Since the advents of credit cards in the 1960s, lenders have used credit scoring, both application and behavioural scoring to monitor and control default risk. However in the last decade their objective has changed from minimising default rates to maximising profit. Lenders have recognized that operating decisions are crucial in determining how much profit is achieved from a card. This paper focuses on the most important decision in an operating policy: the management of credit limit. Soman and Cheema (2002) conducted a study on the use of credit limit policies in encouraging spending and found that the availability of additional credit does promote card usage in some consumers. Consumers assumed lenders have some sophisticated models, which was used to determine appropriate credit limits, but that is not the case in reality.
So how do lenders currently decide on what credit limit to offer a credit card customer? Most use subjective policies based on a risk/return matrix, i.e. they agree credit limits for each combination of risk band and average balance, which is considered a surrogate for the return to the lender from that customer. This approach is static in that it does not consider whether or how the customers default risk and profitability to the lender will change over time. Nor is there any model to guide what are the optimal credit limits to choose.
Consumer debt has traditionally been analyzed within the Fisher framework of shifting consumption between periods and smoothing consumption over the lifecycle. While the shifting of consumption to earlier periods via various debt instruments will necessarily have a negative impact on future consumption as debt is repaid with interest, the assumption is usually made that consumers will indeed over time successfully repay the debt acquired earlier in life. However, with the introduction of non-secured lines of credit with flexible payments i.e., credit cards the traditional patterns of consumption smoothing and debt repayment may no longer be the same for U.S. households. Unlike mortgages, student loans, auto loans, and other installment loans with which the U.S. population has a relatively long history, substantial credit card debt is a fairly recent phenomenon, and predictable debt accumulation and repayment trends have yet to be firmly established. Besides the flexible repayment feature of credit card debt, the possibility of default and bankruptcy poses further uncertainties about how consumers will repay their debt over the lifecycle.
To understand debt accumulation and repayment patterns over time and make reliable predictions, a lifecycle analysis on credit card debt and payoff rates is necessary. Due to data limitations, most previous studies analyzed consumer debt in a static or comparative static context. Using a synthetic cohort approach, this research incorporates a time dimension to provide evidence on credit card debt and payoff rates from a lifecycle perspective. The objectives of this study are: to empirically estimate the lifecycle profiles of credit card borrowing and repayment behavior for 14 different birth cohorts and compare the patterns with the implications of the simple lifecycle model, and to examine the financial, socioeconomic, demographic, and psychological determinants of credit card debt and payoff rates.
This paper solves numerically the intertemporal consumption and portfolio choice problem of an infinitely lived investor who faces a time varying equity premium. There is now considerable evidence that the excess return on stocks over Treasury bills is predictable (see Campbell 1987, Campbell and Shiller 1988, Fama and French 1988, 1989, Hodrick 1992, or the textbook treatment in Campbell, Lo, and MacKin lay 1997, Chapter 7). Merton (1969, 1971), Samuelson (1969), and Giovannini and Weil (1989) have shown that time variation in investment opportunities affects portfolio choice unless investors have unit relative risk aversion. But the large literature on the equity premium puzzle finds that average excess stock returns are too high to be consistent with a representative0investor model with unit relative risk aversion (see Campbell 1996, Cecchetti, Lam, and Mark 1994, Cochrane and Hansen 1992, Hansen and Jagannathan 1991, Kocherlakota 1996, Mehra and Prescott 1985, or the textbook treatment in Campbell, Lo, and MacKinlay 1997, Chapter 8). Therefore, it is important to analyze optimal consumption and portfolio decisions when there is time variation in the investment opportunity set and investors have risk aversion different from one.
The problem, however, is not trivial analytically. Nonlinearities in both the Euler equations and the intertemporal budget constraint make it extremely hard to find exact analytical solutions. Recently a few special cases have been solved. In a continuous time model with a constant riskless interest rate and a single risky asset whose expected return follows a mean0reverting AR(1) process, for example, the model can be solved if long lived investors have power utility defined over terminal wealth (Kim and Omberg 1996), or if investors have power utility defined over consumption and the innovation to the expected asset return is perfectly correlated with the innovation to the unexpected return, making the asset market effectively complete (Wachter 1999), or if the investor has Epstein0Zin utility with intertemporal elasticity of substitution restricted to equal one (Campbell and Viceira 1999, Schroder and Skiadas 1999).
