Compared to the past literature on prediction markets that uses small-scale observational field data or experiments, this present research examines the efficiency of such markets by studying catastrophe (CAT) bonds. We collect actual catastrophe loss data, match them with the defined trigger events of each CAT bond contract, and then employ an empirical pricing framework to obtain the excess CAT premiums in order to represent the market-based forecasts. Our results indeed show that market-based forecasts have more significant predictive content for future CAT losses than professional forecasts that use natural catastrophe risk models. Although the predictive information for CAT events is specialized and complex, our evidence supports that CAT bond markets are successful prediction markets that efficiently aggregate information about future CAT losses. Our resultsalso highlight that actual CAT losses in future periods can explain the excess CAT bond spreads in the primary market and provide support for market efficiency when pricing CAT risk.

A multiperiod model is developed to measure the costs posed to the guaranty fund in a setting that incorporates risk-based capital regulations, interest rate risk and the possibility of catastrophic losses. The guaranty contract is modeled as a put option on the asset of the insurance company with a stochastic strike price and an uncertain maturity. The impacts of the key factors of this model are examined numerically and shown to make material differences in the costs to the guaranty fund.

We propose a multiperiod deposit insurance pricing model that simultaneously incorporates the capital standard and the possibility of forbearance. The model employs the recently developed GARCH option pricing technique in determining the deposit insurance value. Our model offers two distinctive advantages. First, it explicitly considers the implications of the strict enforcement on capital standard as stipulated in FDIC Improvement Act of 1991. Second, the use of the GARCH model allows us to capture many robust features exhibited by financial asset returns. By the GARCH option pricing theory, the value of a contingent claim is a function of the asset risk premium. This unique feature is found to be prominent in determining the bank's deposit insurance value. We also examine the effects of capital forbearance and moral hazard behavior in this multiperiod deposit insurance setting.

This study develops a structural framework to value insurers’ contingent capital with counterparty risk (CR) and overcomes the problem of price endogeneity (PE) in the valuation model. Our results on the focal contingent capital instrument – catastrophe equity put option (CatEPut) – indicate that prices can be significantly overestimated without considering CR and be significantly underestimated without considering PE. This study also examines how CatEPuts affect the buyer’s probability of default (PD). Our results show that buying a CatEPut lowers the PD for high-risk insurers, but not necessarily so for low-risk insurers; however, without taking CR and PE into account, one may significantly overestimate the credit enhancement provided by the CatEPuts.

This research proposes a novel firm-based model for pricing cyber insurance. Our model considers two types of cyber risk: virus attacks and data breaches. Virus attacks deliver adverse shocks to the firm’s productivity, while data breaches cause premium customer departures that worsen the prospect of the firm’s product demand. We derive the endogenous structural form of cyber losses in firms and utilize it to solve the formula for cyber insurance premiums. Our quantitative results show that the consensus prediction about a strictly positive premium-risk nexus is no longer valid. Asymmetries in the sub-premium’s sensitivity to cyber risks from different sources and the premium customer loss rates jointly shape the complexity of the relation between cyber insurance premiums and cyber risks. Improvements in the product demand conditions enhance firms’ incentives to hedge cyber losses and push premiums higher. Lastly, we discuss the influence of product price competition on premiums.