Enterprise AI Research Analysis
A Comparative Study on CSI Green Bond Index Forecasting Based on LSTM-GARCH and ARMA-GARCH Models
Authors: Jiang Zhan, Yurun Fang, Xinchun Zhou, Siqi Chen
Affiliations: School of Finance, Guangdong University of Finance and Economics, Guangzhou, Guangdong, China. Siqi Chen is also affiliated with Graduate School, Guangdong University of Finance and Economics.
This study investigates the volatility characteristics and forecasting of the CSI Green Bond Index using daily data from January 2016 to December 2024.We employ and compare the performance of two models: the Long Short-Term Memory-Generalized Autoregres- sive Conditional Heteroskedasticity model (LSTM-GARCH) and the Autoregressive Moving Average-Generalized Autoregres- sive Conditional Heteroskedasticity model (ARMA-GARCH). The empir- ical results reveal significant volatility clustering and asymmetry within the green bond market. Specifically, the LSTM-GARCH model demonstrates superior performance in short-term forecast- ing, whereas the ARMA-GARCH model exhibits a stronger capabil- ity in capturing medium-to-long-term trends. The findings provide a quantitative basis for risk management among green bond in- vestors and the formulation of regulatory policies.
Executive Impact: Driving Strategic Decisions with Advanced Forecasting
This research provides critical insights into green bond market dynamics, offering quantitative tools for enhanced risk management and policy formulation. Our AI models deliver superior short-term volatility predictions and robust long-term trend analysis.
0%
Green Bond Index Scale Growth (2016-2024)
0%
Reduction in 1-Day Prediction Error (LSTM-GARCH)
¥0T
Current Green Bond Market Scale (RMB)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Model Performance
Key Findings
Methodology
Model Performance Comparison: LSTM-GARCH vs. ARMA-GARCH
This table summarizes the in-sample goodness-of-fit and out-of-sample forecasting metrics for both LSTM-GARCH and ARMA-GARCH models, highlighting their strengths in different forecasting horizons.
| Metric | LSTM-GARCH | ARMA-GARCH | Advantage |
|---|---|---|---|
| In-Sample MSE (lower is better) | 0.000128 | 0.000152 | LSTM-GARCH |
| In-Sample R² (higher is better) | 0.23 | 0.18 | LSTM-GARCH |
| 1-Day Forecast MSE (lower is better) | 0.0098 | 0.0115 | LSTM-GARCH (14.8% reduction) |
| 30-Day Forecast MSE (lower is better) | 0.0357 | 0.0382 | LSTM-GARCH (6.5% reduction) |
Key Finding: LSTM-GARCH demonstrates superior performance in short-term forecasting, providing a 14.8% reduction in 1-day prediction error compared to ARMA-GARCH. However, ARMA-GARCH exhibits stronger capabilities in capturing medium-to-long-term trends.
Key Findings: Green Bond Market Volatility and Predictive Insights
Understanding the core volatility characteristics and empirical discrepancies is crucial for effective risk management and policy making in the green finance sector.
Short-Term Superiority
LSTM-GARCH significantly outperforms in short-term (1-10 days) volatility forecasting.
Bridging Theory-Empiricism Gaps in Green Finance
This module highlights observed discrepancies between theoretical expectations and empirical results, offering critical lessons for market participants and regulators.
- Weaker GRA Impact: The coefficient for Green Certification Level (GRA) was only 0.087, weaker than expected. This is attributed to inconsistent domestic certification standards and instances of fund misappropriation, undermining market trust.
- Short Policy Impact Duration: Significant effects of the Policy variable in the GARCH equation decayed rapidly after 5 trading days. This suggests high policy transparency and efficient market digestion of new information.
- Non-Significant CI Relationship: Carbon Intensity (CI) did not significantly correlate with volatility (p>0.1), due to limited domestic carbon market coverage (mainly power sector) not fully reflecting environmental risks across all sectors like transportation and construction.
- Implication: These gaps indicate that while models can forecast volatility, fundamental market infrastructure and carbon pricing mechanisms need improvement to align green attributes with actual market value.
Hybrid Model Construction Process
This flowchart illustrates the integrated steps involved in developing the LSTM-GARCH hybrid model for robust volatility forecasting of the CSI Green Bond Index.
Enterprise Process Flow
Data Preprocessing & Stationarity Test
→
ARMA-GARCH Model Selection (ARMA(1,3) & GARCH(1,1))
→
LSTM Network Architecture Design & Training
→
Combining LSTM & GARCH for Volatility Modeling
→
Model Prediction & Comparative Evaluation
Calculate Your Potential AI-Driven ROI
Estimate the significant financial and operational benefits your enterprise could achieve by implementing advanced AI forecasting models, tailored to your specific industry and operational scale.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A phased approach to integrate advanced AI forecasting into your enterprise, ensuring seamless transition and maximized impact.
Phase 1: Discovery & Strategy
Initial consultation to understand your unique business challenges, data infrastructure, and strategic objectives. Define KPIs and success metrics.
Phase 2: Data Integration & Model Prototyping
Securely integrate your proprietary data, develop custom AI models (e.g., LSTM-GARCH adaptations), and demonstrate initial prototypes with your datasets.
Phase 3: Validation & Refinement
Extensive testing and validation of model performance against historical and real-time data. Iterative refinement based on your feedback and performance benchmarks.
Phase 4: Deployment & Training
Seamless deployment of the validated AI solution into your existing systems. Comprehensive training for your teams to ensure optimal utilization and adoption.
Phase 5: Continuous Optimization & Support
Ongoing monitoring, performance optimization, and dedicated support to adapt to evolving market conditions and business needs, ensuring sustained ROI.
Ready to Transform Your Enterprise with AI?
Schedule a personalized consultation with our AI strategists to explore how advanced forecasting models can drive innovation and efficiency in your organization.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat