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Systematic mortality risk, i.e. the risk of unexpected changes in mortality and survival rates, can substantially impact a life insurers' risk and solvency situation. By using the “natural hedge” between life insurance and annuities, insurance companies have an effective tool for reducing their net‐exposure. The purpose of this paper is to analyze this risk management tool and to quantify its effectiveness in hedging against changes in mortality with respect to default risk measures.

To achieve this goal, the paper models the insurance company as a whole and takes into account the interaction between assets and liabilities. Systematic mortality risk is considered in two ways. First, systematic mortality risk is modeled using scenario analyses and, second, empirically observed changes in mortality rates for the last 10‐15 years are used.

The paper demonstrates that the consideration of both the asset and liability side is vital to obtain deeper insight into the impact of natural hedging on an insurer's risk situation and shows how to reach a desired safety level while simultaneously immunizing the portfolio against changes in mortality rates.

The paper contributes to the literature by considering the insurance company as a whole in a multi‐period setting and taking into account both, assets and liabilities, as well as their interaction. Furthermore, the paper shows how to obtain a desired safety level while simultaneously immunizing a portfolio against changes in default risk.

The purpose of this paper is to develop new tools to interpret changes in risk neutral probability distributions (RNPDs). It distinguishes between changes attributable to upside potential and those attributable to downside risk, and shows that the distinction is supported empirically.

This paper estimates pricing kernels and RNPDs from option price data, then studies the expected excess returns on a fixed‐strategy reference portfolio composed of the claims defined by the RNPDs. The portfolio is disaggregated so that realized returns can be expressed as a value‐weighted average of returns to upside (investment) and downside (insurance) sub‐portfolios, respectively. An upside sub‐portfolio can be interpreted as defining payoffs to a call option, a downside sub‐portfolio as payoffs to a short put position.

Empirical results indicate that the realized excess returns on the reference portfolios are significantly and negatively related to both S&P index growth and volatility (measured by the Chicago Board Options Exchanges (CBOEs) volatility index (VIX)) in the original data, but neither variable is significant in regressions on data first differences. However, in regressions on both the original data and first differences, realized excess returns on the investment sub‐portfolios are significantly and negatively related to both S&P index growth and volatility, whereas the realized excess returns to insurance sub‐portfolios are significantly and positively related only to the VIX. In regressions on both original data and its first differences the ratio of realized insurance excess return to total return is positively and significantly related only to the VIX. Constant terms are significant in about half of all the regressions, suggesting the presence of additional explanatory factors not captured in currently available data.

The paper shows that upside and downside sub‐portfolios have different return distributions in different market regimes, and that while returns to upside claims depend significantly on both S&P index growth and volatility, returns to downside claims depend significantly on just S&P index volatility. Thus realized excess returns to sub‐portfolios convey more nearly precise information about changes in market attitudes than do realized excess returns to entire portfolios. Although concepts of aggregating and disaggregating information have been investigated in the context of annual earnings announcements in other research, they have not previously been applied to realized portfolio returns in the manner used here. If the paper's findings are sustained in further empirical analyses, they can potentially provide information regarding both the Grossman‐Zhou and Holmstrom‐Tirole theories of claim pricing. Overall, because they distinguish between upside potential and downside risk, these methods contribute to more discriminating ways of understanding reference portfolio returns. In contrast, the CAPM measures of return variance do not distinguish between the risks of returns fluctuating on the upside from the risk of returns fluctuating on the downside.

Purpose: In addition to the liberalisation policy, big data has revolutionised the level of awareness among customers about the quality and prices of insurance products. The rationale behind this study is to underline the issues in managing product portfolios in a disruptive environment, where a sudden and unexpected situation like COVID-19 pandemic is going to challenge the traditional models and insurance covers of organisations as well as individuals.

Methodology: The study is based on secondary data. The scope of the study will only be confined to the top two general insurance companies in India based on year of registration and market share to compare their product portfolios during pre- and post-liberalisation periods ranging from 1985–1986 to 2000–2001 and 2001–2002 to 2018–2019, respectively.

Findings: There is a lack of a balanced product portfolio for fulfilling the varying needs of customers. The insurance companies needed to set up different portfolios and should provide separate covers for natural catastrophes such as floods, earthquakes, landslides, tsunami, and the occurrence of new pandemics like COVID-19.

Significance: The study highlights that the outbreak of COVID-19 and similar pandemics or global emergencies need special preparation from the insurance sector.

Due to the low crop insurance participation by grain growers in the Pacific Northwest, the performance of insurance programs and the futures market is assessed in this area. Revenue insurance, combined with the futures and government programs, is identified as the optimal risk management portfolio. Although yield risk level, decision maker’s risk preference, and actuarial fairness of premiums can all affect farmers’ choices, the current subsidy policy is most influential. The varying subsidy levels induce farmers’ subsidy‐seeking incentive and suppress the risk‐reducing incentive. There is little diversification effect from growing two crops in the rotation instead of one.

The purpose of this paper is to examine the reduction of crop yield uncertainty using rainfall index insurances. The insurance payouts are determined by a transparent rainfall index rather than actual crop yield of any producer, thereby circumventing problems of adverse selection and moral hazard. The authors consider insurances on rainfall indexes of various months and derive an optimal insurance portfolio that minimizes the income variance for a crop producer.

Various regression models are considered to relate crop yield to monthly mean temperature and monthly cumulative precipitation. A bootstrapping method is used to simulate weather indexes and corn yield in a future year with the correlation between precipitation and temperature incorporated. Based on the simulated scenarios, the optimal insurance portfolio that minimizes the income variance for a crop producer is obtained. In addition, the impact of correlation between temperature and precipitation, availability of temperature index insurance and geographical basis risk on the effectiveness of rainfall index insurance is examined.

The authors illustrate the approach with the corn yield in Illinois east crop reporting district and weather data of a city in the same district. The analysis shows that corn yield in this district is negatively influenced by excessive precipitation in May and drought in June–August. Rainfall index insurance portfolio can reduce the income variance by up to 51.84 percent. Failing to incorporate the correlation between temperature and precipitation decreases variance reduction by 11.6 percent. The presence of geographical basis risk decreases variance reduction by a striking 24.11 percent. Allowing for the purchase of both rainfall and temperature index insurances increases variance reduction by 13.67 percent.

By including precipitation shortfall into explanatory variables, the extended crop yield model explains more fluctuation in crop yield than existing models. The authors use a bootstrapping method instead of complex parametric models to simulate weather indexes and crop yield for a future year and assess the effectiveness of rainfall index insurance. The optimal insurance portfolio obtained provides insights on the practical development of rainfall insurance for corn producers, from the selection of triggering index to the demand of the insurance.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

The purpose of this study is to introduce an insurance risk‐exchange model in the presence of background risk and private information and which solves the optimal insurance and investment decisions simultaneously.

The model undertakes a continuous‐time two‐agent framework in which the decisions depend on who can determine the insurance quantity as well as the agents' risk attitudes. The decisions are solved by using dynamic‐programming techniques.

The results show that the insured may purchase full insurance even if the insurance price is actuarially unfair and the insurance risk and investment risk are uncorrelated. Further, the demand for insurance may be affected by background risk even if the two risks are uncorrelated. If Pareto optimality is impeded by private information, the paper shows that the deadweight loss can be mitigated by forming a hedging demand with respect to the parameter risk.

This study is not only an extension of the existing continuous‐time insurance demand model, but also may be considered a model of “enterprise risk management” for institutional agents.

The purpose of this paper is to develop a theoretical model of a jump diffusion-mean reversion constant proportion portfolio insurance strategy under the presence of transaction cost and stochastic floor as opposed to the deterministic floor used in the previous literatures.

The paper adopts Merton’s jump diffusion (JD) model to simulate the price path followed by risky assets and the CIR mean reversion model to simulate the path followed by the short-term interest rate. The floor of the CPPI strategy is linked to the stochastic process driving the value of a fixed income instrument whose yield follows the CIR mean reversion model. The developed model is benchmarked against CNX-NIFTY 50 and is back tested during the extreme regimes in the Indian market using the scenario-based Monte Carlo simulation technique.

Back testing the algorithm using Monte Carlo simulation across the crisis and recovery phases of the 2008 recession regime revealed that the portfolio performs better than the risky markets during the crisis by hedging the downside risk effectively and performs better than the fixed income instruments during the growth phase by leveraging on the upside potential. This makes it a value-enhancing proposition for the risk-averse investors.

The study modifies the CPPI algorithm by re-defining the floor of the algorithm to be a stochastic mean reverting process which is guided by the movement of the short-term interest rate in the economy. This development is more relevant for two reasons: first, the short-term interest rate changes with time, and hence the constant yield during each rebalancing steps is not practically feasible; second, the historical literatures have revealed that the short-term interest rate tends to move opposite to that of the equity market. Thereby, during the bear run the floor will increase at a higher rate, whereas the growth of the floor will stagnate during the bull phase which aids the model to capitalize on the upward potential during the growth phase and to cut down on the exposure during the crisis phase.

The purpose of this paper to develop an empirical methodology for managing spatial basis risk in weather index insurance by studying the fundamental causes for differences in weather risk between distributed locations.

The paper systematically compares insurance payouts at nearby locations based on differences in geographical characteristics. The geographic characteristics include distance between stations and differences in altitude, latitude, and longitude.

Geographic differences are poor predictors of payouts. The strongest predictor of payout at a given location is payout at nearby location. However, altitude has a persistent effect on heat risk and distance between stations increases payout discrepancies for precipitation risk.

Given that payouts in a given area are highly correlated, it may be possible to insure multiple weather stations in a single contract as a “risk portfolio” for any one location.

Spatial basis risk is a fundamental problem of index insurance and yet is still largely unexplored in the literature.

Two strands of the literature are combined, namely the modeling of disability insurance and the design, valuation and discussion of insurance-linked securities.

This paper provides a discussion regarding the advantages and detriments of disability-linked securities in comparison with mortality-linked bonds and swaps as well as regarding potential disability-linked indices and the potential use. The discussion is followed by an introduction of a potential design and a corresponding valuation of disability bonds and swaps.

This securitization will provide useful tools for the risk management of disability risk in a risk-based regulatory framework.

No disability-linked securities have been defined and discussed so far.