Natural Disasters, Political Risk and Insurance Market Development

Chang, Chun Ping; Berdiev, Aziz N.

doi:10.1057/gpp.2013.14

Cited by 23 publications

(21 citation statements)

References 65 publications

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“…40 However, we find that the structural break point exists in 2008, where the global financial crisis may account for the downward fall of oil prices. 41,42 Afterward, we observe an upward trend that may be ascribed to the economic recovery of developed countries following the crisis and then a sharp fall that may be driven by the boom of shale gas/oil fracturing.…”

Section: Data and Empirical Resultsmentioning

confidence: 86%

Oil prices and geopolitical risks: What implications are offered via multi-domain investigations?

Chang

Chu

et al. 2019

Energy & Environment

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This paper firstly investigates the frequency- and time-varying co-movement and causal relationship between crude oil prices (proxied by the West Texas Intermediate, Brent, Dubai and Nigerian Forcados spot oil prices) and geopolitical risks based on the wavelet analysis over the period of 1985–2016. Overall, our results demonstrate significant dynamic co-movement and causality in the varying time–frequency domains. We find high degree of co-movement between geopolitical risks and oil prices at high frequencies (in the short run) for the entire sample period; however, such a correlation does not exist at low frequencies (in the long run) for most of the sample period. We also find distinct patterns of causal relationships between geopolitical risks and oil prices across different benchmark markets. Results are robust when we control for global economic outlook. Our findings provide valuable implications for policy makers and oil market investors based on the dynamic relationship between geopolitical risks and oil prices.

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Section: Data and Empirical Resultsmentioning

confidence: 86%

Oil prices and geopolitical risks: What implications are offered via multi-domain investigations?

Chang

Chu

et al. 2019

Energy & Environment

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“…On the basis of a sample of 39 countries for the period 1984-2009, C. P. Chang and Berdiev (2013) study the influence of external factors, such as political risks and natural disasters, as well as young-age dependency ratio on the insurance market development. The obtained results in their study confirm that a higher dependency ratio causes a decline in life and non-life insurance penetration and density with having only insignificant impact on total insurance consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The age structure of the population is an important measure of demographic change (Bloom & Canning, 2004;Prskawetz et al, 2007). Theoretical considerations confirm that dependents play a vital role in the analysis of insurance consumption (Beck & Webb, 2003;Beenstock et al, 1986;Browne & Kim, 1993;Burnett & Palmer, 1984;Campbell, 1980;C. P. Chang & Berdiev, 2013;Dragos et al, 2019;Feyen et al, 2013;Hammond et al, 1967;Hussels et al, 2005;Kjosevski, 2012;Lewis, 1989;D.…”

Section: Introductionmentioning

confidence: 96%

The Impact of Demographic Burden on Insurance Density

Nebolsina

2020

Sage Open

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The article investigates the relationship between demographic burden and insurance market by employing panel vector autoregression models with six groups of endogenous variables to a dynamic panel data set of 25 economies for the period 1980–2016. Demographic burden is represented by dependency ratios measured in respect to the population younger than the age of 15 (young-age dependency ratio), population above the age of 64 (old-age dependency ratio) as well as males and females above the age of 64 being examined separately. As indicators of insurance market development, life insurance density, non-life insurance density, and total insurance density are used. The robustness of the results is verified across 10 subsamples of the main observation period. The conducted analyses show a heterogeneous impact of demographic burden on the insurance market. The impulse responses reveal that negative effects prevail in the long term, which may result from the negative impact of an increasing demographic burden on the economy. In the short term, growth in female and male old-age dependency ratios drives up life and non-life insurance density.

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“…He has provided a comprehensive view of government risk management and also put forth a set of policy suggestions for integrating risk management and increasing risk reduction measures and planning. Chang and Berdiev (2013) examined the relationship between natural disasters, political risk and insurance market development in a panel of 39 countries over the period 1984-2009 using a dynamic panel two-step system generalised method of moments model. They have established that that the incidences of natural disasters and deaths caused by natural disasters lead to greater total insurance, as well as life insurance and non-life insurance consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimating the maximum probable annual losses due to flooding

Ismail¹,

Awwad²

2019

Afr. J. Bus. Manage.

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This paper aims to estimate the maximum probable annual losses to the risks of flooding. While examining the frequency of the event, it was observed that, at least one accident occurred periodically, which resulted in maximum losses. This is an empirical study and it is based on maximum losses due to floods per year, as obtained from the International Disaster Database and Munich Re. The analysis is based on 66 extreme flood events in the world between 1906 and 2015. The complementary risk method has been used in deriving a mixed probability distribution, which expresses the number of floods and the maximum losses realized, where zero-truncated Poisson distribution is used for frequency distribution and Last order Weibull distribution for losses. The maximum of flood losses was fitted with compound truncated Poisson-Weibull distribution. Probabilities have been calculated for extreme flood losses, which are less than specific descriptive measures, and expected values have been calculated for extreme flood losses, which exceed the specific descriptive measures. The results from the study indicate that the maximum probable losses are greater than the maximum actual losses. This paper contributes to the risk of extreme floods pricing; and it also helps the governments of the countries in calculating the financial reserves to cover the extreme flood losses.

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Natural Disasters, Political Risk and Insurance Market Development

Cited by 23 publications

References 65 publications

Oil prices and geopolitical risks: What implications are offered via multi-domain investigations?

Oil prices and geopolitical risks: What implications are offered via multi-domain investigations?

The Impact of Demographic Burden on Insurance Density

Estimating the maximum probable annual losses due to flooding

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