We propose a new machine learning-based framework for long-term mortality forecasting. Based on ideas of neighbouring prediction, model ensembling, and tree boosting, this framework can significantly improve the prediction accuracy of long-term mortality. In addition, the proposed framework...

While the COVID-19 pandemic has shattered the world with severe human toll and catastrophic economic losses and sufferings, it has also heightened the need for more effective solutions for managing epidemic-related risks. In this paper, we pro- pose two capital market-based epidemic financing...

The frequency and intensity of weather-related catastrophes, such as storms and floods, have been increasing due to climate change. This leads to rising storm catastrophe risks to the property-casualty insurance and reinsurance sector. In this paper, we propose an index-based storm catastrophe...

In the past decade, cyber insurance has raised much interest in the insurance industry, and cyber risk has evolved from a type of pure operational risk to both operational and liability risk for insurers. However, the modeling of cyber risk is still in its infancy. Compared with other insurance...

In this paper we propose a flexible framework for the design of weather index insurance (WII) based on penalized spline methods. The aim is to find the indemnity function which optimally characterizes the intricate relationship between agricultural production losses and weather variables and...

Tail risk measures such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) are popularly accepted criteria for financial risk management, but are usually difficult to optimize. Especially for VaR, it generally leads to a non-convex NP-hard problem which is computationally challenging....

The replacement closeout convention has drawn more and more attention since the 2008 financial crisis. Compared with the conventional risk-free closeout, the replacement closeout convention incorporates the creditworthiness of the counterparty and thus providing a more accurate estimate of the...