A5063247720- Market Dynamics and Volatility
- Complex Systems and Time Series Analysis
- Financial Risk and Volatility Modeling
- Stock Market Forecasting Methods
- Corporate Finance and Governance
- Monetary Policy and Economic Impact
- Complex Network Analysis Techniques
- Financial Markets and Investment Strategies
- Financial Distress and Bankruptcy Prediction
- Stochastic processes and financial applications
- Banking stability, regulation, efficiency
- Risk Management in Financial Firms
- Insurance and Financial Risk Management
- Energy, Environment, Economic Growth
- Capital Investment and Risk Analysis
- Theoretical and Computational Physics
- Evaluation and Optimization Models
- Credit Risk and Financial Regulations
- Energy Load and Power Forecasting
- Fault Detection and Control Systems
- Time Series Analysis and Forecasting
- Working Capital and Financial Performance
- Economic theories and models
- Imbalanced Data Classification Techniques
- Image and Signal Denoising Methods
Hunan University
2015-2024
Tongji University
2024
College of Business Administration
2006-2017
Hunan University of Finance and Economics
2016-2017
Zen-Noh (Japan)
2007
Xiaomi (China)
2000
Using the CAViaR tool to estimate value-at-risk (VaR) and Granger causality risk test quantify extreme spillovers, we propose an spillover network for analysing interconnectedness across financial institutions. We construct networks at 1% 5% levels (which denote VaR networks) based on daily returns of 84 publicly listed institutions from four sectors—banks, diversified financials, insurance real estate—during period 2006–2015. find that have a time-lag effect. Both static dynamic show...
Based on daily data about Bitcoin and six other major financial assets (stocks, commodity futures (commodities), gold, foreign exchange (FX), monetary assets, bonds) in China from 2013 to 2017, we use a VAR-GARCH-BEKK model investigate mean volatility spillover effects between explore whether can be used either as hedging asset or safe haven. Our empirical results show that (i) only the market, i.e., Shanghai Interbank Offered Rate (SHIIBOR) has effect (ii) monetary, bond markets have...
We propose multilayer information spillover networks to measure the interconnectedness of financial institutions by comprehensively considering mean layer, volatility layer and extreme risk based on Granger causality tests in mean, risk. Using daily returns 24 Chinese publicly listed during 2008–2018, we construct static dynamic analyze different layers' similarity, uniqueness overlap. Some unique features, which could not be detected a particular single-layer, are found networks. Dynamic...
We investigate the statistical properties of foreign exchange (FX) network at different time scales by two approaches, namely methods detrended cross-correlation coefficient (DCCA coefficient) and minimum spanning tree (MST). The daily FX rates 44 major currencies in period 2007–2012 are chosen as empirical data. Based on analysis coefficients, we find that coefficients market fat-tailed. By examining three MSTs special (i.e., minimum, medium, maximum scales), come to some conclusions: USD...
Based on logistic regression (LR) and artificial neural network (ANN) methods, we construct an LR model, ANN model three types of a two-stage hybrid model. The is integrated by the approaches. We predict credit risk China’s small medium-sized enterprises (SMEs) for financial institutions (FIs) in supply chain financing (SCF) applying above models. In empirical analysis, quarterly non-financial data 77 listed SMEs 11 core (CEs) period 2012–2013 are chosen as samples. results show that: (i)...