Abstract
Following the speeding up of a process of financial globalization, the risks faced by financial markets have become more complex and diversified. Correlated patterns among financial assets exhibit characteristics of nonlinearity, asymmetry, and tail correlation. The original linear correlation analysis method is no longer suitable, but relevant information describing financial risks. In order to confirm whether an asset is safe, the key is to study and master its volatility, and this is based on our mastery of volatility measurement skills. This article is based on smart sensor big data security analysis and Bayesian analysis. The risk measurement of financial assets based on the empirical probability model is studied. The GARCH-t(1,1) model is selected according to the Akaike information criterion (AIC) after the generalized autoregressive conditional heteroskedasticity (GARCH) model is established by the EViews software. According to the results of probability integral transformation, a series of correlation coefficients and degrees of freedom oft-copula are obtained by the maximum likelihood estimation method. This paper uses the risk-adjusted return on capital (RAROC) method to evaluate the risk performance of financial assets. Financial institutions can only retain and absorb the financial market risks that cannot be avoided and transferred. The edge user node sends the service request to the edge server node. The edge server uses the model proposed in this paper to evaluate the user’s trust and selects the corresponding service level according to the trust level corresponding to the calculated credibility results. The data show that the edge calculation takes 0.2581 seconds, while the linear search takes about 64 seconds. The results show that intelligent edge computing improves the accuracy and efficiency of financial asset risk measurement.
Highlights
For nearly half a century, the world’s financial and economic markets have developed rapidly, and the basic characteristics of financial and economic markets have been gradually replaced by economic globalization and financial integration
The Bayesian posterior method is a method that integrates the prior information about the unknown parameters with the sample information, obtains the posterior information according to the Bayesian formula, and infers the unknown parameters according to the posterior information
Risk source and failure of control measures in advance are mapped to root event, intermediate nodes are built from root event to risk event, the leaf node is mapped from risk event, safety barrier node is mapped from postaccident control measures, and the result node is mapped from accident consequence to establish the risk Bayesian network of evaluation object
Summary
For nearly half a century, the world’s financial and economic markets have developed rapidly, and the basic characteristics of financial and economic markets have been gradually replaced by economic globalization and financial integration. Liu et al believe that in recent years, with the explosive development of smart cities, green energy management systems have received extensive research, with their focus on engineering web-based power generation systems using edge infrastructure including deep reinforcement learning. Xu et al believe that edge caches are vulnerable to cache pollution attacks (CPAttacks), leading to interruption of content delivery To solve this problem, they proposed a CPAttack-detection scheme based on the hidden Markov model (HMM). Luo et al believe that vehicle edge computing (VEC) integrates mobile edge computing (MEC) into the vehicle network, which can provide more functions to execute resourceconstrained applications and reduce the waiting time for connected vehicles They proposed a dual importance (DI) evaluation method to reflect the relationship between vehicle priority (PoV) and content priority (PoC). When modeling with less sample data, prior information and domain knowledge play an important role
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