---
title: Reverse Causality: Credit Markets & Macroeconomic Shocks
url: https://www.ml-quant.com/papers/ssrn/4511662/
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 4511662
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4511662
featured: 2023-07-19
citations: unknown
topic: Risk, Credit & Banking
---


# Reverse Causality: Credit Markets & Macroeconomic Shocks

Credit Markets & Macroeconomic Shocks: Corporate bond credit spreads significantly react to macroeconomic shocks, with credit risk premia and leverage playing crucial roles, enhancing our understanding of credit markets and the macroeconomy.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4511662
- Identifier: SSRN 4511662
- Released: 2020-10-08
- First featured: Quant Letter No. 8 (2023-07-19): https://www.ml-quant.com/issues/2023-07-19/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Risk, Credit & Banking

## Related

- [Machine Learning-Based Variable Selection for Clustered Credit Risk Modeling](https://www.ml-quant.com/papers/ssrn/4506537/): The piece proposes a machine learning-based method for selecting variables in clustered credit risk modeling, using the most influential risk drivers as clustering variables.
- [Double free boundary problem for defaultable corporate bond with credit rating migration risks and their asymptotic behaviors](https://www.ml-quant.com/papers/arxiv/2301.10898/): The paper presents a pricing model for a corporate bond with credit rating migration risk, proving the solution's existence, uniqueness, and regularity.
- [DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data](https://www.ml-quant.com/papers/arxiv/2308.03704/): Credit Risk Deep Learning Framework: DeRisk, a deep learning framework for predicting credit risk using real-world financial data, has been shown to outperform traditional statistical learning methods.
- [Study on Intelligent Forecasting of Credit Bond Default Risk](https://www.ml-quant.com/papers/arxiv/2305.12142/): Intelligent forecasting framework for default risk in China's bond market using ConvLSTM neural network.
- [Differential quantile-based sensitivity in discontinuous models](https://www.ml-quant.com/papers/arxiv/2310.06151/): Discontinuous Model Sensitivity Analysis: The article discusses a framework for understanding complex computational models, focusing on discontinuities and discrete input variables, and applies it to risk analysis of compound risk models and reinsurance credit risk.
- [Joint model for longitudinal and spatio-temporal survival data](https://www.ml-quant.com/papers/arxiv/2311.04008/): Longitudinal and Spatio-Temporal Survival Model: The Spatio-Temporal Joint Model (STJM) is a new method for credit risk analysis that uses spatial and temporal data to predict a borrower's risk, showing better results when spatial data is included.