Improving CAT bond pricing models via machine learning
- PDF / 1,796,747 Bytes
- 19 Pages / 595.276 x 790.866 pts Page_size
- 94 Downloads / 184 Views
ORIGINAL ARTICLE
Improving CAT bond pricing models via machine learning Tobias Götze1 · Marc Gürtler1 · Eileen Witowski1 Revised: 30 April 2020 / Published online: 23 June 2020 © The Author(s) 2020
Abstract Enhanced machine learning methods provide an encouraging alternative to forecast asset prices by extending or generalizing the possible model specifications compared to conventional linear regression methods. Even if enhanced methods of machine learning in the literature often lead to better forecasting quality, this is not clear for small asset classes, because in small asset classes enhanced machine learning methods may potentially over-fit the in-sample data. Against this background, we compare the forecasting performance of linear regression models and enhanced machine learning methods in the market for catastrophe (CAT) bonds. We use linear regression with variable selection, penalization methods, random forests and neural networks to forecast CAT bond premia. Among the considered models, random forests exhibit the highest forecasting performance, followed by linear regression models and neural networks. Keywords CAT bond · Machine learning · Linear regression · Risk premium JEL Classification C45 · C58 · G12 · G17 · G22
Introduction Empirical models to forecast the future price of financial assets are predominantly based on linear regression models (Campbell and Thompson 2007; Rapach et al. 2010; Thornton and Valente 2012). A key strength of linear regression models is that the economic relationships between the variables in the model can be understood and interpreted with relatively low effort. Interpretability is important for developing a forecasting model because the modeler can identify the causes for the poor performance of the model relatively easily. However, a model to forecast asset prices must fulfill further requirements and should (1) provide precise estimates over the respective forecasting horizon, (2) be robust toward outliers and (3) build a stable relationship between * Marc Gürtler marc.guertler@tu‑bs.de Tobias Götze t.goetze@tu‑bs.de Eileen Witowski e.witowski@tu‑bs.de 1
Department of Finance, Braunschweig Institute of Technology, Abt‑Jerusalem‑Straße 7, 38106 Brunswick, Germany
Vol:.(1234567890)
the dependent and explanatory variables throughout the calibration and forecasting horizon (to be robust toward changing market conditions). In order to fulfill these requirements, an asset pricing model should be based on both statistically and economically significant price determinants and avoid over-fitting issues. Besides, a good forecasting framework relies on the correct specification of the functional relationship between the dependent and explanatory variables and a suitable choice of the underlying conditions of the prediction (Gu et al. 2020). Therefore, the development of an appropriate forecasting model is evidently a complex problem, and linear regression models may not always provide the best solution to that problem. Thus, enhanced machine learning methods provide a p
Data Loading...
