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The purpose of this paper is to present a simulation‐based approach for modeling multi‐year non‐life insurance risk in internal risk models. Strategic management in an insurance company requires a multi‐year time horizon for economic decision making, for example, in the context of internal risk models. In the literature to date, only the ultimate perspective and, more recently, the one‐year perspective (for Solvency II purposes) are considered.

The authors present a way of defining and calculating multi‐year claims development results and extend the simulation‐based algorithm (“re‐reserving”) for quantifying one‐year non‐life insurance risk, presented in Ohlsson and Lauzeningks, to a multi‐year perspective.

The multi‐year algorithm is applied to the chain ladder reserving model framework of Mack (1993).

The usefulness of the new multi‐year horizon is illustrated in the context of internal risk models by means of a case study, where the multi‐year algorithm is applied to a claims development triangle based on Mack and on England and Verrall. This algorithm has been implemented in an excel tool, which is given as supplemented material.

To the best of the authors' knowledge, there are no model approaches or studies on insurance risk for projection periods of not just one, but several, new accident years; this requires a suitable extension of the classical Mack model; however, consideration of multiple years is crucial in the context of enterprise risk management.

The purpose of this paper is to explore the relationship between insurance and economic growth for six (developed, emerging and developing) countries over the period of 1980 to 2015.

The study applies panel auto-regressive distributed lagged (PMG/ARDL) method to examine long-term and short-term relationship between insurance and economic growth for the USA, the UK, China, India, Malaysia and Pakistan.

The authors concluded that there exists a positive and significant relationship between life insurance, non-life insurance, trade openness, stock-market development and economic growth in the long run as p-value is less than 5 per cent. This study also found a significant relationship between employment rate, banking development and economic growth for the long run but the direction is negative. Foreign direct investment shows an insignificant relationship with economic growth in the long run. The results highlighted a significant and positive relationship between non-life insurance and economic growth in the short-run for the USA, the UK, China, India, Malaysia and Pakistan. Moreover, the relationship between life insurance and economic growth is positive and significant for India, Pakistan and the UK. Results reveal a significant but a negative relationship between life insurance and economic growth for the USA, China and Malaysia.

Analysis is performed for only six countries and results of these six might not represent the whole world.

This research would help policymaker to consider wider aspects of insurance rather than considering it complementary service industry.

Every individual, today, spends a huge amount of funds to purchase insurance. He or she should be aware of the wider social impact of their spending apart from risk transferring.

Researchers recently shifted their focus to investigate the relationship between insurance and economic growth but the topic is still lacking sufficient literature and various knowledge gaps. The study is an attempt to contribute in terms of refinement of the already existing body of knowledge and to fill literature gap. In addition, apart from the insurance–economy relationship, very few empirical studies used financial, banking and stock market along with insurance, proxies to measure accurate insurance contribution. Another element of originality lies in the comparative analysis of developed, emerging and developing countries.

The purpose of this paper is to illustrate the importance of modeling parameter risk in premium risk, especially when data are scarce and a multi‐year projection horizon is considered. Internal risk models often integrate both process and parameter risks in modeling reserve risk, whereas parameter risk is typically omitted in premium risk, the modeling of which considers only process risk.

The authors present a variety of methods for modeling parameter risk (asymptotic normality, bootstrap, Bayesian) with different statistical properties. They then integrate these different modeling approaches in an internal risk model and compare their results with those from modeling approaches that measure only process risk in premium risk.

The authors show that parameter risk is substantial, especially when a multi‐year projection horizon is considered and when there is only limited historical data available for parameterization (as is often the case in practice). The authors' results also demonstrate that parameter risk substantially influences risk‐based capital and strategic management decisions, such as reinsurance.

The authors' findings emphasize that it is necessary to integrate parameter risk in risk modeling. Their findings are thus not only of interest to academics, but of high relevance to practitioners and regulators working toward appropriate risk modeling in an enterprise risk management and solvency context.

To the authors' knowledge, there are no model approaches or studies on parameter uncertainty for projection periods of not just one, but several, accident years; however, consideration of multiple years is crucial when thinking strategically about enterprise risk management.

The purpose of this paper is to illustrate how risk capital can be calculated and allocated in a multi‐year context. This is an important issue, since strategic management and decision making within insurance companies require a multi‐year time horizon (instead of a one‐year time horizon, as set out in solvency models).

After defining risk capital in a multi‐year context, the paper discusses the different properties of the multi‐year risk capital concept. The paper also presents an allocation rule of how to allocate the multi‐year risk capital to individual years as well as to individual segments. The paper applies the author's model framework in an application study to illustrate the different effects.

The paper shows how multi‐year risk capital can be used as a basis for analyzing different management strategies within risk and return indicators in the context of value‐based management. Furthermore, the paper demonstrates the effect of allocating risk capital in a multi‐year context.

The analysis provides new and relevant information to insurance companies' management. Whereas usually solvency rules set out a time horizon of one year, in the context of internal risk models a multi‐year planning horizon is taken into account. Management needs to get an idea of how much risk capital is necessary in order to survive the next five years without external capital supply. The paper presents an answer to these questions.

The purpose of this paper is to look at the main empirical findings related to the bank‐insurance model and to outline the market practices across the world. The market dynamics underpinning the bancassurance phenomenon are analyzed alongside discussions of the various bancassurance products and bank‐insurance modes of entry.

The paper presents a brief survey of the bank‐insurance trend and provides an insight into the underlying dynamics and corporate structures of financial conglomerates.

There is an uneven success of the bancassurance phenomenon across the world. It is not clear whether re‐regulation is the cause or response to globalization, and vice versa, which in turn both shape the bancassurance arena. A number of incentives for the formation of financial conglomerates are identified. Finally, three modes of entry have been documented to reflect market realities.

The paper will be of value to those interested in financial conglomerates, banking and insurance. It is suitable for academics and practitioners alike.

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.

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.

The authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.

The backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.

The paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.

The authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.

The authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

The purpose of this editorial is to provide a quick glance at the dominant issues that have characterized the developing economics debate during the past five decades. It seeks to offer a backdrop for the papers in the present volume of AJEMS.

It is based on a review of a selection of literature that highlights the dominant perspectives in development economics.

It draws a distinction between soft and hard economics, arguing that economic growth must be converted into social change that benefits poor for it to be described as development-oriented.

It provides a direction for future research into issues of economic growth and poverty alleviation in Sub-Sahara Africa.

The purpose of this paper is to examine the risk management practices of life assurance firms and non‐life insurance firms.

Through a comparative case study methodology, the study assesses the state of risk management in both life assurance companies and non‐life insurance firms to determine whether they exhibit different or similar risk management practices. The results of the survey were also analyzed and compared to the principles of good practices in financial risk management.

The findings of the study revealed some differences and similarities in the risk management practices of life and non‐life insurance firms. Almost all the life companies have stated their risk appetite levels, which enable them to identify which risks to absorb and which ones to transfer. But non‐life insurance firms have not laid down their risk tolerance levels explicitly. The results further revealed that the industry lacks sufficient personnel with the requisite risk management skills and that the sector does not manage risks proactively, rather they do so in a reactive response to regulatory directives.

Effective management of risks by insurers will increase the penetration of insurance in Ghana.

Risk management is a crucial issue, not only for the survival and profitability of the insurance industry, but also for the socio‐economic growth and development of the whole economy. As major risks underwriters, insurance companies need to adopt good practices or quality measures in the management of financial risk. This is important, more so, as the industry prepares to re‐position itself to underwrite the risks in the emerging oil and gas industry of Ghana.

Research into financial risk management in the insurance industry from the Ghanaian perspective is rare. This study is therefore timely and its findings are invaluable for the efficient management of financial risk in the insurance industry.

The purpose of this paper is to examine the impact of the regulatory-driven market structure on firm pricing behaviour by testing the structure-conduct-performance (S-C-P) hypothesis for both life and non-life insurance markets in Ghana.

Using a panel data on 14 life and 22 non-life insurers from 2007 to 2011, the authors employed the Herfindahl Hirschman Index and concentration ratio as proxies for the S-C-P hypothesis while efficiency scores were estimated using the data envelopment analysis technique to proxy for the efficient structure (ES) hypothesis. The dependent variable, profitability was measured as return on assets while controlling for size, underwriting risk, leverage, GDP growth rate and inflation. The models were estimated using the panel corrected standard errors of Beck and Katz (1995) and random effects estimations.

The results from the empirical estimation provide ample evidence in support for ES hypothesis for both life and non-life insurance markets. While conflicting results was found for SCP hypothesis in the non-life insurance market, it was rejected in the life insurance market. The findings also point to an increasing level of competition in both life and non-life insurance industry in Ghana though they still remain concentrated with the life insurance sector having high levels of efficiency compared to the non-life sector.

The findings of the study will enhance the understanding of firm behaviour in the new markets created to shape regulatory and competition policies of the regulator to promote consumer welfare while ensuring a stable industry to enhance its role in economic development.

This is the first study to test the market power and efficient hypotheses on the insurance industry in Ghana. To the best of the author’s knowledge, this study is the first to examine the determinants of profitability in the non-life insurance market.