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This paper makes a novel attempt to estimate the potential impact of credit risk on foreign direct investment (FDI hereafter), thereby focusing on a completely unexplored area in the existing empirical literature.

To provide a comprehensive understanding of the relationship between credit risk and FDI inflows, the study incorporates all the eight-member economies of the South Asian Association of Regional Cooperation (SAARC hereafter) and analyzes a panel data set, over the period 2011 to 2019, extracted from the World Development Indicators, using the suitable econometric techniques for the efficient estimations of the specified models.

The results indicate a negative and statistically significant relationship between the credit risk of the banking sectors and FDI inflows. Similarly, market size and inflation rate appear to be the two other main factors behind the increasing FDI inflows in the SAARC member economies. Interestingly, the size of the market became irrelevant in attracting FDI inflows when the Indian economy is excluded from the sample due to its higher economic weight. On the other hand, FDI inflows are not dependent on the level of trade openness, with most of the specifications showing either an insignificant or negative coefficient of the variable.

The obtained results are unique and robust to alternative methodologies, and hence, the SAARC economies could consider them as the critical inputs in formulating the appropriate policies on FDI inflows.

The findings are unique and original. The authors have established a relationship between credit risk and FDI for the first time in the SAARC context.

The accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.

By using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.

The authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.

Future cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.

The knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.

To the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.

The purpose of this paper is to examine the impact of real exchange rate misalignment on economy and economic sectors, namely construction, manufacturing and mining and quarrying in Malaysia.

The equilibrium real exchange rate and economic models are estimated using the autoregressive distributed lag approach.

An increase in productivity differential or reserve differential will lead to an appreciation of real exchange rate in the long run. An increase in positive (negative) real exchange rate misalignment will lead to an increase (decrease) in economy. An increase in long-run real exchange rate misalignment will lead to a decrease in economy. Real exchange rate misalignment or long-run real exchange rate misalignment can influence the manufacturing sector in Malaysia. More specifically, undervaluation will promote whereas overvaluation will hurt the manufacturing sector.

Real exchange rate misalignment can be a policy to influence economy but may not be the best choice.

This study aims to analyse the conditional volatility of the Vietnam Index (Ho Chi Minh City) and the Hanoi Exchange Index (Hanoi) with a specific focus on their application to risk management tools such as Expected Shortfall (ES).

First, the author tests both indices for long memory in their returns and squared returns. Second, the author applies several generalised autoregressive conditional heteroskedasticity (GARCH) models to account for asymmetry and long memory effects in conditional volatility. Finally, the author back tests the GARCH models’ forecasts for Value-at-Risk (VaR) and ES.

The author does not find long memory in returns, but does find long memory in the squared returns. The results suggest differences in both indices for the asymmetric impact of negative and positive news on volatility and the persistence of shocks (long memory). Long memory models perform best when estimating risk measures for both series.

Short-time horizons to estimate the variance should be avoided. A combination of long memory GARCH models with skewed Student’s t-distribution is recommended to forecast VaR and ES.

Up to now, no analysis has examined asymmetry and long memory effects jointly. Moreover, studies on Vietnamese stock market volatility do not take ES into consideration. This study attempts to overcome this gap. The author contributes by offering more insight into the Vietnamese stock market properties and shows the necessity of considering ES in risk management. The findings of this study are important to domestic and foreign practitioners, particularly for risk management, as well as banks and researchers investigating international markets.

The purpose of this paper is to investigate whether the factors that summarise the information in the yield curves of Australia and the USA can predict changes in the Australian–USA exchange rate (i.e. the AUD/USD rate) and Australian dollar excess returns.

The paper extracts the three Nelson–Siegel factors (level, slope and curvature) from the relative yield curve of Australia with the USA to predict changes in the bilateral exchange rate and excess returns on the Australian dollar. The full sample regressions allow for a shift in the coefficient on the relative curvature factor which can account for the impact of the Fed’s changed monetary policy to one of quantitative easing.

The paper finds that the relative curvature factor strongly predicts changes in the AUD/USD exchange rate and Australian dollar excess returns out to 12 months ahead in the sample that precedes the Fed’s policy of quantitative easing. The relative curvature factor retains its predictive power in the full sample regressions but anticipates smaller exchange rate changes and excess currency returns in in-sample predictions made from August 2007.

The yield curves of Australia and the USA reliably reflect investor’s expectations about prospective monetary policies in each economy.

The paper investigates the predictive content of the relative Nelson–Siegel factors for changes in the AUD/USD exchange rate and for Australian dollar excess returns over various forecast horizons for a period that covers the Fed’s policy of quantitative easing.

The study mainly aims to examine the currency misalignment of Turkish lira and evaluate if it has an impact on economic growth of Turkey.

It relies on Johansen cointegration technique for measuring currency misalignment relying on single-equation approach and the autoregressive distributed lag (ARDL) approach to evaluate how misalignment affects economic growth. The sample period covers from 1980 to 2016.

The study identifies that terms of trade, relative productivity differences, net foreign asset, investment and trade openness determine the equilibrium REER of Turkey, and the degree of currency misalignment is observed at a substantial level. The outcome of the ARDL approach suggests that higher currency misalignment reduces economic growth. Turning to the separate impacts of undervaluation and overvaluation, while the former falters economic growth, the later promotes it, a finding contrary to the conventional expectation. Therefore, the use of exchange rate as a policy variable is a critical concern to avoid misalignment for sustained economic growth.

The anti-growth effect of undervaluation and misalignment is an indication of redistribution of income which could be verified by examining the aggregate consumption behavior of the economy in response to RER movements.

The impacts of currency undervaluation and overvaluation on economic growth of Turkey have been studied in a number of time-series studies. But there is no documented study on the role of currency misalignment on Turkish economic growth. This study is the first that examines how the economic growth of Turkey is influenced by currency misalignment together with the impact of undervaluation and overvaluation.

This paper aims to investigate the relationship between capital flow surges, reversals and sudden stops.

Emphasizing the importance of looking at the behavior of domestic as well as foreign capital flows, the authors distinguish sudden stops from capital flow reversals by attributing the former to foreign capital flows only.

It is found that, despite the large differences in the number of surges identified by several different measures in the literature, a majority of surges do end in reversals of some type. The percentages tend to be slightly over half for surges in net capital flows, but on average, 70 per cent of gross surges end in sudden stops. Furthermore, contrary to popular belief, approximately half of sudden stops and net capital flow reversals are not preceded by surges. It is also found that surges that persist longer are more likely to turn into sudden stops and reversals.

The authors find substantial empirical differences in the characteristics of sudden stops (based on gross foreign flows) and reversals (based on net flows).

Large inflows of financial capital are not always a strong indicator that a country’s economic policies will continue to provide stability in the future. They may signal an increase rather than reduction in the risk of future instability.

This study focuses on an issue that has been less explored to date, the relationship between capital flow surges, reversals and sudden stops. The authors distinguish, redefine and document differences among capital flow reversals and sudden stops. Duration of surges is related to the likelihood of having reversals and sudden stops.

The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization.

In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis.

Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively).

The study can be extended by including more cryptocurrencies and high-frequency data.

The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

In this article we propose a multivariate dynamic probit model. Our model can be viewed as a nonlinear VAR model for the latent variables associated with correlated binary time-series data. To estimate it, we implement an exact maximum likelihood approach, hence providing a solution to the problem generally encountered in the formulation of multivariate probit models. Our framework allows us to study the predictive relationships among the binary processes under analysis. Finally, an empirical study of three financial crises is conducted.