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The purpose of this paper is to measure a single financial institution's contribution to systemic risk by using extremal quantile regression and analyzing the influential factors of systemic risk.

Extreme value theory is applied when measuring the systemic risk of financial institutions. Extremal quantile regression, where extreme value distribution is assumed for the tail, is used to measure the extreme risk and analyze the changes in and dependencies of risk. Furthermore, influential factors of systemic risk are analyzed using panel regression.

The key findings of the paper are that value at risk and contribution to systemic risk are very different when measuring the risk of a financial institution; banks’ contributions to systemic risk are much higher; and size and leverage ratio are two significant and important factors influencing an institution's systemic risk.

Characterizing variables of financial institutions such as size, leverage ratio and market beta should be considered together when regulating and constraining financial institutions.

To take extreme risk into account, this paper measures systemic financial risk using extremal quantile regression for the first time.

The purpose of this paper is to investigate and compare the extreme behavior of securitized real estate and stock market returns as well as their value‐at‐risk (VaR) dynamics in international investing. Extreme value theory using the block maxima method is applied to ten securitized real estate and equity market indices representing Asian, European and North American markets.

The paper models the maxima and minima of all return series within the extreme value theory (EVT) framework and derive the VaR estimates. It then compares the VaR estimates derived from the EVT and the normal distribution and investigates the impact of clustered returns on the VaR estimates. Finally, both the conventional standard deviation measure and VaR method are conducted to evaluate and compare the impact of the Asian financial turmoil on the real estate and stock market risk profiles.

Evidence shows that Asian real estate and equity maxima and minima return series are characterized by a fat‐tailed Fréchet distribution. The frequency and severity of extreme Asian real estate returns are greater than their European and North American counterparts. Securitized real estate markets are riskier than the broader stock markets before and during the Asian financial turmoil. In contrast, many stock markets become riskier after the financial crisis with their VaRs higher than the equivalent VaR estimates for the real estate series.

Knowledge about real estate market returns exhibit extreme behavior can help investors and fund managers understand the distribution of real estate market returns better and obtain potentially more accurate real estate return forecasts.

International real estate portfolio risk management should include both extreme risks and standard deviations. Accordingly, global investors should be even more cautious in formulating their diversification strategies since gains from diversification can be reduced significantly by the severity of extreme return levels.

The paper characterizes the distribution of extreme returns for a broad spectrum of international securitized real estate markets from three continents. The extreme value investigation is also conducted for broader stock markets corresponding to the individual real estate markets. The July 1997 turmoil that occurred in Asian financial markets provides interesting exploratory opportunities within which this paper estimates and compares the extreme market risk with the conventional standard deviation measure.

Modern property investment allocation techniques are typically based on recognised measures of return and risk. Whilst these models work well in theory under stable conditions, they can fail when stable assumptions cease to hold and extreme volatility occurs. This is evident in commercial property markets which can experience extended stable periods followed by large concentrated negative price fluctuations as a result of major unpredictable events. This extreme volatility may not be fully reflected in traditional risk calculations and can lead to ruin. The paper aims to discuss these issues.

This research studies 28 years of quarterly Australian direct commercial property market performance data for normal distribution features and signs of extreme downside risk. For the extreme values, Power Law distribution models were examined as to provide a better probability measure of large negative price fluctuations.

The results show that the normal bell curve distribution underestimated actual extreme values both by frequency and extent, being by at least 30 per cent for the outermost data point. For the statistical outliers beyond 2 SD, a Power Law distribution can overcome many of the shortcomings of the standard deviation approach and therefore better measure the probability of ruin, being extreme downside risk.

In highlighting the challenges to measuring property market performance, analysis of extreme downside risk should be separated from traditional standard deviation risk calculations. In recognising these two different types of risk, extreme downside risk has a magnified domino effect with the tendency of bad news to come in crowds. Big price changes can lead to market crashes and financial ruin which is well beyond the standard deviation risk measure. This needs to be recognised and developed as there is evidence that extreme downside risk determinants are increasing by magnitude, frequency and impact.

Analysis of extreme downside risk should form a key part of the property decision process and be included in the property investment manager’s toolkit. Modelling techniques for estimating measures of tail risk provide challenges and have shown to be beyond traditional risk management practices, being too narrow and constraining a definition. Measuring extreme risk and the likelihood of ruin is the first step in analysing and dealing with risk in both an asset class and portfolio context.

The purpose of this paper is to discuss two important extensions to the well‐known value‐at‐risk (VaR) methodology, namely extreme value theory (EVT) and expected shortfall (ES). Both of these extensions address the weaknesses of VaR, in particular the methodology's tendency to systematically underestimate risk of extreme market events.

The theory of VaR and the two extensions are reviewed and the methodology is evaluated in light of the Basel II regulatory framework that calls for the use of VaR by financial institutions.

The paper clarifies the use of VaR and its extensions to make practitioners more aware of the pitfalls and how to address them. It is recommended that the two extended measures of extreme event risk (i.e. EVT and ES) be included into every risk manager's information pool.

A compact review of these approaches and their regulatory connection has not previously been compiled. This review is of particular value to risk managers and policy markers given the turbulent market conditions of the past year.

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

Application of financial risk models in the emerging markets poses special challenges. A fundamental challenge is to accurately model the return distributions which are particularly fat tailed and skewed. Value-at-Risk (VaR) measures based on the Extreme Value Theory (EVT) have been suggested, but typically data histories are limited, making it hard to test and apply EVT. The chapter addresses issues in (i) modeling the VaR measure in the presence of structural breaks in an economy, (ii) the choice of stable innovation distribution with volatility clustering effects, (iii) modeling the tails of the empirical distribution, and (iv) fixing the cut-off point for isolating extreme observations. Pakistan offers an instructive case since its equity market exhibits high volatility and incidence of extreme returns. The recent Global Financial Crisis has been another source of extreme returns. The confluence of the two sources of volatility provides us with a rich data set to test the VaR/EVT model rigorously and examine practical challenges in its application in an emerging market.

Extreme value theory (EVT) holds promise for advancing the assessment and management of extreme financial risks. Recent literature suggests that the application of EVT generally results in more precise estimates of extreme quantiles and tail probabilities of financial asset returns. This article assesses EVT from the perspective of financial risk management. The authors believe that the recent optimism regarding EVT may be appropriate but exaggerated, and that much of its potential remains latent. They support their claim by describing various pitfalls associated with the current use of EVT techniques, and illustrate how these can be avoided. In conclusion, the article defines several specific research directions that may further the practical and effective application of EVT to risk management.

The purpose of this paper is to apply safety‐first portfolio principles in an environment where financial risk exists because of the probability of terrorist attacks, where the catastrophic events of September 11, 2001 (911) are the focal point of the analysis.

Safety‐first portfolios of US equities bilaterally combined with 12 developed and emerging region global equity indices are obtained for 911. Extreme value theory and safety‐first principles are used to optimize these portfolios for US risk‐averse investors. The actual performances of all portfolios in the post‐911 period are compared to the optimal results. The robustness of the results is examined by replicating the analysis for the period following July 7, 2006, when no actual terrorist attacks occurred on US soil.

Optimal ex ante (ex post) safety‐first portfolios on 911 have high (low) US weights, and on July 7, 2006 low US weights. The differences are attributed to changes in market projections and/or conditions. In all cases, wealth is preserved even without the ex post optimal portfolios.

Safety‐first portfolio optimization can protect wealth given financial risks of extreme events like terrorist attacks.

The paper shows that quantitative assessments of financial risk are feasible, even though uncertainty with experts' risk assessments of extreme events such as 911 exists because of limited historical data and low probability of occurrence. The results are useful to investors developing international diversification strategies to protect wealth given the risks of terrorist attacks.

The paper aims to rethink empirical models and theory used in explaining banks and financial institutions (FIs) and to enhance the process of theory construction. This is a provisional response to Colander et al. (2009) and Gendron and Smith-Lacroix’s (2013) call for a new approach to developing theory for finance and FIs.

An embryonic “behavioural theory of the financial firm” (BTFF) is outlined based on field research about banks and FI firms and relevant literature. The paper explores “conceptual connections” between BTFF and traditional finance theory ideas of financial intermediation. It does not seek to “integrate” finance theory and alternative theory in “meta theory” and has a more modest aim to improve theory content through “connections”.

The “conceptual connections” provide a means to develop ideas proposed by Scholtens and van Wensveen (2003). They are part of a “house with windows” intended to provide systematic means to “take data from the outside world” whilst continuously recognising “the complexities of the context” (Keasey and Hudson, 2007) to both challenge and build the core ideas of FT.

The BTFF is a means to create “conversations” between academics, practitioners and regulators to aid theory construction. This can overcome the limitations of such an embryonic theory.

The ideas developed create new opportunities to develop finance theory, propose changes in banks and FIs and suggest changes in the focus of regulation.

Regulators can use the expanded conceptual framework to encourage theory development and to enhance accountability of banks and FIs to citizens.

This study aims to analyse the impact of the big five personality traits on the financial risk tolerance of individuals. Furthermore, it also examines the differences in personality traits and financial risk tolerance across four generations: baby boomers, Generation X, millennials and Generation Z.

The data constituted 869 responses from Indian individuals, collected using a self-administered structured questionnaire using a convenience sampling technique.

Structural equation modelling analysis showed that openness to experience, extraversion and neuroticism had a significant impact on financial risk tolerance. Multivariate analysis revealed the role of specific personality traits in predicting the financial risk tolerance of generational cohorts. Mean difference showed that millennials and Generation Z had the greatest risk tolerance, whereas the tolerance levels were lower for Generation X and baby boomers.

This research provides insights into the role of personality on financial risk-taking among generational cohorts in India. Thus, these results cannot be generalised for other risk-taking domains or outside the Indian context.

This study’s results align with the pulse rate hypothesis of generational theory and contribute to the growing field of behavioural economics and finance. It provides a perspective of the emerging economy of India, where behavioural finance studies are still at a nascent stage.