Spatial modelling of claim frequency and claim size in non-life insurance

In this paper, models for claim frequency and average claim size in non-life insurance are considered. Both covariates and spatial random effects are included allowing the modelling of a spatial dependency pattern. We assume a Poisson model for the number of claims, while claim size is modelled using a Gamma distribution. However, in contrast to the usual compound Poisson model, we allow for dependencies between claim size and claim frequency. A fully Bayesian approach is followed, parameters are estimated using Markov Chain Monte Carlo (MCMC). The issue of model comparison is thoroughly addressed. Besides the deviance information criterion and the predictive model choice criterion, we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. We give an application to a comprehensive data set from a German car insurance company. The inclusion of spatial effects significantly improves the models for both claim frequency and claim size, and also leads to more accurate predictions of the total claim sizes. Further, we detect significant dependencies between the number of claims and claim size. Both spatial and number of claims effects are interpreted and quantified from an actuarial point of view.     

The propagation of liquidity imbalances in manufacturing supply chains: evidence from a spatial auto-regressive approach

The number of distressed firms increased sharply during the last recessionary phase. The scope of the paper is to analyze determinants of distress by focusing the attention on trade-credit chains as the key source of contagion effects in 2009–2013. Financial and liquidity imbalances propagate along the supply chain: firms respond to late payments from customers by defaulting on payments to suppliers. The novelty of our approach consists of applying spatial econometric techniques to assess spillover effects of trade debt. We employ a representative sample of around 12,000 Italian manufacturing firms that combines balance sheet items with information collected in the Credit Register of the Italian central bank. Our proxy for supply-chain interconnections is a matrix of firm-to firm transactions which mirrors the networked structure of the industrial system. Estimates show that trade debt has been affected by spillover effects during the great crisis of 2009–2013. Moreover, trade debt and financial indebtedness exerted an impact of almost identical magnitude on firms' distress likelihoods. This evidence sheds light on the importance to move away from a static view of the trade-credit phenomenon, and to integrate solvency models with detailed information on firm-to-firm transactions which is increasingly available through big data collection.     

Prediction of Individual Bond Prices via a Dynamic Bond Pricing Model: Application to Japanese Government Bond Price Data

In this paper, we propose a dynamic bond pricing model and report the usefulness of our bond pricing model based on analysis of Japanese Government bond price data. We extend the concept of the time dependent Markov (TDM) model proposed by Kariya and Tsuda (Financial Engineering and the Japanese Markets, Kluwer Academic Publishers, Dordrecht, The Netherlands, Vol. 1, pp. 1–20) to a dynamic model, which can obtain information for future bond prices. A main feature of the extended model is that the whole stochastic process of the random cash-flow discount functions of each individual bond has a time series structure. We express the dynamic structure for the models by using a Bayesian state space representation. The state space approach integrates cross-sectional and time series aspects of individual bond prices. From the empirical results, we find useful evidence that our model performs well for the prediction of the patterns of the term structure of the individual bond returns.     

The new political importance of the old hurricane risk: a contextual approach to understanding contemporary struggles with hurricane risk and insurance

In the United States, policy-makers struggle to resolve conflict between public demands for affordable insurance costs covering hurricanes and market demands for risk-based insurance pricing. Given the socially constructed nature of risk, a risk-based pricing approach prioritizes insurer values and business practices over all societal value goals expressly limiting democratic inclusion in decision-making about risk. As a step towards the more democratically inclusive approach of risk governance, this article uses the state of Florida as a case study to provide a narrative of the social and political context for the evolution of the idea of U.S. hurricane risk. I argue that today’s hurricane risk is a product of long-standing shared efforts to build prosperity. However, it is no longer a simple risk for society to overcome on its way to economic well-being. Contemporary hurricane risk is systemic and serves as a nexus for political battles over American values.