the years 1900 to 2019, we compare the goodness of fit and forecasting performance of the four nonlinear models. We then … demonstrate the implications of nonlinearity in mortality modeling on the pricing of longevity bonds as a practical illustration …

This study considers the forecasting of mortality rates in multiple populations. We propose a model that combines … mortality forecasting and functional data analysis (FDA). Under the FDA framework, the mortality curve of each year is assumed … to be a smooth function of age. As with most of the functional time series forecasting models, we rely on functional …

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the … Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank's short-term resilience … risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the …

We introduce an additive stochastic mortality model which allows joint modelling and forecasting of underlying death … Monte Carlo (MCMC) is used for parameter estimation. We then link our proposed model to an extended version of the credit … risk model CreditRisk+. This allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm …

shows better forecasting accuracy than the Lee-Carter and Bayesian vector autoregressive (BVAR) models without regime …-switching and while retaining the advantages of BVAR. MSBVAR provides more reliable estimates for parameter uncertainty and more …We apply a Markov-switching Bayesian vector autoregression (MSBVAR) model to mortality forecasting. MSBVAR has not …

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to … analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in … that non-normal distributions are best suited for both model fitting and forecasting. The MC-GARCH(1,1) model under the …

Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity … generalised error distribution (GED). Based on the smallest forecasting metrics values for mean absolute error (MAE) and mean … robust performing model for gold is the ARFIMA (1, o, 1)-FIGARCH (1, ξ, 1)-GED model. The best performing forecasting model …

research and studying almost 30 years of quarterly underwriting data, we can improve forecasting performance by (dis …