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The purpose of this paper is to examine how goal achievement and goal exceedance influence the aspects of loyalty in logistics outsourcing relationships. Specifically, it aims to develop and test a model of customer loyalty across two cultures to determine if dedicated strategies for building loyalty are required.

This effort develops a conceptual model that provides a better understanding of the relationship between two dimensions of logistics outsourcing performance (goal achievement and goal exceedance) to loyalty across cultures. The model is then tested using structural equation modeling along with multi‐group analysis.

The findings indicate that goal achievement strongly influences the loyalty aspects of retention and referrals, but not extension. Meanwhile, all three dimensions of loyalty were influenced by goal exceedance of the logistics provider. Further, goal achievement was found to have a stronger effect on retention only, with goal exceedance demonstrating a stronger influence on extension and referrals. In addition, cultural differences in the model were identified.

Future research should examine more transactional settings as well as other potential moderators that may be consequential to the examination of loyalty formation.

The findings suggest that logistics service providers (LSPs) need to have an appreciation for the differences between goal achievement and goal exceedance as it relates to loyalty formation. In addition, LSPs need to adapt their performance goals based on cultural differences that may exist across their markets.

The close examination of the two dimensions of outsourcing performance on three aspects of loyalty behavior builds on the extant literature. The examination across the two national settings provides yet another contribution of the study.

The purpose of this paper is to focus on flight exceedances in pilots' operations. With some bad conditions, such as a bad weather, flight exceedances might lead to serious consequences. They are significant hidden dangers of aviation. Risk analysis is carried out to identify pilots' high‐risk or low‐risk operations.

A multi‐objective optimization model is proposed for risk analysis of flight operations. An evolutionary algorithm is designed to divide flight operation state‐space into some high‐risk and low‐risk sub‐spaces.

Through the empirical study of a certain flight exceedance with the analysis model, the authors discover some high‐risk flight operations, which indicate coordination problems in coordinate control of airplane's speed, rate of descent, heading, roll and pitch, etc.

This paper employs a quantitative model to carry out risk analysis of flight operations. The results are useful to pilots' training and may improve flight safety fundamentally. The risk analysis of flight exceedance is one specific case in airlines safety risk management. Some other problems, such as cause analysis of flight delay, aircraft faults diagnosis, can be addressed in the same way and dealt with by specific model adjustments and algorithm designs.

The purpose of this paper is to examine how the customer partnering behavior dimensions – operational information exchange, planning, sharing of benefits and burdens, and extendedness – influence goal achievement and goal exceedance at the interface between customer firms and their logistics service providers.

This research develops a conceptual model of relationships between the customer partnering dimensions and logistics outsourcing performance (goal achievement and goal exceedance). The model is then tested using partial least squares structural equation modeling.

The results indicate that sharing of benefits and burdens has no significant effect on performance, while the other partnering dimensions positively influence goal achievement and goal exceedance. Amongst these partnering dimensions, extendedness demonstrates the strongest effect on both performance dimensions. Furthermore, operational information exchange primarily influences goal exceedance, while planning primarily impacts goal achievement.

The paper extends the extant logistics outsourcing literature by examining the effects of established customer partnering dimensions on performance.

The purpose of this paper is to examine the effectiveness of building codes in earthquake risk mitigation in Taiwan.

Using probabilistic risk analysis tools with available data, this study assesses the exceedance probability of extensive damage limit for general buildings in their 50‐year useful lives. The buildings were classified into 15 categories according to their construction materials and building height. Then, the effects of construction materials, building height and construction years are detected.

The exceedance probabilities of extensive damage limit for all of the investigated buildings in their 50‐year useful lives are on the order of 10⁻². The effect of construction materials and building height on seismic risk of buildings is decreasing with the development of a seismic design code. Significant discrepancy of seismic risk still exists among some buildings.

Seismic risk analysis requires quite restrictive statistical idealizations for the relevant probabilistic terms in the mathematical formulation. The problem of imperfect simplification and lack of sufficient empirical data has shown the research needs for improvements of seismic risk assessment. The questions of what constitutes acceptable risk for various performance levels and how safe is safe enough remain context‐specific.

Although probabilistic risk analysis provides a tool for quantifying the probability of structural failure, current earthquake‐resistant design procedures do not relate performance levels to probability. The paper explores some probability information for current earthquake‐resistant design for general buildings during their 50‐year useful lives and the information may provide some valuable information for future code calibration.

This chapter examines the impact of price fluctuations in foreign stock markets on the stock prices of domestic banks in Korea, Malaysia, Singapore, and Thailand. Some studies have argued that the 2007–2009 global financial crisis (GFC) affected domestic banks less in East Asia, even though the supporting evidence is rather limited. Employing a multinomial logit model, we estimate how changes in the United States and Japanese stock markets affected the banking sectors in the sampled countries before the 1997 Asian financial crisis, and before and during the more recent GFC. We interpret the number of banks in a given country that experienced a large price shock on the same day (or “coexceedance”) as shocks to the domestic banking sector. The results suggest that fluctuations in foreign stock market indices exerted a larger impact on the prices of East Asian banking stocks during the 2000s than during the 1990s. In addition, although the shocks brought about by the deterioration of foreign stock markets were significant before the GFC, both increases and decreases in foreign stock prices significantly affected the banking sectors of the respective countries during the crisis. Lastly, we conclude that increasing foreign capital flows and foreign assets and liabilities greatly influenced domestic banking systems in East Asia during the 2000s.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY.

By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation.

In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure.

To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.

This study aims to examine the systemic risk contagion in banks from 15 US states using extreme shocks in their distance to risk.

The authors contemplate a model that inputs co-exceedances in the base US states’ banking sector as the dependent variable and the co-exceedances in other states’ banking sector (along with other underlying variables of a banking system) as the explanatory variables.

The authors find smaller states transmit and receive more systemic shocks than their larger counterparts and larger states exhibit a better shock-resisting capacity than their smaller counterparts. The authors also find that bigger shocks are more contagious than the smaller shocks.

This will be the first paper that will investigate the inner linkage of US states’ banking network using three different distance to risk methods, thus providing timely guidance for regulators.

Using a market model of international equity returns, which fully incorporates the regime switching and heteroskedasticity effects, we conduct an empirical study on the asymmetric behavior of 31 emerging equity markets across the different regimes of both the global and the local markets. Asymmetric correlation is found to be much weaker than that among developed markets as documented in the recent studies. There is little evidence of performance enhancement by possessing information on asymmetric correlation in international asset allocation strategies involving emerging markets.