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The purpose of this paper is to develop a loan insurance pricing model allowing for the skewness and kurtosis existing in underlying asset returns.

Using the theory of Gram-Charlier option, the authors first derive a closed-form solution of the Gram-Charlier pricing model. To address the difficulties in implementing the pricing model, the authors subsequently propose an iterative method to estimate skewness and kurtosis in practical application, which shows a relatively fast convergence rate in the empirical test.

Not only the theoretical analysis but also the empirical evidence shows that the effects of skewness and kurtosis on loan insurance premium tend to be negative and positive, respectively. Furthermore, the actual values of skewness and kurtosis are usually negative and positive, respectively, which leads to the empirical result that the pricing model ignoring skewness and kurtosis substantially underestimates loan insurance premium.

This paper proposes a loan insurance pricing model considering the skewness and kurtosis of asset returns, in which the authors use the theory of Gram-Charlier option. More importantly, the authors further propose a novel iterative method to estimate skewness and kurtosis in practical application. The empirical evidence suggests that the Gram-Charlier pricing model captures the information content of skewness and kurtosis.

In less competitive markets, firms with market power are likely to exercise pricing power by setting output prices above their marginal cost, inducing welfare losses from resource misallocation, managerial inefficiency and market instability. In order to address such market imperfections, it is important for regulatory authorities to identify the sources of pricing power and devise policies to address their adverse effects. In this context, the purpose of this paper is to undertake an empirical analysis to identify the determinants of pricing power in the South African non-life insurance market.

The authors estimate the Lerner competitive index as the proxy for pricing power using annual data on 79 firms from 2007 to 2012. In the second stage, the paper employs panel regression techniques in the ordinary least squares, random effects and generalised method of moment’s estimations to examine the effect of insurer level characteristics on pricing power.

The authors find the market to be characterised by firms with high pricing power. Domestic-owned insurers are found to exercise high pricing power compared with foreign-owned insurers. The authors also identify size, cost efficiency, product line diversification, market concentration, leverage and reinsurance contracts as the significant predictors of pricing power in the market. Finally, through a quantile regression analysis, the authors find the effect of cost efficiency, business line diversification and reinsurance to be heterogeneous across different quantiles of pricing power.

The findings provide regulatory authorities with useful indicators in addressing anti-competitive behaviour in high pricing power to enhance the stability of the insurance market and improve consumer welfare and economic development.

To the best of the authors’ knowledge, this is first paper to examine the determinants of pricing power and competitive behaviour in an insurance market.

The purpose of this paper is to alleviate the moral hazard problem created by deposit insurance and therefore develop a deposit insurance pricing model explicitly considering systematic risk.

Using the market model, the authors introduce the systematic risk component consisting of market risk and beta risk. A closed-form solution for the authors’ pricing model is derived based on the option pricing framework.

Compared with the authors’, the pricing model that ignores systematic risk underestimates deposit insurance premium, and cannot cover the excessive loss created by systematic risk. To examine the effect of the systematic risk component on the deposit insurance premiums estimated by the authors’ model, this paper also provides empirical evidence from China by regression analysis. The results demonstrate that, in addition to the individual failure risk, the systematic risk component is properly priced and explicitly reflected in the authors’ model.

More risk factors such as liquidity risk should be introduced in the pricing of deposit insurance.

Deposit insurance premiums estimated by the authors’ model can alleviate the moral hazard problem that banks have an incentive to take on excessive systematic risk, because substantial higher insurance premiums would be charged in doing so.

Applying the option pricing theory and market model, this paper develops a deposit insurance pricing model with explicit consideration of systematic risk. The systematic risk component contains not only the market volatility but also the sensitivity of market risk.

This article aims to examine the risk inherent in the insurance of the aviation industry, to take an outsider's look at those risks and to develop certain “capital market” pricing rules.

The aviation industry presents a classic low‐frequency/high‐limit insurance problem. Pricing such exposures is difficult because of the high degree of uncertainty involved. After a review of the considerations that an insurance underwriter traditionally brings to the problem, the author applies a simple pricing rule incorporating current thinking on risk evaluation.

The results of the author's approach are compared with the results of traditional pricing rules, generating interesting insights. The application of the new methodology to the analysis of current pricing and of alternative proposals is suggested.

The article will be of value to those interested in the aviation industry and in the pricing of insurance and reinsurance.

The development of digital technology has provided technical support to various industries. Specifically, Internet-based freight platforms can ensure the high-quality development of the logistics industry. Online freight platforms can use cargo transportation insurance to improve their service capabilities, promote their differentiated development, create products with platform characteristics and increase their core competitiveness.

This study uses a generalised linear model to fit the claim probability and claim intensity data and analyses freight insurance pricing based on the freight insurance claim data of a freight platform in China.

Considering traditional pricing risk factors, this study adds two risk factors to fit the claim probability data, that is, the purchase behaviour of freight insurance customers and road density. The two variables can significantly influence the claim probability, and the model fitting outcomes obtained with the logit connection function are excellent. In addition, this study examines the model results under various distribution types for the fitting of the claim intensity data. The fitting outcomes under a gamma distribution are superior to those under the other distribution types, as measured by the Akaike information criterion.

With actual data from an online freight platform in China, this study empirically proves that a generalised linear model is superior to traditional pricing methods for freight insurance. This study constructs a generalised linear pricing model considering the unique features of the freight industry and determines that the transportation distance, cargo weight and road density have a significant influence on the claim probability and claim intensity.

This article discusses issues common to the pricing of both insurance and finance. These include increasing collaboration between insurance companies and banks, deregulation of various insurance and finance markets, integrated risk management, and the emergence of financial engineering as a new profession. Rather than attempting to give an exhaustive exposition of the issues at hand, the author highlights developments that, from a methodological point of view, offer new insight into the comparison of pricing mechanisms between insurance and finance.

The authors present a model that incorporates stochastic interest rates to value equity‐linked life insurance contracts. The model generalizes some previous pricing results of Arne and Persson [1994] that are based on deterministic interest rates. The article also proposes and compares a design for a new equity‐linked product with the periodical premium contract of Brennan and Schwartz [1976]. The advantages of the proposed prod‐uct are its simplicity in pricing and its ease of hedging, by using either by long positions in the linked mutual fund or by European call options on the same fund.

The purpose of this paper is to develop a model to hedge annuity portfolios against increases in life expectancy. Across the globe, and in the industrial nations in particular, people have seen an unprecedented increase in their life expectancy over the past decades. The benefits of this apply to the individual, but the dangers apply to annuity providers. Insurance companies often possess no effective tools to address the longevity risk inherent in their annuity portfolio. Securitization can serve as a substitute for classic reinsurance, as it also transfers risk to third parties.

This paper extends on methods insurer's can use to hedge their annuity portfolio against longevity risk with the help of annuity securitization. Future mortality rates with the Lee-Carter-model and use the Wang-transformation to incorporate insurance risk are forecasted. Based on the percentile tranching method, where individual tranches are aligned to Standard & Poor's ratings, we price an inverse survivor bond. This bond offers fix coupon payments to investors, while the principal payments are at risk and depend on the survival rate within the underlying portfolio.

The contribution to the academic literature is threefold. On the theoretical side, building on the work of Kim and Choi (2011), we adapt their pricing model to the current market situation. Putting the principal at risk instead of the coupon payments, the insurer is supplied with sufficient capital to cover additional costs due to longevity. On the empirical side, the method for the German market is specified. Inserting specific country data into the model, price sensitivities of the presented securitization model are analyzed. Finally, in a case study, the procedure to the annuity portfolio of a large German life insurer is applied and the price of hedging longevity risk is calculated.

To illustrate the implication of this bond structure, several sensitivity tests were conducted before applying the pricing model to the retail sample annuity portfolio from a leading German life insurer. The securitization structure was applied to calculate the securitization prices for a sample portfolio from a large life insurance company.

The findings contribute to the current discussion about how insurers can face longevity risk within their annuity portfolios. The fact that the rating structure has such a severe impact on the overall hedging costs for the insurer implies that companies that are willing to undergo an annuity securitization should consider their deal structure very carefully. In addition, we have pointed out that in imperfect markets, the retention of the equity tranche by the originator might be advantageous. Nevertheless, one has to bear in mind that by this behavior, the insurer is able to reduce the overall default risk in his balance sheet by securitizing a life insurance portfolio; however, the fraction of first loss pieces from defaults increases more than proportionally. The insurer has to take care to not be left with large, unwanted remaining risk positions in his books.

In this paper, we extend on methods insurer's can use to hedge their annuity portfolio against longevity risk with the help of annuity securitization. To do so, we take the perspective of the issuing insurance company and calculate the costs of hedging in a four-step process. On the theoretical side, building on the work of Kim and Choi (2011), we adapt their pricing model to the current market situation. On the empirical side, we specify the method for the German market. Inserting specific country data into the model, price sensitivities of the presented securitization model are analyzed.

The purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market.

Based on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market.

The authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks.

This paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.

This paper aims to investigate the interaction between capital requirements and pricing constraints as measures for insurance regulation.

In a theoretical model framework, the author derives the insurer’s shareholder-value-maximizing response to capital regulation, price regulation and the unregulated strategy as a benchmark; all three strategies are presented in an analytical form.

The paper demonstrates that risk-based capital requirements exhibit an efficiency advantage over price regulation and allow for lower premiums. Moreover, the analysis identifies situations in which price floors make insurance more expensive, but have no positive impact on the safety level.

The comparison between capital regulation and price floors provides policymakers with a methodology to evaluate which regulatory tool is more appropriate. Also, the article discusses that maximum discount rates for European life insurers could be ineffective when the new regulatory framework Solvency II is in place.

In all, the article obtains analytical and informative results with relevant implications for insurance regulation.