Financial Systematic Risk Research Articles

Research on prediction of China’s financial systematic risk based on the hybrid model

An important feature of the outbreak of systemic financial risk is that the linkage and contagion of risk amongst the various sub-markets of the financial system have increased significantly. In addition, research on the prediction of systemic financial risk plays a significant role in the sustainable development of the financial market. Therefore, this paper takes China’s financial market as its research object, considers the risks co-activity among major financial sub-markets, and constructs a financial composite indicator of systemic stress (CISS) for China, describing its financial systemic stress based on 12 basic indicators selected from the money market, bond market, stock market, and foreign exchange market. Furthermore, drawing on the decomposition and integration technology in the TEI@I complex system research methodology, this paper introduces advanced variational mode decomposition (VMD) technology and extreme learning machine (ELM) algorithms, constructing the VMD-DE-ELM hybrid model to predict the systemic risk of China’s financial market. According to eRMSE, eMAE, and eMAPE, the prediction model’s multistep-ahead forecasting effect is evaluated. The empirical results show that the China’s financial CISS constructed in this paper can effectively identify all kinds of risk events in the sample range. The results of a robustness test show that the overall trend of China’s financial CISS and its ability to identify risk events are not affected by parameter selection and have good robustness. In addition, compared with the benchmark model, the VMD-DE-ELM hybrid model constructed in this paper shows superior predictive ability for systemic financial risk.

Effects of oil price fall on the betas in the Unconventional Oil & Gas Industry

We examine the consequences of the oil prices movements started in July 2014 on the financial systematic risk – proxied by Betas – of firms operating in the Unconventional Oil & Gas Industry, compared to that of the Conventional Oil & Gas players. The analysis is developed using two cross section regressions, performed before and after the 2014 oil price drop respectively. The results look coherent with the reasonable belief that a sharp and sudden decrease in the oil price can generally lead to higher Betas in the Oil & Gas Industry. Interestingly, it emerged that the market tends to attribute an additional risk to unconventional firms and, analyzing the regression coefficients evolution, it appears that this circumstance has been substantially amplified by the 2014 oil price shock. To our knowledge this is the first paper covering the topic treated.