Fotios C. Harmantzis, Linyan Miao and Yifan Chien
This paper aims to test empirically the performance of different models in measuring VaR and ES in the presence of heavy tails in returns using historical data.
Abstract
Purpose
This paper aims to test empirically the performance of different models in measuring VaR and ES in the presence of heavy tails in returns using historical data.
Design/methodology/approach
Daily returns of popular indices (S&P500, DAX, CAC, Nikkei, TSE, and FTSE) and currencies (US dollar vs Euro, Yen, Pound, and Canadian dollar) for over ten years are modeled with empirical (or historical), Gaussian, Generalized Pareto (peak over threshold (POT) technique of extreme value theory (EVT)) and Stable Paretian distribution (both symmetric and non‐symmetric). Experimentation on different factors that affect modeling, e.g. rolling window size and confidence level, has been conducted.
Findings
In estimating VaR, the results show that models that capture rare events can predict risk more accurately than non‐fat‐tailed models. For ES estimation, the historical model (as expected) and POT method are proved to give more accurate estimations. Gaussian model underestimates ES, while Stable Paretian framework overestimates ES.
Practical implications
Research findings are useful to investors and the way they perceive market risk, risk managers and the way they measure risk and calibrate their models, e.g. shortcomings of VaR, and regulators in central banks.
Originality/value
A comparative, thorough empirical study on a number of financial time series (currencies, indices) that aims to reveal the pros and cons of Gaussian versus fat‐tailed models and Stable Paretian versus EVT, in estimating two popular risk measures (VaR and ES), in the presence of extreme events. The effects of model assumptions on different parameters have also been studied in the paper.
Details
Keywords
Qingfeng Meng, Yifan Zhang, Zhen Li, Weixiang Shi, Jun Wang, Yanhui Sun, Li Xu and Xiangyu Wang
The purpose of this paper is to summarize the current applications of BIM, the integration of related technologies and the tendencies and challenges systematically.
Abstract
Purpose
The purpose of this paper is to summarize the current applications of BIM, the integration of related technologies and the tendencies and challenges systematically.
Design/methodology/approach
Using quantitative and qualitative bibliometric statistical methods, the current mode of interaction between BIM and other related technologies is summarized.
Findings
This paper identified 24 different BIM applications in the life cycle. From two perspectives, the implementation status of BIM applications and integrated technologies are respectively studied. The future industry development framework is drawn comprehensively. We summarized the challenges of BIM applications from the perspectives of management, technology and promotion, and confirmed that most of the challenges come from the two driving factors of promotion and management.
Research limitations/implications
The technical challenges reviewed in this paper are from the collected literature we have extracted, which is only a part of the practical challenges and not comprehensive enough.
Practical implications
We summarized the current mode of interactive use of BIM and sorted out the challenges faced by BIM applications to provide reference for the risks and challenges faced by the future industry.
Originality/value
There is little literature to integrate BIM applications and to establish BIM related challenges and risk frameworks. In this paper, we provide a review of the current implementation level of BIM and the risks and challenges of stakeholders through three aspects of management, technology and promotion.
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Keywords
Yunfei Xing, Wu He, Gaohui Cao and Yuhai Li
COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence…
Abstract
Purpose
COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.
Design/methodology/approach
The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.
Findings
Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.
Research limitations/implications
This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.
Originality/value
This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.