Underwriting Cycles in Property and Liability Insurance: An Empirical Analysis of Industry and By-Line Data

Fung, Hung‐Gay; Lai, Gene C.; Patterson, Gary A.; Witt, Robert C.

doi:10.2307/253802

Cited by 52 publications

(47 citation statements)

References 22 publications

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“…Moreover, our results clearly demonstrate that the coefficient of β 1 , that is the attribution to net incurred premiums due to losses, asserts the dominant impact on the premiums for the accident and liability insurance industries, justifying the case of no perfect foresight in the insurance industry (Fung at al., 1998). Effectively the provided evidence partially supports the 'rational expectations' hypotheses in the long run, according to which expected claims/losses are used to set premiums (Mamatzakis and Staikuras, 2006).…”

Section: Estimation Of Betas Over the Full Samplementioning

confidence: 56%

“…However, the drawback of using forecasts is the underlying measurement errors (Niehaus and Terry, 1993) To account for uncertainty we opt for the conditionally expected volatility of claims (V c ) as derived from appropriate time series modelling. In the literature, it is quoted that there exist a positive relationship between uncertainty and premiums (Lai andWitt 1990, 1992). Moreover, uncertainty about losses would increase the variance of insurance premiums.…”

Section: Sharpe Returns-based Style Analysis For Insurancementioning

confidence: 99%

“…These findings highlight some differences and similarities across industries and are in line with Lai and Witt (1990) and (1992), though Niehaus and Terry (1993) has highlighted the sensitivity of the results to measurement errors. Further, for the motor and property industry, that together captures 65% of the whole market in 2005, volatility in losses is of high importance due to the significance of risks attached to the general economic conditions for these industries along the lines first proposed by Fung et al (1998) for the property insurance industry in the US. In particular, the oil-crisis present in our time-span could matter for the motor industry, thus the high share of volatility, whilst the property industry is sensitive to country specific and/or global macroeconomic instabilities that are directly linked to mortgage default.…”

Section: Estimation Of Betas Over the Full Samplementioning

confidence: 99%

“…In addition, net premiums are also attributed to the conditional expectations formed over both claims and expenses Venezian (1985), Fung, Lai, Patterson and Witt (1998), while to account for possible uncertainty over the market we opt for the conditionally expected variance of losses.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Return attribution analysis of the UK insurance portfolios

Christodoulakis

Mamatzakis

2009

Ann Finance

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We examine the attribution of premium growth rates for the five main insurance sectors of the United Kingdom for the period 1969-2005; in particular, Property, Motor, Pecuniary, Health & Accident, and Liability. In each sector, the growth rates of aggregate insurance premiums are viewed as portfolio returns which we attribute to a number of factors such as losses, expenses, their expectations and uncertainty as well as market power, using the Sharpe (1988, 1992) Style Analysis. Our estimation method differs from the standard least squares practice which does not provide confidence intervals for style betas and adopts a Bayesian approach, resulting in a robust estimate of the entire empirical distribution of each beta coefficients over the long run for the full sample. We also perform a dynamic analysis of robust estimation for a rolling window of seven overlapping samples. Our empirical findings show that there are some main differences across industries as far as the shares attributed to the underlying factors in the long run. Rolling regressions assist us to identify the variability of these shares over time, but also across industries.

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Section: Estimation Of Betas Over the Full Samplementioning

confidence: 56%

Section: Sharpe Returns-based Style Analysis For Insurancementioning

confidence: 99%

Section: Estimation Of Betas Over the Full Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Return attribution analysis of the UK insurance portfolios

Christodoulakis

Mamatzakis

2009

Ann Finance

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“…2 The presented autoregressive process is the most widespread approach to model the cyclical pattern of premium rate level over time; see Venezian (1985), Niehaus and Terry (1993), Daykin, Pentikäinen, and Pesonen (1994), Lamm-Tennant and Weiss (1997), Fung et al (1998), Chen, Wong, and Lee (1999), and Meier (2006) for an overview. An alternative in the context of DFA is to use Markov processes and transition probabilities as done in Eling, Parnitzke, andSchmeiser (2008) andD'Arcy et al (1998).…”

Section: Model Frameworkmentioning

confidence: 99%

Modeling and Management of Nonlinear Dependencies - Copulas in Dynamic Financial Analysis

Eling

Toplek

2009

SSRN Journal

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We study the influence of nonlinear dependencies on a non-life insurer's risk and return profile. To achieve this, we integrate several copula models in a dynamic financial analysis framework and conduct numerical tests. We also test risk management strategies in response to adverse outcomes. Nonlinear dependencies have a crucial influence on the insurer's risk profile that can hardly be affected by the analyzed management strategies. We find large differences in risk assessment for the ruin probability and for the expected policyholder deficit. This has important implications for insurers, regulators, and rating agencies that use these measures as a foundation for internal risk models, capital standards, and ratings.

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Dynamics of underwriting profits in the US market: Payout patterns and regulation effects

2021

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The empirical investigation of the underwriting profits of six major insurance lines in the US market during 1956–2016 suggests that the data generating process of underwriting profits follows a first‐order autoregressive process for less regulated business lines, whereas highly regulated business lines follow a second‐order autoregressive process. This study verified the long‐run relationships between risk‐free interest rates and underwriting profits for individual lines. Specifically, the long‐run sensitivities of interest rates for less regulated long‐tail lines were greater than those for other lines. We also found that the length of tail acted as a crucial part of the feedback dynamics. The shorter the tails, the greater the tendency to go back to equilibrium, while the longer the tails, the smaller the tendency. In addition, an analysis of cumulative dynamic multipliers showed that the adjustment patterns for the lagged effects of interest rates varied for each business line. The pricing strategies and the safety loading activities of different individual lines were also discussed in this study.

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Underwriting Cycles in Property and Liability Insurance: An Empirical Analysis of Industry and By-Line Data

Cited by 52 publications

References 22 publications

Return attribution analysis of the UK insurance portfolios

Return attribution analysis of the UK insurance portfolios

Modeling and Management of Nonlinear Dependencies - Copulas in Dynamic Financial Analysis

Dynamics of underwriting profits in the US market: Payout patterns and regulation effects

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