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Register a new userBlack-box model risk in finance
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Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and quantified. In this sub-chapter, we will focus on a well studied application of machine learning techniques, to pricing and hedging of financial options. Our aim will be to highlight the various sources of risk that the introduction of machine learning emphasises or de-emphasises, and the possible risk mitigation and management strategies that are available.
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Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. One year ago the total value locked in DeFi systems was approximately 700m USD, now, as of April 2021, it stands at around 51bn USD. The frenetic evolution of the ecosystem makes it challenging for newcomers to gain an understanding of its basic features. In this Systematization of Knowledge (SoK), we delineate the DeFi ecosystem along its principal axes. First, we provide an overview of the DeFi primitives. Second, we classify DeFi protocols according to the type of operation they provide. We then go on to consider in detail the technical and economic security of DeFi protocols, drawing particular attention to the issues that emerge specifically in the DeFi setting. Finally, we outline the open research challenges in the ecosystem.
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The COVID-19 Pandemic has been described as the global challenge of our time, an enormous human tragedy with dramatic economic impacts. This paper describes the response and expected recovery process for Western Australia, where a rapid and effective response was implemented. This has enabled an early transition into an expected recovery both in health and economic terms. The positive lessons learned from this experience are documented as they emerge in order to support other states and nations as they address this issue globally in the near-term and consider enduring improvements for the longer term. While the authors have personal experience in the WA context, wider observations across Australia and selected international benchmarks are also included. Key lessons include the importance of good health advice in Australias interest; timely, synchronized and aligned action at all levels of government; a program of well communicated, aligned health and economic measures which support all in society allowing a very high level of appropriate community behaviour, ensuring the health system was not overloaded; innovation in telehealth, testing, pandemic modelling, and integrated operations which also allowed essential industries to continue; and strong border and travel controls with highly effective isolation preventing community spread, ultimately enabling rapid elimination of the disease from the hospital system. In combination, these demonstrate that in the case of Western Australia the result of first eliminating the disease from the community, and then reopening the economy progressively at a strong pace, has enabled a world leading outcome in both in health and economic terms. The lessons from this experience are widely applicable, shareable both as supporting service to other regions and through knowledge transfer.
This paper investigates the impact of economic policy uncertainty (EPU) on the crash risk of US stock market during the COVID-19 pandemic. To this end, we use the GARCH-S (GARCH with skewness) model to estimate daily skewness as a proxy for the stock market crash risk. The empirical results show the significantly negative correlation between EPU and stock market crash risk, indicating the aggravation of EPU increase the crash risk. Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the pandemic.
After the 2007/2008 financial crisis, the UK government decided that a change in regulation was required to amend the poor control of financial markets. The Financial Services Act 2012 was developed as a result in order to give more control and authority to the regulators of financial markets. Thus, the Financial Conduct Authority (FCA) succeeded the Financial Services Authority (FSA). An area requiring an improvement in regulation was insider trading. Our study examines the effectiveness of the FCA in its duty of regulating insider trading through utilising the event study methodology to assess abnormal returns in the run-up to the first announcement of mergers. Samples of abnormal returns are examined on periods, under regulation either by the FSA or by the FCA. Practically, stock price data on the London Stock Exchange from 2008-2012 and 2015-2019 is investigated. The results from this study determine that abnormal returns are reduced after the implementation of the Financial Services Act 2012; prices are also found to be noisier in the period before the 2012 Act. Insignificant abnormal returns are found in the run-up to the first announcement of mergers in the 2015-2019 period. This concludes that the FCA is efficient in regulating insider trading.
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