. The methodology is designed for the processing of multi-criteria decision-making problems where criteria have positive and negative polarities as well as other ambiguous information.

It is also shown that the proposed methodology outperforms the traditional weighted sum or product models when assessing feature selection methods. The incorporation of bipolar complex fuzzy sets with WASPAS improves the assessment of selection criteria by taking into account both positive and negative aspects of the criteria, which contributes to more accurate feature selection for software defect prediction. We investigate a case study related to the identification of feature selection techniques for software defect prediction by using the bipolar complex fuzzy WASPAS methodology. We compare the proposed methodology with certain prevailing ones to reveal the supremacy and the requirements of the proposed theory.

This research offers the first integrated framework for handling bipolarity and uncertainty in feature selection for software defect prediction. The combination of Einstein operators with bipolar complex fuzzy sets improves the DM process, which will be useful for software engineers and help them select the best feature selection techniques. This work also helps to enhance the overall performance of software defect prediction systems.