Financial Risk Management

Volatility Forecasting in Global Financial Markets Using TimeMixer ArXiv ID: 2410.09062 “View on arXiv” Authors: Unknown Abstract Predicting volatility in financial markets, including stocks, index ETFs, foreign exchange, and cryptocurrencies, remains a challenging task due to the inherent complexity and non-linear dynamics of these time series. In this study, I apply TimeMixer, a state-of-the-art time series forecasting model, to predict the volatility of global financial assets. TimeMixer utilizes a multiscale-mixing approach that effectively captures both short-term and long-term temporal patterns by analyzing data across different scales. My empirical results reveal that while TimeMixer performs exceptionally well in short-term volatility forecasting, its accuracy diminishes for longer-term predictions, particularly in highly volatile markets. These findings highlight TimeMixer’s strength in capturing short-term volatility, making it highly suitable for practical applications in financial risk management, where precise short-term forecasts are critical. However, the model’s limitations in long-term forecasting point to potential areas for further refinement. ...

Study on the Identification of Financial Risk Path Under the Digital Transformation of Enterprise Based on DEMATEL-ISM-MICMAC ArXiv ID: 2305.04216 “View on arXiv” Authors: Unknown Abstract Digital transformation challenges financial management while reducing costs and increasing efficiency for enterprises in various countries. Identifying the transmission paths of enterprise financial risks in the context of digital transformation is an urgent problem to be solved. This paper constructs a system of influencing factors of corporate financial risks in the new era through literature research. It proposes a path identification method of financial risks in the context of the digital transformation of enterprises based on DEMATEL-ISM-MICMAC. This paper explores the intrinsic association among the influencing factors of corporate financial risks, identifies the key influencing factors, sorts out the hierarchical structure of the influencing factor system, and analyses the dependency and driving relationships among the factors in this system. The results show that: (1) The political and economic environment being not optimistic will limit the enterprise’s operating ability, thus directly leading to the change of the enterprise’s asset and liability structure and working capital stock. (2) The enterprise’s unreasonable talent training and incentive mechanism will limit the enterprise’s technological innovation ability and cause a shortage of digitally literate financial talents, which eventually leads to the vulnerability of the enterprise’s financial management. This study provides a theoretical reference for enterprises to develop risk management strategies and ideas for future academic research in digital finance. ...

The Oxford Olympics Study 2016: Cost and Cost Overrun at the Games ArXiv ID: ssrn-2804554 “View on arXiv” Authors: Unknown Abstract Given that Olympic Games held over the past decade each have cost USD 8.9 billion on average, the size and financial risks of the Games warrant study. The objec Keywords: Major Event Financing, Cost-Benefit Analysis, Public Infrastructure, Revenue Bonds, Financial Risk Management, Public Finance/Infrastructure Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper’s math is limited to descriptive statistics (averages, percentages) without advanced modeling. While it uses historical data, it lacks the implementation-heavy backtesting or detailed datasets typical of high-empirical-rigor finance research, focusing instead on broad phenomenological analysis. flowchart TD A["Research Goal<br>Quantify Olympic cost &amp; overrun"] --> B["Methodology<br>Retrospective cost analysis"] B --> C["Data Sources<br>Olympic Games budgets 1960-2016"] C --> D["Computation<br>Mean cost &amp; overrun calculations"] D --> E["Key Findings<br>Avg cost: USD 8.9B<br>Avg overrun: 156%"]