Finance

Since 2000, the trend in the insurance industry is greater reliance on competition and increased direction towards developing prudential regulation that encourages effective self-regulation of insurance firms following certain guidelines (OECD, 1998). Under this condition, competition requires insurance firms to effectively control risks as incentive to gaining more clients and investors and as part of accountability to existing customers and shareholders (Hubbard, 2009). Better risk management enhances the attractiveness of the insurance firm relative to competitors and the market.

In the context of self-regulation, value-at-risk (VAR) emerged as the standard measure in quantifying market risk (Manganelli  Engle, 2001). It quantifies market risk by providing a summary of the statistical estimation of portfolio losses (Linsmeier  Pearson, 1996). This makes VAR an important risk management tool for two reasons. One, it is standardized to imply widespread utility to insurance firms. The other is as a tool in measuring possible portfolio risks to support decision-making of various stakeholders including customers, investors, managers, and regulators.

Three most commonly used VAR methods serve as the standards recognized by industry and regulatory bodies. Insurance firms have three VAR options, which are the parametric method (variance-covariance), Monte Carlo Simulation, and historical simulation, each with its respective advantages and disadvantages (Minnich, 1998). Insurance firms need to determine the appropriate method depending on the key parameters considered in measuring portfolio risk (Christoffersen, 2003). These methods yield different risk measures. The method used by insurance firms has an impact on competitiveness and effective self-regulation.

Problem Statement
VAR developed in the banking industry and adopted by the insurance industry in managing portfolio risk. As a relatively new development in the insurance industry, it is important to determine the extent that insurance firms have adopted the three VAR methods and the extent that the methods employed by insurance firms supported their goal of measuring portfolio risk to support effective decision-making. While there are theoretical studies on the application and use of the VAR methods in the insurance industry, there is need for an empirical study on actual application and use given real competitive and regulatory contexts of portfolio risk.

Aim  Objectives of the Study
The aim of this study is to compare the extent of use of the three VAR methods by insurance firms and the extent of effectiveness in measuring portfolio risk to draw implications on competitiveness and self-regulation. To achieve this aim, the study considers the following objectives
To provide a theoretical comparison of the advantages and disadvantages in using the three VAR methods
To determine the VAR methods used by insurance firms in estimating portfolio risk
To complete a comparative assessment of the effectiveness of the three VAR methods in measuring the portfolio risk faced by insurance firms
To determine the issues faced by insurance firms in using the VAR methods
To draw implications of the VAR methods used on competitiveness and self-regulation of insurance firms.

Significance of the Study
The study would describe the experiences of insurance firms in applying the VAR methods for purposes of contributing to research gap on the adoption of the VAR by the insurance industry and to building best practices on portfolio risk management in the industry.

Brief Literature Review
Minnich (1998) considered the theoretical assumptions of the three VAR methods and summarized the resulting pros and cons in using one or the other of these methods as shown in Table 1 below. The assumptions of the VAR methods result to variances in measurement parameters and computation models. This in turn leads to different estimations of risk (Beder, 1995). Before selecting from the three methods, the following are important considerations (1) time horizon (2) confidence interval (3) data series (4) mappingselecting relevant risk factors and, (5) option valuation (Minnich, 1998, p. 41).
Table 1 Comparison of Pros and Cons of VAR Methods

Source (Minnich, 1998)
Manganelli and Engle (2001) compared the three VAR methods and found similarities and differences. The three methods adhere to similar structure by involving the following activities mark-to-market portfolio, estimate portfolio distribution, and compute portfolio VAR. The primary distinction between these methods is the means of estimating the possible shifts in portfolio value. Bohdalova (2007) conducted a theoretical study comparing the VAR methods in measuring financial risk, specifically of a portfolio of government bonds. Careful selection of the data used in the computation and the other parameters of the computation as well as care in the computation process itself reduces cost and computational time.

Lambadiaris et al. (2003) conducted an empirical study comparing the historical and Monte Carlo simulation methods as applied to stocks and bonds portfolio. The study employed data collected from the Greek stock and bonds industry. The results of the study showed two things. One is the Monte Carlo simulation as a better method in estimating stock portfolio risk because the historical method commits more than the necessary capital. There is no preferred method in measuring bond portfolio risk because of variance in superiority of the methods given different test samples and confidence levels. Kuester et al. (2006) compared the methods of estimating VAR using data sets of the NASDAQ Composite Index in the context of the existing regulatory context. The study found that the methods comply with regulatory requirements for adequacy. However, the methods have downsides and the study recommends the combination of methods for better results.

Methodology
The investigation is a quantitative study by requiring the comparative use of the three VAR methodologies in the insurance industry. The quantitative study involves the testing of a theory, model or hypothesis to derive numerical results (Creswell, 2003 Saunders et al., 2009). In the study, the VAR methods comprise the models for testing, in comparing the estimation of value-at-risk in the insurance industry. Data for use in the calculations will come from the U.S. insurance market and individual firms. The method of analysis is comparison. Analysis involves the calculation of VAR using the three methods for different portfolios in the insurance industry, for confidence levels of 99 percent and 95 percent, and for different periods.  The results would show comparative accuracy and utility of the three methods for insurance firms. A list of insurance firms together with the VAR method employed and the relative risk management performance based on industry data would be useful in analyzing data and deriving conclusions.

Summary  Conclusion
The aim of this study is to compare the extent of use of the three VAR methods by insurance firms and the extent of effectiveness in measuring portfolio risk to draw implications on competitiveness and self-regulation. There are previous studies comparing the utility of the VAR methods but there are limited empirical studies comparing these models in the context of the insurance industry. Using the quantitative method to compare the use of these models for portfolio risk estimation in the insurance industry would contribute to the research gap.