---
title: Volatility Predictions in Credit Markets
url: https://www.ml-quant.com/papers/ssrn/5130271/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5130271
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5130271
featured: 2025-02-19
citations: unknown
topic: Derivatives & Volatility
---


# Volatility Predictions in Credit Markets

The research presents a predictive causality network among corporate bond issuers to aid proactive portfolio management and diversification analysis.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5130271
- Identifier: SSRN 5130271
- Released: 2025-02-10
- First featured: Quant Letter No. 85 (2025-02-19): https://www.ml-quant.com/issues/2025-02-19/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [APC Framework for Profit Modeling](https://www.ml-quant.com/papers/repec/gam-jmathe-v-12-y-2024-i-10-p-1427-d-1389720/): Age–period–cohort models can enhance credit risk modeling across a company, improving underwriting and enabling profit and volatility predictions at the account level.
- [Forecasting realized volatility in the stock market: a path-dependent perspective](https://www.ml-quant.com/papers/arxiv/2503.00851/): A new volatility forecasting model, combining the heterogeneous autoregressive model with path-dependent volatility models, shows improved forecasting accuracy in the Chinese stock market.
- [Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2504.09380/): The paper proposes a new GARCH-GRU model for financial volatility forecasting, showing better computational efficiency and forecasting accuracy than other models.
- [A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm](https://www.ml-quant.com/papers/arxiv/2412.07223/): The article presents a unique AI model for predicting future volatility in emerging stock markets, showing high accuracy and low error rates.
- [Modeling Regime Structure and Informational Drivers of Stock Market Volatility via the Financial Chaos Index](https://www.ml-quant.com/papers/arxiv/2504.18958/): The research uses the Financial Chaos Index to study stock market volatility, identifying three market types and using sentiment predictors for volatility forecasting.
- [Foundation Time-Series AI Model for Realized Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2505.11163/): The study finds that the TimesFM model, with incremental fine-tuning, is effective for volatility forecasting in financial risk management, outperforming traditional models.
