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Regional Private Financing Risk Index Model Based on Private Financing Big Data.

Jingfeng Zhao1, Bo Li1

  • 1College of Management and Economic, North China University of Water Resources and Electric Power, Zhengzhou, China.

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|April 28, 2022
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Summary

China's private financing, crucial for SMEs, faces risks. This study models these risks using the Yantai Index, revealing asymmetric responses to economic shocks and validating an early warning system.

Keywords:
financing riskmarket's effectivenessprincipal component analysisprivate financingprivate lending

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Area of Science:

  • Economics
  • Financial Risk Management
  • China's Financial Markets

Background:

  • Rapid economic development in China has spurred growth in private financing, offering flexible solutions for Small and Medium-sized Enterprises (SMEs).
  • Decentralization of economic regulation and legal support has further facilitated the expansion of private financing.
  • The effectiveness of local financial markets is enhanced by SMEs addressing their financing needs through private channels.

Purpose of the Study:

  • To construct a private financial risk index model incorporating interest rate, scale, and credit risk.
  • To develop an early warning system for private financing risks using macro, micro, and stability indicators.
  • To analyze the response of private lending to macroeconomic shocks and its information value.

Main Methods:

  • Construction of a private financial risk index model based on the "Yantai Private Financing Interest Rate Index."
  • Screening of early warning system indicators from macro, micro, and stability dimensions with subjective and objective adjustment coefficients.
  • Application of Principal Component Analysis and Bayesian Vector Autoregressive models for data processing and analysis.

Main Results:

  • The study found significant asymmetry in private lending's response to macroeconomic shocks: inflation impacts higher rates more, while monetary policy affects lower rates more.
  • Interest rates decrease as the lending term increases, with shorter terms (1-month) posing higher risks than 3-month or 6-month terms.
  • The developed model can calculate comprehensive evaluation values and fluctuations, establishing a risk range for the early warning system.

Conclusions:

  • The private financial risk index model and early warning system are feasible and effective for monitoring risks in China's private financing sector.
  • Understanding the asymmetric responses to macroeconomic shocks is crucial for policymakers and financial institutions.
  • The term structure of private lending rates provides valuable insights into economic conditions and associated risks.