Research on Systemic Financial Risk Measurement Based on HMM and Text Mining: A Case of China Financial Market

The paper considered the sensitivity of unstructured network data to external shocks in the financial system, based on HMM applied in the traditional financial indicator system to construct the new composite index. We integrated economic statistical structure data and internet information, to capture the internal correlation and external shocks to financial markets. There appeared to be some evidence that the new index was superior at measuring the systemic financial risk. In addition, according to the new composite index we constructed, China’s systemic financial risk was at the medium high level. It is an important task to prevent the systemic financial risk and maintain the stability of macro-economy.

[1]  Alfred Lehar.  Measuring Systemic Risk: A Risk Management Approach , 2005 .

[2]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[3]  Charles Goodhart,et al.  Banking Stability Measures , 2009, SSRN Electronic Journal.

[4]  Lubomir T. Chitkushev,et al.  Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers , 2020, IEEE Access.

[5]  Arnaud Doucet,et al.  Stochastic sampling algorithms for state estimation of jump Markov linear systems , 2000, IEEE Trans. Autom. Control..

[6]  K. Ho,et al.  How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches , 2013 .

[7]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[8]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[9]  S. Schwarcz,et al.  Systemic Risk , 2008 .

[10]  A. Lo,et al.  Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors , 2011 .

[11]  Craig S. Hakkio,et al.  Financial stress: what is it, how can it be measured, and why does it matter? , 2009 .

[12]  Hans Degryse,et al.  Interbank Exposures: An Empirical Examination of Contagion Risk in the Belgian Banking System , 2012 .

[13]  Ying Liu,et al.  Measuring financial stress in a developed country: An application to Canada , 2006 .

[14]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[15]  Chang-Ping Yu,et al.  Triclosan: A review on systematic risk assessment and control from the perspective of substance flow analysis. , 2016, The Science of the total environment.

[16]  Jitendra Tugnait,et al.  Adaptive estimation and identification for discrete systems with Markov jump parameters , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[17]  D. Klingebiel,et al.  Bank Insolvencies: Cross-Country Experience , 1996 .

[18]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[19]  John F. O. Bilson.  Recent Developments in Monetary Models of Exchange Rate Determination , 1979 .

[20]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[21]  Mathias Drehmann,et al.  Measuring the Systemic Importance of Interconnected Banks , 2011 .

[22]  William J. Baumol,et al.  An Expected Gain-Confidence Limit Criterion for Portfolio Selection , 1963 .

[23]  Javier Garcia-Frías,et al.  Turbo decoding of hidden Markov sources with unknown parameters , 1998, Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225).

[24]  Philippe Artzner.  Application of Coherent Risk Measures to Capital Requirements in Insurance , 1999 .

[25]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[26]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .