...is significantly related to interest rate volatility. We investigate the interest rate sensitivity of monthly stock returns of life insurers based on generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) model. We examine three different portfolios (equally weighted, risk-based, and size-based) with binary variables to explicitly account for varying interest rate strategies adopted by the Federal Reserve System. Results based on data for the period 1975 through 2000 indicate that life insurer equity values are sensitive to long-term interest rates and that interest sensitivity varies across subperiods and across risk-based and size-based portfolios. The results complement insolvency research that links insurer financial performance to changes in interest rates.

INTRODUCTION

Interest rate risk is an important concern for financial firms, and a large body of literature shows a strong relation between the stock returns of financial institutions and interest rates. Much research has investigated the interest sensitivity of commercial bank equity returns (e.g., Lloyd and Shick, 1977; Lynge and Zumwalt, 1980; Chance and Lane, 1980; Flannery and James, 1984; Bae, 1990; Kwan, 1991; Scott and Peterson, 1986; Kane and Unal, 1988, 1990; Elyasiani and Mansur, 1998, 2003). However, excepting the early works of Scott and Peterson (1986) and Bae (1990), little attention has been paid to the interest sensitivity of life insurer equity returns. Santomero and Babbel (1997) state that insurers have a sense of urgency to apply the tools of asset/liability management to manage interest rate risk. Life insurer equity returns that vary with changes in interest rates suggest that the market is accounting for their interest rate exposure. Thus, we investigate the interest rate sensitivities of monthly stock returns of life insurers.

We make four primary contributions. First, we evaluate the systematic factors that generate life insurer common stock returns by utilizing a GARCH-M (generalized autoregressive conditionally heteroskedastic in the mean) model. The model includes several basic capital asset pricing models as its special cases and allows a test of their validity. Although previous research has employed the GARCH-M methodology (Elyasiani and Mansur, 1998) in the analysis of the interest rate sensitivity of depository institution stock returns, to our knowledge the current study is the first application of a GARCH-M model in the analysis of the interest rate sensitivity of life insurer stock returns. Second, the study tests if the interest rate sensitivity of life insurers stock returns and overall stock return volatility of these firms remain constant over time, when the Fed adopts different interest rate strategies, and interest rate volatility changes as a result. Third, the study employs a comprehensive data set for the period from 1975 through 2000. Finally, to examine firm level variation in interest rate sensitivity, the sample is disaggregated and portfolios are formed based on high-beta and low-beta firms as well as small, medium, and large insurers. (1)

Our results provide several insights. First, consistent with research on insurer financial strength (e.g., Browne, Carson, and Hoyt, 1999), we find the equity values of life insurers to be sensitive to long-term interest rates. Second, as in previous studies for depository institutions (e.g., Scott and Peterson, 1986), the stock returns of life insurers are negatively related to changes in interest rates. Third, similar to studies for other financial institutions (e.g., Yourougou, 1990), we find that the interest rate sensitivity of life insurer stock returns varies across the monetary policy regimes. Finally, we find that life insurers with low market betas exhibit significant interest rate sensitivity whereas those with high market beta do not, and that equity returns of smaller life insurers are more sensitive to movements in the stock market than to movements in interest rates.

The remainder of the article is organized as follows. The next section reviews the literature regarding interest rate risk of life insurers and the equity returns model for financial institutions. We then describe our sample, data, and methodology. Next we analyze the empirical results, and the final section summarizes and concludes.

REVIEW OF RELATED LITERATURE

Insurers issue stochastic debt instruments for which the amount and timing of loss payments (contingent claims) are unknown at policy issuance, and they invest the proceeds to maximize the risk-adjusted return on capital. By effectively "borrowing" from policy owners, insurers lever ownership capital. Interest rate risk, defined as the degree of exposure, or elasticity, of insurer net worth to changes in the interest rate, is important to life insurers for a number of reasons, as discussed, e.g., by Staking and Babbel (1995) and Briys and Varenne (1997). The importance of interest rate risk is based on (1) the investment portfolio of the typical highly leveraged insurer is concentrated in long-term fixed-income securities; (2) life insurer performance is negatively related to changes in interest rates (Browne, Carson, and Hoyt, 1999, 2001); (3) for insurers whose duration of assets exceeds that of their liabilities, rising interest rates erode the value of surplus, leading to increased leverage and a greater probability of ruin; (4) higher leverage increases the insurer's cost of capital (Cummins and Lamm-Tennant, 1994); and (5) interest rate risk leads insurers to take steps to match asset-liability durations with futures and options (hedge) in order to hedge to protect franchise value (Hoyt, 1989b; Colquitt and Hoyt, 1997).

It is fruitful to explore whether the equity returns of life insurers vary in response to changes in market interest rates, since life insurer equity returns that vary with changes in interest rates would suggest that the market accounts for insurers' interest rate exposure. Interest rate sensitivity also reinforces the importance of asset-liability management (Santomero and Babbel, 1997; Panning, 1995) and dynamic financial analysis (D'Arcy et al., 1997) for insurers.

Research on the general relationship between interest rates and equity returns is extensive. Stone (1974) postulates a two-index market model that includes an interest rate for explaining equity returns of financial firms. Lloyd and Shick (1977) use a two-index market model and find that financial firms' stock values are sensitive to changes in interest rates. However, Chance and Lane (1980) do not find a relationship, whereas Lynge and Zumwalt (1980) do. Flannery and James (1984), Booth and Officer (1985), and Scott and Peterson (1986) all find interest rate sensitivity in the two-index market model for financial firms during an era of high volatility in market rates.

The works of Akella and Chen (1990), Brewer and Lee (1990), Choi, Elyasiani, and Kopecky (1992), Kane and Unal (1988,1990), Kwan (1991), Neuberger (1991), Wetmore and Brick (1994), and Yourougou (1990) all find that the interest rate dependency of financial stocks is time varying; interest rate sensitivity shifts according to economic conditions and monetary policy strategy (e.g., Brewer and Lee, 1990).

Maher (1997) finds that the time-varying interest rate sensitivity renders tests over long periods inconclusive. To address the time-varying nature of the stock return generating process for banks, Song (1994) employs an ARCH-type methodology. Elyasiani and Mansur (1998) go further by employing an extended GARCH-M model, which includes an interest rate in the mean and interest rate volatility as an argument in the volatility of the bank stock return generating process. Inclusion of the latter variable reveals that changing interest rate volatility, in turn, leads to changing volatility in financial firms' stock returns and varying expected risk premia. (2)

SAMPLE, DATA, AND METHODOLOGY

Data Description and Diagnostics

The sample consists of all (60) publicly traded insurance companies specializing in life insurance with available data, as described below. (3) Based on 1990 figures, insurers in the sample range in size from $20 million in assets to $89 billion in assets (as shown in Appendix A). Mean (median) assets for the sample equals $8 billion ($2 billion). Total assets of sample firms ($505 billion) in 1990 are approximately one-third of industry assets ($1.4 trillion) for the same year. The data for the study run from January 1975 to December 2000. Monthly return data for life insurers are obtained from the Center for Research in Securities Prices (CRSP) file. Monthly (versus daily) return data provide a longer historical period that better reflects long-term movements in volatility. Settlements and clearing delays are also less problematic with monthly data versus daily returns (Baillie and DeGennaro, 1990).

We follow the approach of Friend, Westerfield, and Granito (1978) and Harrington (1983) in using portfolio data versus individual security data. There is a trade-off between using individual firm data and portfolio data. When individual firm data are used, the noise is high and the results tend to be unduly influenced by individual random shocks. The use of portfolios does mask some of the detailed information provided by individual firm data but produces more reliable results as it washes out the noise. Given the trade-off between these approaches, a middle of the road approach is adopted here as a compromise. Following this strategy, we examine the sample in smaller subsamples sorted by risk (high-beta versus low-beta portfolios) and by asset size (smaller, medium, and large firm portfolios), as well as subsample periods based on changing monetary policy strategy. (4)

The data set for each time period includes each of the 60 life insurers (listed in Appendix A) that were in business during that particular period. It follows that the sample size and sample membership vary over time. The rationale for this sample selection procedure is to use all the company data available in each period, and thus to minimize survivor bias, and to maximize the membership in the sample, in order to improve estimator efficiency. (5) For the stock market index we employ the S&P 500 equity market index, obtained from CRSP database. The interest rate series, described below, is obtained from Ibbotson Associates (2002). Table 1 contains the descriptive statistics of life insurer stock returns. The summary statistics suggest that the data series is skewed and leptokurtic relative to the normal distribution. (6) Overall, these diagnostics suggest that a GARCH-type process is appropriate for modeling life insurer stock returns.

Model and Methodology

The degree of exposure of a life insurance company...

NOTE: All illustrations and photos have been removed from this article.




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