The compound Poisson process (CPP) is often used in catastrophe risk modeling, for example, aggregate loss risk modeling. Hence, CPP can be involved in pricing catastrophe bonds (CAT bonds) because it requires a catastrophe risk modeling method. However, studies of how the application of CPP in pricing CAT bonds is still scarce. Therefore, this study aims to conduct a systematic literature review (SLR) on how CPP is used in pricing CAT bonds. The SLR consists of three stages: the literature selection, bibliometric analysis, and gap analysis. At the literature selection stage, the 30 articles regarding the application of CPP in pricing CAT bonds are obtained. Then, the conceptual and nonconceptual structures of the articles are mapped at the bibliometric analysis stage. Finally, in the gap analysis stage, the application of CPP in pricing CAT bonds from the previous studies is analyzed, and new research opportunities are studied. This research can be a reference for researchers regarding the application of CPP in pricing CAT bonds and can motivate them to design more beneficial ways of pricing CAT bonds with CPP in the future.

Investor interest in single-trigger catastrophe bonds (STCB) has the potential to decline in the future. It is triggered by the increasing trend of global catastrophe loss and intensity every year, which increases the probability that a claim of STCB will occur. To increase investor interest again, the issuance of multiple-trigger catastrophe bonds (MTCB) can be one solution. However, to issue MTCB, its pricing is more complex because it involves more factors than STCB. Therefore, this study aims to design a simple MTCB pricing model. The claim trigger indices used are actual loss and fatality. Then, a nonhomogeneous compound Poisson process is used to model actual losses and fatalities aggregate to consider catastrophe intensity. In addition, this study proposes numerical methods, namely the continuous distribution approximation method and the Nuel recursive method, to facilitate the application of the model. Finally, an analysis of the effect of catastrophe intensity and other factors on MTCB prices is also presented. This study is expected to help special-purpose vehicles as MTCB issuers in MTCB pricing.

Investors generally aim to obtain a high return from their stock portfolio. However, investors must realize that a high value-at-risk (VaR) is essential to calculate for this aim. One of the objects in the VaR calculation is the asymmetric return volatility of stocks, which causes an unbalanced decrease and increase in returns. Therefore, this study proposes a mean-value-at-risk (mean-VaR) stock portfolio optimization model based on stocks’ asymmetric return volatility and investors’ risk aversion preferences. The first stage is the determination of the mean of all stocks in the portfolio conducted using the autoregressive moving average Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity (ARMA-GJR-GARCH) models. Then, the second stage is weighting the capital of each stock based on the mean-VaR model with the investors’ risk aversion preferences. This is conducted using the Lagrange multiplier method. Then, the model is applied to stock data in Indonesia’s capital market. This application also analyzed the sensitivity between the mean, VaR, both ratios, and risk aversion. This research can be used for investors in the design and weighting of capital in a stock portfolio to ensure its asymmetrical effect is as small as possible.

The issuance of multiple-event catastrophe bonds (MECBs) has the potential to increase in the next few years. This is due to the increasing trend in the frequency of global catastrophes, which makes single-event catastrophe bonds (SECBs) less relevant. However, there are obstacles to issuing MECBs since the pricing framework is still little studied. Therefore, this study aims to develop such a new pricing framework. The model uniquely involves three new variables: the trigger event correlation, interest, and inflation rates. The trigger event correlation rate was accommodated by the involvement of the copula while the interest and inflation rates were simultaneously considered using an integrated autoregressive vector stochastic model. After the model was obtained, the model was simulated on storm catastrophe data in the United States. Finally, the effect of the three variables on MECB prices was also analyzed. The analysis results show that the three variables make MECB prices more fairly than other models. This research is expected to guide special purpose vehicles to set fairer MECB prices and can also be used as a reference for investors in choosing MECBs based on the rates of trigger event correlation and the real interest they can expect.

Earthquake contingency costs in traditional insurance cannot provide sufficient earthquake funding for a country because they often differ significantly from actual losses. Over the last three decades, this approach has been replaced by linking earthquake insurance to bonds in the capital market; this is now known as the earthquake catastrophe bond (ECB). Through the ECB, contingency costs become larger and more sustainable earthquake funds. Unfortunately, there are challenges in ECB issuance, as the pricing framework does not yet have standard rules and still needs to be studied. Therefore, the objective of this study is to systematically review how the ECB pricing framework is designed. The method used in this review is PRISMA. First, articles aiming to design an ECB pricing framework were collected from the Scopus, Science Direct, and Dimensions databases on 22 March 2023. Then, the results were selected, resulting in eleven relevant articles. Then, the articles’ pricing frameworks were reviewed based on variables, methods, trigger events, coupon and redemption value payment schemes, and the model solution forms. Finally, several research opportunities for academics are also outlined. This research constitutes a reference for ECB issuers during the pricing process and can motivate academics to design more useful ECB pricing models.

The frequency and economic damage of natural disasters have increased globally over the last two decades due to climate change. This increase has an impact on the disaster insurance field, particularly in the calculation of premiums. Many regions have a shortcoming in employing insurance because the premium is too high compared with their budget allocation. As one of the solutions, the premium calculation can be developed by applying the cross-subsidies mechanism based on economic growth. Therefore, this research aims to develop premium models of natural disaster insurance that uniquely involve two new variables of an insured region: cross-subsidies and the economic growth rate. Another novelty is the development of the Black–Scholes model, considering the two new variables, and it is used to formulate the premium model. Following the modeling process, this study uses the model to estimate the premiums for natural disaster insurance in each province of Indonesia. The estimation results show that all new variables involved in the model novelties significantly affect the premiums. This research can be used by insurance companies to determine the premium of natural disaster insurance, which involves cross-subsidies and economic growth.