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Forecasting and trading credit default swap indices using a deep learning model integrating Merton and LSTMs

人工智能 计算机科学 机器学习 信用违约掉期 感知器 夏普比率 支持向量机 均方误差 索引(排版) 计量经济学 人工神经网络 经济 信用风险 数学 财务 统计 文件夹 万维网
作者
Weifang Mao,Huiming Zhu,Hao Wu,Yijie Lu,Haidong Wang
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:213: 119012-119012 被引量:17
标识
DOI:10.1016/j.eswa.2022.119012
摘要

Using macroeconomic and financial conditions to forecast credit default swap (CDS) spreads is a challenging task. In this paper, we propose the Merton-LSTM model, a modified LSTM model formed by integrating with the Merton determinants model, to forecast the CDS indices. We provide the rigorous math behind the Merton-LSTM model, which demonstrates that by leveraging the nonlinear learning ability of LSTM with increased model capacity, the Merton-LSTM model is expected to learn the inherent association between the Merton determinants and CDS spreads. Further, the Merton-LSTM model is compared with the machine learning models LSTM, gated recurrent unit (GRU), multilayer perceptron network (MLP), support vector machine (SVM) and a typical stochastic series model in forecasting the two most liquid five-year CDS indices, North America High Yield index (CDX.NA.HY) and North America Investment Grade index (CDX.NA.IG) through the root mean squared error (RMSE) and the Diebold-Mariano test. The comparison results show that the RMSEs of the Merton-LSTM model are the lowest (6.2570–27.2000 for CDX.NA.HY and 1.3168–6.4772 for CDX.NA.IG) compared to other competitive models. The superiority of the Merton-LSTM model in forecasting performance is highlighted in long-term prediction even with a forecasting horizon extended to 28 days. Simulated trading with different thresholds and horizons is conducted in this study. We find that the Merton-LSTM trading strategy yields the highest annualized Sharpe ratios and lowest maximum losses at most thresholds and horizons, highlighting the economic significance of the proposed model.
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