The Effect of Taxes on the Location of Property-Casualty Insurance Firms

States levy insurance premium taxes, which are essentially gross receipt taxes on premiums. An unusual characteristic of insurance premium taxes is that in each state in which an insurance company writes premiums, the firm pays the higher of the tax rate in the state in which the company is domiciled and the state in which the policy is written. Thus, the choice of location has a significant effect on the firm's tax liability. Using firm-level data for the property-casualty (P-C) insurance industry, we calculate the firm-specific tax rate for each P-C firm for every possible state of domicile. We estimate conditional logistic models to analyze the effect of insurance premium taxes on the choice of the state of domicile of existing and relocated firms. We find robust evidence of a small, negative, and statistically significant effect of these taxes on the choice of the state of domicile.     

Adverse Selection and the Opaqueness of Insurers

While adverse selection problems between insureds and insurers are well known to insurance researchers, few explore adverse selection in the insurance industry from a capital markets perspective. This study examines adverse selection in the quoted prices of insurers' common stocks with a particular focus on the opacity of both asset portfolios and underwriting liabilities. We find that more opaque underwriting lines result in greater adverse selection costs for property-casualty (P-C) insurers. A similar effect is not apparent for life-health (L-H) insurers and we find no effect of asset opaqueness on adverse selection for either L-H or P-C insurers.     

It’s About Time: An Examination of Loss Reserve Development Time Horizons

A rich body of academic research has addressed the question of earnings management in the property-casualty insurance industry via manipulation of loss reserve estimates. This study analyzes the variability of reserve estimates at different development horizons to determine whether the predominant practice of relying on five years of development is appropriate. We examine two common measures of reserve estimation error, calendar year development and accident year development, and compare and contrast the two approaches. We also consider the appropriateness of the common practice of aggregating lines of business. After examining reserve development patterns for each of the major lines of business, we conclude that the appropriate development horizon to adequately establish ultimate liability may be longer than the current maximum reported horizon of 10 years found in Schedule P for most lines of business, including the aggregate reserves. Although longer-term development horizons are necessary to establish insurers’ ultimate liability, relatively short-term development horizons may be more appropriate when attempting to identify deliberate manipulations or to assess solvency risk, where the short-term variations are the primary object of interest. Ultimately, this article investigates the degree to which methodology originally developed for estimating loss reserve errors is appropriate today, in particular, relative to current data availability.     

“The Future of Risk Classification in the Age of Predictive DNA-Based Testing”, Donald C. Chambers,January 1999

Abstract

The correlation among multiple lines of business plays an important role in quantifying the uncertainty of loss reserves for insurance portfolios. To accommodate correlation, most multivariate loss-reserving methods focus on the pairwise association between corresponding cells in multiple run-off triangles. However, such practice usually relies on the independence assumption across accident years and ignores the calendar year effects that could affect all open claims simultaneously and induce dependencies among loss triangles. To address this issue, we study a Bayesian log-normal model in the prediction of outstanding claims for dependent lines of business. In addition to the pairwise correlation, our method allows for an explicit examination of the correlation due to common calendar year effects. Further, different specifications of the calendar year trend are considered to reflect valuation actuaries’ prior knowledge of claim development. In a case study, we analyze an insurance portfolio of personal and commercial auto lines from a major U.S. property-casualty insurer. It is shown that the incorporation of calendar year effects improves model fit significantly, though it contributes substantively to the predictive variability. The availability of the realizations of predicted claims permits us to perform a retrospective test, which suggests that extra prediction uncertainty is indispensable in modern risk management practices.     

The Association between External Monitoring and Earnings Management in the Property-Casualty Insurance Industry

This paper examines the association between external monitoring and earnings management by property-casualty insurers. We extend previous work by Petroni and Beasley (1996) by expanding the set of external monitors to include both auditors and actuaries. We investigate whether certain auditor-actuary pairs are associated with less understatement of the loss reserve account by financially struggling insurers. Our data consist of loss adjustments reported by 465 property-casualty insurers for reserves established in 1993. The results indicate that under-reserving by weak insurers is essentially eliminated when the firm uses auditors and actuaries that are both from Big Six accounting firms. In contrast, non-Big Six actuaries have less impact on under-reserving by weak insurers. Our results suggest that the quality usually associated with Big Six auditors falls when the audit firm relies on third party actuaries to evaluate the loss reserve estimates of struggling insurance clients. We conjecture that Big Six actuaries insist on more conservative loss reserve levels because, compared to actuarial consulting firms, they are more attuned to the liability exposure of the auditor.     

Robust estimation of intraweek periodicity in volatility and jump detection

Opening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We show that price jumps cause a large bias in the classical periodicity estimators and propose robust alternatives. We find that accounting for periodicity greatly improves the accuracy of intraday jump detection methods. It increases the power to detect the relatively small jumps occurring at times for which volatility is periodically low and reduces the number of spurious jump detections at times of periodically high volatility. We use the series of detected jumps to estimate robustly the long memory parameter of the squared EUR/USD, GBP/USD and YEN/USD returns.     

Does boardroom nationality affect the performance of UK insurers?

Drawing on the board capital literature, we use a panel data design to investigate the effect of boardroom nationality on the profitability and solvency of property-casualty insurers operating in the United Kingdom (UK). We find that boardroom nationality influences corporate outcomes depending on the financial aspects being measured. For example, North American directors are linked with profitable outcomes, while European directors tend to be associated with better solvency. This reflects differences between the shareholder value corporate culture in North America and stakeholder approaches more common in Europe. Our results could help insurers, regulators, and others (e.g., investors) to better understand the potential performance implications of the appointment of directors of different nationality.     

Sarmanov Family of Bivariate Distributions for Multivariate Loss Reserving Analysis

The correlation among multiple lines of business plays a critical role in aggregating claims and thus determining loss reserves for an insurance portfolio. We show that the Sarmanov family of bivariate distributions is a convenient choice to capture the dependencies introduced by various sources, including the common calendar year, accident year, and development period effects. The density of the bivariate Sarmanov distributions with different marginals can be expressed as a linear combination of products of independent marginal densities. This pseudo-conjugate property greatly reduces the complexity of posterior computations. In a case study, we analyze an insurance portfolio of personal and commercial auto lines from a major U.S. property-casualty insurer.     

Using self-organizing maps to adjust for intra-day seasonality

The existence of an intra-day seasonality component in financial market variables (volatility, volume, activity, etc.) has been highlighted in many previous studies. To remove this cyclical component from raw data, many researchers use the intra-day average observations model (IAOM) and/or some smoothing techniques (e.g. the kernel method). When the seasonality is related to the first moment (the conditional expectation) and involves only a deterministic component, the IAOM method succeeds in estimating the periodicity almost perfectly. However, when seasonality affects the first or the second moment (the conditional variance) of the data and contains both deterministic and stochastic components, both IAOM and the kernel method fail to capture it. We introduce self-organizing maps (SOM) as a solution. SOM are based on neural network learning and nonlinear projections. Their flexibility allows seasonality to be captured even in the presence of stochastic cycles.     

Intraday periodicity adjustments of transaction duration and their effects on high-frequency volatility estimation

We study two methods of adjusting for intraday periodicity of high-frequency financial data: the well-known Duration Adjustment (DA) method and the recently proposed Time Transformation (TT) method (Wu (2012)). We examine the effects of these adjustments on the estimation of intraday volatility using the Autoregressive Conditional Duration-Integrated Conditional Variance (ACD-ICV) method of Tse and Yang (2012). We find that daily volatility estimates are not sensitive to intraday periodicity adjustment. However, intraday volatility is found to have a weaker U-shaped volatility smile and a biased trough if intraday periodicity adjustment is not applied. In addition, adjustment taking account of trades with zero duration (multiple trades at the same time stamp) results in deeper intraday volatility smile.     

The impact of monitor choice on insurer loss reserves

This paper examines the association between monitoring and earnings management by property-casualty insurers. Prior literature has evaluated the impact of auditors and actuaries on insurer reserving. We extend this work by considering the nonrandom nature of monitor assignment. We model the insurer decisions regarding choice of auditor and actuary jointly using a Heckman selection model. Consistent with prior literature, we account for potential loss reserving incentives that may confound these decisions. We find that the use of internal actuaries is significantly related to higher reserve errors, but this is reduced, but not fully offset, when the internal actuary is an officer of the insurer. We find lower reserve error for auditors from a Big N firm. However, the use of an auditor and actuary from the same Big N firm is significantly related to higher reserve errors.     

Analyst Earnings Forecasts for Publicly Traded Insurance Companies

Several trends in the insurance and financial services industry, including demutualizationconsolidation, and deregulation, have attracted increasing attention from investors and financial analysts. This paper investigates the accuracy of the earnings forecasts of financial analysts for insurance companies. Our empirical results indicate that analyst forecasts outperform random walk time-series forecasts. Furthermore, we find that both disagreement over earnings forecasts among analysts and the relative forecasting error in the mean forecasts is smaller for life insurers than for property-casualty insurers, whereas the relative errors for forecasts for multiple-line insurers are in between the two. Forecasting error is a negative function of firm size and the number of analysts who are following a company, and is a positive function of the disagreement among analysts.Analyst forecasts have a timing advantage over the random walk model. Our results also suggest that the fair value reporting requirement (SFAS 115), which has been in effect since 1994, has enhanced the accuracy of analyst forecasts. The SFAS 115 has improved the superiority of analyst forecasts over the random walk forecasts for life insurers, but not for property-casualty insurers, and there is a weak improvement for multiple-line insurers. JEL Classification: G15     

Versicherungszyklen in der deutschen KFZ-Versicherung

In this paper we analyze underwriting cycles in the German motor insurance industries for the last 50 years. A special emphasis is laid on the so-called aggregation bias that arises when data from different lines of business is merged in a total market statistic. In literature, underwriting cycles were found for many international markets and for numerous lines of business. We find underwriting cycles for the German motor insurance with lengths between six and ten years, which have been increasing for the last years. Our empirical evidence on cycle lengths in German motor insurance fits well into the context of international comparative studies on underwriting cycles.     

Exchange rate volatility,macroeconomic announcements and the choice of intraday periodicity filtering method

Filtering out the intraday periodicity of volatility is crucial for using high frequency data in econometric analysis. This paper studies the effects of filtering on statistical inference as regards the impact of news on exchange rate volatility. The properties of different methods are studied using a five-minute frequency EUR/USD data set and simulated returns. The simulation results suggest that all the methods tend to produce downward-biased estimates of news coefficients, some more biased than others. The study supports the Flexible Fourier Form method as the best for seasonality filtering.     

The Financial Modeling of Property-Casualty Insurance Companies

This paper describes a financial model currently being used by a major U.S. multiline property-casualty insurer. The model, which was first developed for solvency monitoring purposes, is now being employed for a variety of internal management purposes as well, including (1) the allocation of equity to corporate units, thereby allowing measurements of profitability by business segment and policy year, as well as analysis of the progression of “free surplus,” (2) the analysis of major risks–such as inflation risks, interest rate risks, and reserving risks–that have heretofore been difficult to quantify, and (3) consideration of varying scenarios on the company’s financial performance, both of macroeconomic conditions as well as of the insurance environment.

Many aspects of financial modeling do not differ significantly between life and property-casualty insurers, and these are not discussed in the paper. Rather, the paper focuses on the following topics:

1. Surplus allocation and profitability: how economic surplus and the returns on this surplus are determined by line of business, separately for new business and for the runoff of existing business, and how the progression of free surplus is viewed.

2. Multifaceted risks: how to model risks that affect multiple components of the insurer’s operations, such as economic risks and financial risks. The multiple effects of macroeconomic conditions and changing inflation rates on workers’ compensation claim frequencies and severities complicate the basic interest rate path modeling of life insurance products and annuity contracts.

3. Scenario building: how to construct scenarios of macroeconomic conditions or industry cyclical movements to test the resilience of the company to changing external conditions.     

Regulator's Determination of Return on Equity in the Absence of Public Firms: The Case of Automobile Insurance in Ontario

In a regulated market, such as automobile insurance (AI), regulators set the return on equity that insurers are allowed to achieve. Most insurers are engaged in a variety of insurance lines of business, and thus the full information beta methodology (FIB) is commonly employed to estimate the AI beta. The FIB uses two steps: first, the beta of each insurer is estimated, and then the beta of each line of business is estimated, as the beta of an insurer is a weighted average of the betas of the lines of business. When there are a sufficient number of public companies, company and market returns are used. Otherwise, researchers have resorted to using accounting data in the FIB. Theoretically, the two steps are not separable and the estimation should be done with one step. We introduce the one‐step methodology in our article. The one‐step and two‐step methodologies are compared empirically for the Ontario market of AI. Insurers in Ontario are predominantly private companies; thus, accounting data are used to estimate the AI beta. We show that a significant bias is introduced by the traditional, two‐step FIB methodology in estimating the betas for different lines of business, while insurers’ betas are very similar under both methods. This has a significant application to the estimation of betas of “pure players” in classic corporate finance. It implies that their betas and hence the resulting, required rates of return used in the net present value calculations should be estimated based on the one‐step method that we develop in this article.     

The Theory of Optimal Stochastic Control as Applied to Insurance Underwriting Cycles

We use the theories of optimal stochastic control and engineering process control to analyze the well-known phenomenon of insurance underwriting cycles in continuous time. We show in a continuous time framework that underwriting cycles can be explained with a model where premiums are set rationally, but where there are various reporting and regulatory lags. We find that the observed cycle length depends on the length of these underlying lags. Our result can be seen as consistent with previous empirical work showing underwriting cycles varying across countries and lines of insurance. In the event that no lags exist, our result is also consistent with more recent literature suggesting that insurance cycles may not exist.     

International business cycles: What are the facts?

Modern business cycle theory involves developing models that explain stylized facts. For this strategy to be successful, these facts should be well established. In this paper, we focus on the stylized facts of international business cycles. We use the generalized method of moments and quarterly data from twenty industrialized countries to estimate and test hypotheses concerning pairwise cross-country correlations of macroeconomic aggregates. A remarkable common feature emerges: these correlations are mostly positive, not very high and of a similar order of magnitude. The most important discrepancy with the theory is the low cross-country correlation of consumption.     

Are female CEOs associated with lower insolvency risk? Evidence from the US property-casualty insurance industry

This paper investigates the relationship between female CEOs and insolvency risk of US property-casualty insurance companies. We show that female CEOs are associated with lower insurer insolvency propensity, higher z-score, and lower standard deviation of return on assets. These findings are robust to alternative econometric specifications to address potential endogeneity concerns and self-selection issues, including propensity score matching, the instrumental variable approach, and the difference-in-difference approach. Furthermore, we find that the impact of female CEOs on insurer insolvency risk is moderated by firm capitalization, the presence of female directors, and political conservatism of insurers' home states.     

Idea mining for web-based weak signal detection

We investigate the impact of idea mining filtering on web-based weak signal detection to improve strategic decision making. Existing approaches for identifying weak signals in strategic decision making use environmental scanning procedures based on standard filtering algorithms. These algorithms discard patterns with low information content; however, they are not able to discard patterns with low relevance to a given strategic problem. Idea mining is proposed as an algorithm that identifies relevant textual patterns from documents or websites to solve a given (strategic) problem. Thus, it enables to estimate patterns’ relevance to the given strategic problem. The provided new methodology that combines weak signal analysis and idea mining is in contrast to existing methodologies. In a case study, a web-based scanning procedure is implemented to identify textual internet data in the field of self-sufficient energy supply. Idea mining is applied for filtering and weak signals are identified based on the proposed approach. The proposed approach is compared to a further – already evaluated – approach processed without using idea mining. The results show that idea mining filtering improves quality of weak signal analysis. This supports decision makers by providing early and suggestive signals of potentially emerging trends, even with only little expressive strength.