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Article
Publication date: 1 February 2004

SIAMAK DANESHVARAN and ROBERT E. MORDEN

The insurance industry, in general, accepts large risks due to the combined severity and frequency of catastrophic events; further, these risks are poorly defined given the small…

167

Abstract

The insurance industry, in general, accepts large risks due to the combined severity and frequency of catastrophic events; further, these risks are poorly defined given the small amount of data available for extreme events. It is important for the equitable transfer of risk to understand and quantify this risk as accurately as possible. As this risk is propagated to the capital markets, more and more parties will be exposed. An important part of pricing insurance‐linked securities (ILS) is quantifying the uncertainties existing in the physical parameters of the catastrophe models, including both the hazard and damage models. Given the amount of reliable data (1945 till present) on important storm parameters such as central pressure drop, radius to maximum winds, and non‐stationarity of the occurrence rate, moments estimated for these parameters are not highly reliable and knowledge uncertainty must be considered. Also, the engineering damage model for a given class of building in a large portfolio is subject to uncertainty associated with the quality of the buildings. A sample portfolio is used to demonstrate the impact of these knowledge uncertainties. Uncertainties associated with variability of statistics on central pressure drop, occurrence rate, and building quality were estimated and later propagated through a tropical cyclone catastrophe model to quantify the uncertainty of PML results. Finally their effect on the pricing of a typical insurance‐linked security (ILS) was estimated. Statistics of spread over LIBOR given different bond ratings/probability of attachment are presented using a pricing model (Lane [2000]). For a typical ILS, a relatively large coefficient of variation for both probability of attachment and spread over LIBOR was observed. This in turn leads to a rather large price uncertainty for a typical layer and may explain why rational investors expect a higher return for assuming catastrophe risk. The results hold independent of pricing model used. The objective of this study is to quantify this uncertainty for a simple call option and demonstrate its effect on pricing.

Details

The Journal of Risk Finance, vol. 5 no. 2
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 24 February 2012

Siamak Daneshvaran and Maryam Haji

In general, the insurance industry accepts large risks due to the frequency and severity of extreme events. Because of the short record on hazard data for such events, a large…

220

Abstract

Purpose

In general, the insurance industry accepts large risks due to the frequency and severity of extreme events. Because of the short record on hazard data for such events, a large amount of uncertainty has to be dealt with. Given this large uncertainty it is important to better quantify the hazard parameters that are defined as inputs to the catastrophe models. The purpose of this paper is to evaluate the hurricane risk from loss point of view in the USA for both long‐term and warm phase conditions using a simulation‐based stochastic model.

Design/methodology/approach

A Poisson process is used to simulate the occurrence of events for both conditions. The generated event‐sets were used along with vulnerability and cost models to estimate the loss to an insurance industry portfolio. The paper discusses the statistics of events categorized by the Saffir‐Simpson Hurricane Wind Scale, annualized and return period losses and compares the results for both assumed long‐term and warm phase climate states.

Findings

The analysis shows that the population of landfall data for the two climate conditions is not statistically different. However, if we accept that a difference in the frequency of landfall occurrence between the two assumptions exists, the increase in average annual loss is about 17 per cent.

Originality/value

This paper provides insights to the difference between the two states of atmosphere from the point of view of insured losses for hurricanes and is one of the first papers that offers conclusion on the uncertainty associated with the warm phase data.

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Article
Publication date: 1 January 2013

Siamak Daneshvaran and Maryam Haji

A reliable forecast of hurricane activity in the Atlantic Basin has the potential to help mitigate the economic losses caused by hurricanes. One of the difficult problems is to…

792

Abstract

Purpose

A reliable forecast of hurricane activity in the Atlantic Basin has the potential to help mitigate the economic losses caused by hurricanes. One of the difficult problems is to make reasonable annual forecast of catastrophe losses based on the short record of historical observations. Atmospheric conditions tend to influence tropical cyclone development. Considering the complex interactions among climatological factors, prediction of future hurricane activity is challenging. In this study, the authors are attempting to predict the number of Atlantic hurricanes for a given year based on two different approaches.

Design/methodology/approach

In part I, an autoregressive integrated moving average (ARIMA) is used to model a long‐run behavior of Atlantic hurricane frequency. The authors present a comparison of CSU's forecast with ARIMA model. Part II focuses on the relationship between the climate signals and hurricane activity and introduces a new approach in including climate indices into the prediction model. In this part, principal components analysis (PCA) is used to identify possible patterns in historical data based on six climate indices measured prior to hurricane season. The objective is to reduce the data set to a smaller set while most of the variability observed in the real data is captured. The variances observed in an orthogonal system indicate the order of contribution of each mode shape.

Findings

Results from part I suggest that CSU's forecast model, in general, is superior to results obtained by ARIMA. In part II, the correlation between mode (shapes) and the number of Atlantic hurricanes per year is examined. The resulting relationships show that, for the time interval of 1990 through 2011, PCA‐based approach provides better estimates compared to CSU's forecast.

Originality/value

The paper presents a unique prediction approach which is simple, relatively accurate and easy to apply. The results of this study show that complex statistical analyses/models do not necessarily provide better forecasts.

Details

The Journal of Risk Finance, vol. 14 no. 1
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 6 March 2007

Siamak Daneshvaran and Robert E. Morden

Perils of tornado and hail cause large amounts of loss every year. Based on the data provided by Property Claims Services, since 1949, tornado, hail and straight‐line‐wind losses…

986

Abstract

Purpose

Perils of tornado and hail cause large amounts of loss every year. Based on the data provided by Property Claims Services, since 1949, tornado, hail and straight‐line‐wind losses account for more than 40 percent of total natural losses in the USA. Given the high frequency of tornado and damaging hail in the continental USA, quantifying these risks will be an important advancement in pricing them for insurance/reinsurance purposes. In the absence of a realistic physical model, which would look at these perils on a cluster/outbreak basis, it is not possible to underwrite these risks effectively. The purpose of this paper is to focus on the tornado risk.

Design/methodology/approach

A tornado wind‐field model is developed based on the model used by Wen and Ang. The model is calibrated to the specifications given in the Fujita intensity scale. To estimate the tornado hazard, a historical database is generated and de‐trended using the information provided by Storm Prediction Center along with the dataset given by Grazulis. This new historical database together with a reinsurance timeframe criterion in mind was used to define outbreaks. These outbreaks are used in a Monte‐Carlo simulation process to generate a large number of outbreaks representing 35,000 years of simulated data. This event‐set is used to estimate spatial frequency contours and loss analyses.

Findings

The results focus on the spatial frequency of occurrence of tornadoes in the USA. The losses are tallied using multiple occurrences of tornado and/or hail per outbreak. The distribution of loss, both on per occurrence and on aggregate basis, are discussed.

Originality/value

This paper is believed to be the first one to use a tornado wind‐field model, outbreak model, and vulnerability models, which estimate both spatial distribution of hazard and location‐based distribution of losses. Estimation of losses due to hail is also provided.

Details

The Journal of Risk Finance, vol. 8 no. 2
Type: Research Article
ISSN: 1526-5943

Keywords

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Article
Publication date: 24 February 2012

Siamak Daneshvaran and Maryam Haji

By reviewing recent literature, it is noticeable that considerable attention has been given to the relationship between all Atlantic hurricanes and those that make landfall in the…

321

Abstract

Purpose

By reviewing recent literature, it is noticeable that considerable attention has been given to the relationship between all Atlantic hurricanes and those that make landfall in the USA. However, less research has been done regarding landfall frequency and identifying spatial areas that are statistically more likely to produce landfalling hurricanes. The purpose of this paper is to provide a better prediction method for US landfalling hurricanes.

Design/methodology/approach

This work is based on the hypothesis that landfall variations along the US coast can be better explained in terms of hurricane origination points over more susceptible areas on the North Atlantic Ocean. Simulation techniques are used to spatially quantify the landfall probability.

Findings

Results indicate the existence of a landfall corridor in the Atlantic Basin, which explains some of the variances observed in the landfall process. Two different hypotheses of climate are examined. A long‐term assumption is based on the historical data from 1940 to 2010. The second assumption is based on the Atlantic Multidecadal Oscillation. Since 1995, we are in a warm phase and we assume that sea surface temperatures remain warmer than the long‐term average over the next several years. Results indicate that the average increase on landfall frequency is about 13 per cent.

Originality/value

This paper is the first paper that introduces the concept of landfall origination corridor. It spatially identifies the differences between long term and warm phase of the atmosphere in terms of US landfall occurrence using hurricane origination points.

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Article
Publication date: 28 January 2014

Nadine Gatzert

132

Abstract

Details

The Journal of Risk Finance, vol. 15 no. 1
Type: Research Article
ISSN: 1526-5943

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