The future of consumer payments is electronic, secure, easy to use, and right around the corner. With that prospect in mind, Wells Fargo® has sponsored this Javelin Strategy & Research White Paper to help merchants think through current and future decisions related to payment options, and distribution channels. Wells Fargo is committed to continuing to partner with merchants in the implementation and processing of existing as well as new payment options.
Evolving merchant needs, technology innovation, government regulations, fraud and security, and demographic trends are converging to accelerate the pace of payment electronification. Payment electronification is defined here as the migration from cash and checks to electronic alternatives. These trends are leading to a period of rapid growth of electronic payments while at the same time shrinking the number of checks written each year.
Model evaluation has always been at the forefront of macroeconomic research. A modeling techniques have advanced over time, a wide variety of time-series models have sprung up to satisfy different needs, from simple univariate and multivariate linear models to more complicated univariate and multivariate nonlinear models. It is therefore important to establish an efficient and reasonable approach to model comparison and evaluation that is suitable for very different types of time-series models. In this paper, we evaluate a variety of univariate linear, univariate nonlinear, and multivariate linear models of U.S. real gross domestic product (GDP) in terms of their abilities to produce simulated data that exhibit the business cycle features in the actual GDP data. Our primary goal is to investigate the extent to which multivariate information inherent in macroeconomic variables such as the unemployment rate, inflation, interest rates, and the components of GDP can account for the apparent univariate evidence of nonlinear dynamics in U.S. GDP previously demonstrated in the literature.
The conventional methods for conducting model evaluation – hypothesis testing, out-of-sample forecast comparisons, and Bayes factors – face several drawbacks. When the models under consideration are non-nested, hypothesis testing is often intractable. Out-of-sample forecast comparisons tend to be sensitive to the particular out of sample period used. Bayes factors can be very sensitive to the specification of priors. Furthermore, Bayes factors only provide a sense of the relative performance of different models and not an absolute measure of the ability of a model to explain the dynamics in the data.
Considerable attention has been given in the financial press to the increase in stock market volatility during the late 1990s. The facts suggest, however, that this attention has been misplaced. As shown first by Schwert (1989), no long-run uptrend is evident for the volatility of the market as a whole. The volatility of the market during the late 1990s, while larger than it was earlier in the decade, was still considerably below the volatility recorded during earlier periods of the century.
What has received far less attention is the behavior of the volatility of individual stocks. The volatility of individual stocks can increase even when the volatility of the market as a whole remains stable as long as correlations among stocks are declining. In this study, we show, from a different perspective and using different measures from those used by Campbell, Lettau, Malkiel, and Xu (2001), that volatilities of individual stocks have indeed increased over the decades of the 1980s and 1990s. When the total volatility of individual stocks is decomposed into systematic volatility and idiosyncratic volatility, we present clear evidence that idiosyncratic volatility has trended up. We find that our result is not solely attributable to the increasing prominence of the NASDAQ market. Most importantly, we find from cross-sectional regressions that the volatility of individual stocks may be related to the amount of institutional ownership and to the firms’ objectives in pursuing high growth.
