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Forecast quality of historical simulation, variance-covariance approach, and extreme value theory within a 99.9% confidence level
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Cryptocurrencies show some properties that differ from typical financial instruments. For example, dynamic volatility, larger price jumps, and other market participants and their associated characteristics can be observed (Pardalos, Kotsireas, Guo, & Knottenbelt, 2020). Especially high tail risk (Sun, Dedahanov, Shin, & Li, 2021; Corbet, Meegan, La...
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Cryptocurrencies have gained popularity over the past five to six years. Most recently, events like the FTX bankruptcy fueled the interest in regulation. Moreover, it is possible that the FTX event disrupting the cryptocurrency market was a factor in Silicon Valley Bank's failure. While several countries consider regulation, from soft regulation, l...
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... They also show the shifts from smaller cryptocurrencies to larger ones in terms of market capitalization. Opala et al. (2022) study the risk measurement of Ethereum, Bitcoin, and Litecoin using different models such as the extreme value theory. They report that the extreme value theory with the peaks-over-threshold method shows satisfactory backtesting results at a 99% confidence level. ...
... Strong was the push of the Swiss authorities, first of all, provided by the Swiss Financial Market Supervisory Authority (FINMA), the regulator of the Swiss stock market, with a sandbox in 2017 (FINMA, 2017). The Swiss supervisor also shows an interest in informal dialogue with technologically advanced financial companies, for example, crypto assets especially in Switzerland based in Zug, as (Opala et al., 2022) reported from a risk management perspective and a theoretical point of view, this new asset class, with its high tail dependence, some historical drawdowns, and a high degree of volatility, aims to develop and use suitable risk measurement methods that anticipate these observations. Finally, it is essential to create efficient and diversified portfolios and to back them up with appropriate risk capital. ...
Following the Great Financial Crisis, the emergence of digital technologies and the end of banking secrecy (“Swiss say goodbye to banking secrecy”, 2017), financial technology (FinTech) and regulatory technology (RegTech) startups have been offering products in the financial regulatory sector. This trend has increased since the outbreak of COVID-19. Most of the studies on RegTech have focused on reviewing the literature on the macro context and the issues of vast amount of regulation (Arner et al., 2017). Today the academic literature about case studies in regulatory technology is not proposing any solution of cooperation or aggregation of RegTech’s startups in Switzerland. Due to the lack of papers about RegTech in Switzerland, we adopt an approach already used for case studies in the FinTech area (Foster & Heeks, 2013; Burtch et al., 2013) through exploratory investigation through interviews and literature review. The findings of our article have allowed us to analyse the topics and the applications in the RegTech ecosystem provided by startups to Swiss banks. We examine also the aggregations, incubators, and associations active in Switzerland Swisscom, International RegTech Association (IRTA), and F10 (a Swiss FinTech incubator based in Zurich) to examine how they can bring RegTech solutions of the RegTech startups into the banks.
... For example, Tamošaitienė et al. [8] studied Project portfolio construction using the EVT. Opala et al. [9] proposed modeling the tail-dependence of crypto assets with EVT from the perspective of risk management in banks. Embrechts et al. [10] propose a modern EVT at the interface of risk management. ...
The objective of this research was to develop a mathematical and statistical model for long-term prediction. The Extreme Value Theory (EVT) was applied to analyze the appropriate distribution model by using the peak-over-threshold approach with Generalized Pareto Distribution (GPD) to predict daily extreme precipitation and extreme temperatures in eight provinces located in the upper northeastern region of Thailand. Generally, each province has only 1–2 meteorological stations, so spatial analysis cannot be performed comprehensively. Therefore, the reanalysis data were obtained from the NOAA Physical Sciences Laboratory. The precipitation data were used for spatial analysis at the level of 25 square kilometers, which comprises 71 grid points, whereas the temperature data were used for spatial analysis at the level of 50 square kilometers, which includes 19 grid points. According to the analysis results, GPD was appropriate for the goodness of fit test with Kolmogorov-Smirnov Statistics (KS Test) according to the estimation for the return level in the annual return periods of 2 years, 5 years, 10 years, 25 years, 50 years, and 100 years, indicating the areas with daily extreme precipitation and extreme temperatures. The analysis results would be useful for supplementing decision-making in planning to cope with risk areas as well as in effective planning for resources and prevention. Doi: 10.28991/CEJ-2023-09-07-014 Full Text: PDF
