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In recent decades, interconnections between countries have increased substantially worldwide as the process of integration and globalisation intensifies, with a positive impact in terms of economic development, but, also with a vulnerability to external shocks, such as the financial contagion phenomenon. The analysis of this research field becomes even more relevant in the context of a new major exogenous shock, but which, this time, has different specificities, being a sanitary crisis. Thus, the chapter aims to investigate the impact of crises on capital market volatility for the period of 1995–2021, using the bibliometric analysis highlighting the dynamics of the literature and potential future research directions through a science mapping that enables investigating scientific knowledge. In order to explore the development of the research field in terms of publications, author impact, affiliated institutions and countries, citation patterns, trending topics, relationship between keywords–authors–journals, abstracts’ analysis, authors and documents clustering by coupling, multiple correspondence analysis of major research themes, keyword analysis, co-citation analysis and authors, institutions and countries collaboration analysis have been applied. Hence, almost 500 publications from Web of Science database covering the period 1995–2021 have been extracted. The empirical findings emphasise the conceptual structure, with clusters focussing mainly on long-term receivables, market efficiency, volatility, dynamic conditional correlation (DCC)-GARCH models, asymmetric effects. According to the intellectual structure of the field, Lambertides N., Zopiatis A., McAleer M. or Savva C. S. are the most representative authors for the sub-area of volatility topic; whilst Balcerzak A. P., Pietrzak M. B., Zinecker M., Meluzin T. and Faldzinski M. are the reference names for the whole spectrum of DCC-GARCH models’ topic. Jayasekera R., Lundblad C., Choundhry T., Gupta R. and Demirer R. are the authors mostly associated with asymmetric effects’ topic, whilst Thorp S., Bouchaud J. P. and Dungey M. with the quantitative finance. The Journal of Banking & Finance, the Journal of International Money and Finance and the International Review of Financial Analysis as well as Economic Modelling, Research in International Business and Finance and the International Journal of Finance & Economics are the most prolific journals in the field of capital flow and financial crises. This chapter’s main contribution is to build a structure of knowledge for the impact of crises on capital market volatility, elaborate and classify empirical research into relevant dimensions that can be used as a reference for comprehensively developing research. Finally, the bibliometric analysis results may provide insight into future research prospects. Our conclusions offer some recommendations for market practitioners and policy-making.

A thorough understanding of transmission mechanism is a key to a successful conduct of monetary policy. This chapter attempts to improve knowledge in this respect by examining the impacts of commercial bank risks on the transmission of monetary policy. We investigate the impact of monetary policy on bank risk in Vietnam pre and post 2008 global financial crisis employing a unique and disaggregated bank level data set from 2003 to 2012. The results of panel data estimation indicate that the bank lending channel of monetary is evidenced in Vietnam. In addition, we find that the transmission mechanism is affected by characteristics of commercial banks.

Adolescent alcohol consumption has declined in most high-income countries over the last decade; however, the prevalence of drinking under the legal age of 18 years remains high. There are several confounding factors related to alcohol use inclusive of gender, poverty, parental education, parental alcohol use and parental mental health difficulties. In addition, young people placed under the care of the state are disproportionately affected by alcohol misuse.

Longitudinal research has shown a linear risk between alcohol consumption and educational performance. Adolescents that have heavy alcohol consumption are associated with lower enrolment in post-secondary education, potentially reduced earnings and heightened job instability.

Universal interventions are one potential way to provide education regarding problematic alcohol use and its consequences. A recent Cochrane review identified that school-based interventions have potential to provide adolescents with the necessary knowledge, skills and opportunities for young people to remain alcohol free and decrease the risk of multiple risk-taking behaviours.

Scientific research provides a reliable means for developing and accumulating knowledge bases to guide policy and practice. However, evidence from meta-research and large-scale replication projects suggests that the published research base likely reflects bias, which threatens the validity and credibility of research-based recommendations. Moreover, there is limited accessibility to research reports, which limits the impact and application of scientific research. In this chapter, we propose that open-science reforms, which aim to make the research process as open and transparent as possible, can be applied to help address these issues. We describe and discuss four open-science practices – preregistration and Registered Reports, open data and materials, open peer review, and open access and preprints – and propose that they may become one of the next big things in special education research.

Unfortunately, the answers given are thoroughly embedded in the physics-inspired view of the financial economy as a stable and an equilibrium seeking system. In such a view, if some changes do occur in the financial markets, those changes present no discontinuities and the model has ample time to react by slowly adjusting risk forecasts as the volatility rises. As almost everybody in the world by now knows, currently accepted risk models have time and again shown their inability to deal with financial market reality. Frequent talk of ‘hundred year floods’ and ‘rise in correlations’ not only suggests frequent failures of a theory, but also the inability of the theory to learn from past mistakes by incorporating new data. The crash of 2008, completely unforeseen by all traditional risk systems, should serve as the final wake-up call to re-examine the foundations of the old paradigm and consider how sound they really are.

Importance sampling is a popular Monte Carlo method used in a variety of areas in econometrics. When the variance of the importance sampling estimator is infinite, the central limit theorem does not apply and estimates tend to be erratic even when the simulation size is large. The authors consider asymptotic trimming in such a setting. Specifically, the authors propose a bias-corrected tail-trimmed estimator such that it is consistent and has finite variance. The authors show that the proposed estimator is asymptotically normal, and has good finite-sample properties in a Monte Carlo study.

Statistical inference (estimation and testing) for the stochastic volatility (SV) model Taylor (1982, 1986) is challenging, especially likelihood-based methods which are difficult to apply due to the presence of latent variables. The existing methods are either computationally costly and/or inefficient. In this paper, we propose computationally simple estimators for the SV model, which are at the same time highly efficient. The proposed class of estimators uses a small number of moment equations derived from an ARMA representation associated with the SV model, along with the possibility of using “winsorization” to improve stability and efficiency. We call these ARMA-SV estimators. Closed-form expressions for ARMA-SV estimators are obtained, and no numerical optimization procedure or choice of initial parameter values is required. The asymptotic distributional theory of the proposed estimators is studied. Due to their computational simplicity, the ARMA-SV estimators allow one to make reliable – even exact – simulation-based inference, through the application of Monte Carlo (MC) test or bootstrap methods. We compare them in a simulation experiment with a wide array of alternative estimation methods, in terms of bias, root mean square error and computation time. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that ARMA-SV estimators match (or exceed) alternative estimators in terms of precision, including the widely used Bayesian estimator. The proposed methods are applied to daily observations on the returns for three major stock prices (Coca-Cola, Walmart, Ford) and the S&P Composite Price Index (2000–2017). The results confirm the presence of stochastic volatility with strong persistence.

During the banking crisis of October 2008, Iceland became the first developed country in decades to seek the assistance of the International Monetary Fund (IMF). Iceland’s IMF programme provided a measure of stability at a time of intense turbulence. The IMF’s credibility was helpful during this period of collapse not just of the banks but also of the public trust towards almost all Icelandic institutions. Importantly, the IMF implicitly supported Iceland’s policy of letting institutional creditors of the banks rather than Icelandic taxpayers bear the costs of their collapse; this provided credibility for the policy and limited repercussions. In a reversal of previous IMF policy, capital controls were imposed. The controls helped stabilise the exchange rate, and inflation subsided. The controls also helped recovery after the crisis by shielding the economy from international financial shocks. The direct fiscal cost of the Icelandic crisis was very high, but the considerable and painful fiscal tightening that was a part of the programme was needed to avoid a sovereign debt crisis. This helped in regaining trust from international markets. Mistakes were made in the design and implementation of the IMF programme, but overall, we judge that its contribution was positive. The programme provided one of the elements for restoring trust in Iceland when it was most needed, both domestically and internationally, during the depth of the crisis in 2009–2010.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.