Identifying the Asymmetric Channel of Crude Oil Risk Pass-Through to Macro Economy: Based on Crude Oil Attributes

The impact channel of crude oil market risk on the macroeconomy is highly related to oil attributes. This paper uses a stepwise test method with dummy variables to identify the channel effect of commodity market risk as well as financial market risk and explore the characteristics of the channel effect in different periods dominated by different oil attributes. Furthermore, this paper investigates the asymmetric characteristics of the channel effect under the condition of crude oil returns heterogeneity. The empirical results show that: First, commodity market risk, as well as financial market risk plays a channel role in the impact of crude oil market risk on the macroeconomic operation. Second, there is a significant difference in the ability of the commodity market and financial market to cope with shocks of crude oil market risk in periods dominated by different attributes. During the period dominated by the commodity attribute of oil, both commodity market and financial market play the role of “risk buffer”; during the period dominated by dual attributes of oil, the commodity market risk plays the role of “risk buffer”, while the financial market risk plays the role of “magnifier” of the crude oil market risk. Third, the channel effect pattern and degree of commodity market risk and financial market risk are significantly asymmetric.

Analyzing the Internet financial market risk management using data mining and deep learning methods

PurposeTo identify and analyze the occurrence of Internet financial market risk, data mining technology is combined with deep learning to process and analyze. The market risk management of the Internet is to improve the management level of Internet financial risk, improve the policy of Internet financial supervision and promote the healthy development of Internet finance.Design/methodology/approachIn this exploration, data mining technology is combined with deep learning to mine the Internet financial data, warn the potential risks in the market and provide targeted risk management measures. Therefore, in this article, to improve the application ability of data mining in dealing with Internet financial risk management, the radial basis function (RBF) neural network algorithm optimized by ant colony optimization (ACO) is proposed.FindingsThe results show that the actual error of the ACO optimized RBF neural network is 0.249, which is 0.149 different from the target error, indicating that the optimized algorithm can make the calculation results more accurate. The fitting results of the RBF neural network and ACO optimized RBF neural network for nonlinear function are compared. Compared with the performance of other algorithms, the error of ACO optimized RBF neural network is 0.249, the running time is 2.212 s, and the number of iterations is 36, which is far less than the actual results of the other two algorithms.Originality/valueThe optimized algorithm has a better spatial mapping and generalization ability and can get higher accuracy in short-term training. Therefore, the ACO optimized RBF neural network algorithm designed in this exploration has a high accuracy for the prediction of Internet financial market risk.

A Sentiment-based Risk Indicator for the Mexican Financial Sector

We apply text analysis to Twitter messages in Spanish to build a sentiment- based risk index for the financial sector in Mexico. We classify a sample of tweets for the period 2006-2019 to identify messages in response to positive or negative shocks to the Mexican financial sector. We use a voting classifier to aggregate three different classifiers: one based on word polarities from a pre-defined dictionary; one based on a support vector machine; and one based on neural networks. Next, we compare our Twitter sentiment index with existing indicators of financial stress. We find that this novel index captures the impact of sources of financial stress not explicitly encompassed in quantitative risk measures. Finally, we show that a shock in our Twitter sentiment index correlates positively with an increase in financial market risk, stock market volatility, sovereign risk, and foreign exchange rate volatility

Improving on Defaults: Helping Pension Participants Manage Financial Market Risk in Target Date Funds

The central issue of this paper is analysis and resulting proposals to help unsophisticated pension participants achieve pension portfolios that match their level of risk aversion when there is a large amount of unexplained heterogeneity in risk aversion. Target date funds are commonly used as the default investment in defined contribution plans in the U.S., UK and other countries. These funds recognize that individuals usually should hold less risky investment portfolios as their expected retirement date approaches because their ability to bear financial market risk declines as the time horizon decreases. However, these funds do not account for differences in risk aversion among people with the same target date. Empirical studies find large amounts of unexplained heterogeneity in risk aversion. Target date funds cannot deal with this issue simply by sorting people into demographic groupings, other than age, that are known to affect risk aversion, such as gender. Financial education can help people do a better job of managing financial market risk in their pension portfolios, but we argue that it is unreasonable to expect millions of pension participants to attain advanced levels of financial literacy. This paper considers three innovations in target date funds that can help individual pension participants do a better job of managing financial market risk. The analysis can be applied to other situations where defaults are used for investing pension participants’ portfolios. The paper suggests new lines of research relating to individual differences in risk aversion.

Research on Financial Market Risk Based on GARCH-M Model

Since 1970, with the gradual acceleration of economic globalization and the rapid development of information technology, the financial market has become increasingly unstable. Therefore, we must enhance our competitiveness in the financial market, enhance our ability to resist risks, and master effective measures such as measuring risks. In this paper, GARCH-M model and VAR method are used to study the value at risk of financial market and make an empirical analysis. Firstly, the VAR value calculation method based on GARCH-M model under generalized error distribution is given. Secondly, the closing price of Shanghai Stock Exchange Index is selected as sample data, and Eviews software is used to analyze its characteristics. The results show that the logarithmic yield series of the closing price of Shanghai Stock Exchange Index is not normally distributed, and the series has fluctuation aggregation effect, autocorrelation effect and heteroscedasticity effect. Finally, GARCH-M model is established, and VAR estimates at 95% and 99% confidence levels are calculated and tested. The results show that GARCH-M(1,1) model is more suitable for estimating the risk of logarithmic return rate of closing price of Shanghai Composite Index.

Risk spillover in financial markets based on support vector quantile regression

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.

Financial Market Risk Perceptions and the Macroeconomy*

We provide evidence that financial market risk perceptions are important drivers of economic fluctuations. We introduce a novel measure of risk perceptions: the price of volatile stocks (PVSt), defined as the book-to-market ratio of low-volatility stocks minus the book-to-market ratio of high-volatility stocks. PVSt is high when perceived risk directly measured from surveys and option prices is low. Using our measure, we show that high perceived risk is associated with low risk-free interest rates, a high cost of capital for risky firms, and future declines in output and real investment. Perceived risk as measured by PVSt falls after positive macroeconomic news. These declines are predictably followed by upward revisions in perceived risk, indicating that fluctuations in investor risk perceptions are not fully rational.

Insurance of economic security of registrars on the financial market: risk management techniques and allocation of resources (2001-2018). Part 2

The subject of this research is the problems of allocation of resources associated with risk management of the registry of securities holders. The object of this research is the activity of the registrars on risk management. The author examines the peculiarities of risk management techniques of registrars, and dynamics of their application for the period from 2001 to 2018. The factors that contribute to selection of one or another technique are analyzed. Special attention is given to the market insurance and reservation as management techniques, as well as to price and institutional factors that affect the choice. The article employs the methods of retrospective research of economic indicators in accordance with sampling that covers Russia’s largest registrars (in 2001-2018), as well as methods of correlation and regression analysis. As a result of the conducted research, the author determines stages in the dynamics of using the key risk management techniques. The factors of dynamics are related to the parameters of risk and relative cost of implementation of particular techniques. The author's contribution consists in the empirical research on the basis of public data of factors and trends in allocation of resources associated with risk management of registrars on the financial market for the period from 2001 to 2018. The novelty of this work lies in outlining the factors and trends in allocation of resources related to risk management of registrars in fort the period from 2001 to2018, considering public data on the activity of Russia’s largest registrars, content analysis of publications, and case law of the  computer-based legal research system “ConsultantPlus”.