Risk spillover in financial markets based on support vector quantile regression

Abstract

In terms of financial market risk research, with the rapid popularization of non-linear perspectives and the improvement of theoretical reasoning, scholars have slowly broken through the cage of linear ideas and derived new and more practical methods from non-linear perspectives to make up for the shortcomings of traditional research. Based on the support vector classification regression algorithm, this research combines the typical facts and characteristics of financial markets, from the perspective of quantile regression and SVR intelligent technology in computer science, to explore the research method of financial market risk spillover effects from a nonlinear perspective. Moreover, this research integrates statistical research, machine learning and other related research methods, and applies them to the measurement of financial risk spillover effects. The empirical analysis shows that the method proposed in this paper has certain effects, and financial risk analysis can be performed based on the risk spillover effect measurement model constructed in this paper.

Temperature difference and systemic risk: Evidence from LASSO-VAR-DY based on China’s pan-financial market

“…Most current scholars study climate change's impact on a single industry or market, such as banking, stocks, bonds, commodities and foreign exchange markets, real estate, and energy assets [17][18][19]. As for the study of pan-financial systems composed of multiple markets, scholars are currently using the network approach to study the risk contagion among nine sub-markets in traditional financial market, real estate, and commodities market [4], as well as studying the systemic risk spillover effects among China's oil, gold, real estate, and financial sectors, or studying the risk dependence between the banks and the energy sector, the development of the low-carbon transition has led to a more pronounced risk dependence of the banking sector on the new energy sector than on the traditional energy sector [20], but is limited to the study of risk contagion between pan-financial sectors, and there are also scholars who have introduced temperature difference as an exogenous shock and used a network approach to study the impact on the systemic risk of macro-financials (equities, commodities, currencies, and bonds), and have found that climate risk not only affects a single financial market, but also triggers a synergistic movement of risk, exacerbating potential systemic financial risks. Flori et al [21] argued that climate risk negatively affects the financial sector, leading to macroeconomic downturns and that the subsectors in the financial system are closely interconnected and interact with each other, further increasing the risk exposures of the financial sector.…”

“…Climate change has broad and far-reaching implications for the financial system. Temperature changes have exacerbated economic policy uncertainty, and financial generalization caused by the financialization of the real economy, especially the high-carbon sector, has become increasingly prominent [2][3][4][5][6]. The financialization of high-carbon sectors, such as energy and real estate, is prominent [3,7].…”

“…The financialization of high-carbon sectors, such as energy and real estate, is prominent [3,7]. Xie et al [4] defined nine submarkets in traditional financial market, real estate, and commodity market as pan-financial market. They investigated the extreme risk spillovers and evolutionary patterns in pan-financial market.…”

“…Against this backdrop, the Central Committee of the Communist Party of China (CPC) has made a significant decision to respond to global warming, which means that it will "strive to achieve carbon peaking by 2030 and carbon neutrality by 2060", which will lead to extensive and profound economic and social systemic changes, and global climate change as well as institutional and policy arrangements to address extreme climate change are bound to impact China's High-carbon sectors. Based on the above analysis, we believe that it is necessary to re-examine the impact of temperature difference on systemic financial risk: first, the financial generalization problem in the context of temperature difference has become increasingly prominent, the boundaries of the financial sector have gradually blurred and expanded, and the financial system has gradually generalized from the traditional sectors of banking, insurance, and securities to a pan-financial sector that encompasses banking, insurance, securities, real estate, traditional energy, and new and emerging energy sources [3,4]; secondly, due to the expansion of the number of submarkets in the financial system, the path of systemic risk contagion caused by temperature difference changes will become more complex. On the contrary, few scholars have conducted in-depth and systematic investigations on the systemic risk of temperature change on pan-financial market (including the high-carbon sectors where the financialization phenomenon is very prominent, such as the traditional energy, new energy, and real estate sectors) in the existing researches.…”

Dynamic Risk Spillovers in Non-Ferrous Metal Future Markets During COVID-19: A Frequency Domain Analysis

“…In addition to GARCH model methods, there are also studies on risk spillovers based on Granger causality test, VAR model and Copula. Xie, et al [6] used GED based ARMA-(T) GARCH-VAR model and Granger causality test to investigate the downside and upside extreme risk spillover effects of nine submarkets in China's pan-financial market during different periods. Flavin and Lagoa-Varela [7] found there was a significant increase in correlation between stock and bond markets during financial crises.…”