Paper # Author Title
We explore the macro/finance interface in the context of equity markets. In particular, using half a century of Livingston expected business conditions data we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R2 . Expected business conditions retain predictive power even after controlling for an important and recently introduced non-financial predictor, the generalized consumption/wealth ratio, which accords with the view that expected business conditions play a role in asset pricing different from and complementary to that of the consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, while time-varying consumption/wealth may capture time-varying risk Download Paper
We take a nonstructural time-series approach to modeling and forecasting daily average temperature in ten U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time series modeling reveals both strong conditional mean dynamics and conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. Most importantly, it adapts readily to produce the long-horizon forecasts of relevance in weather derivatives contexts. We produce and evaluate both point and distributional forecasts of average temperature, with some success. We conclude that additional inquiry into nonstructural weather forecasting methods, as relevant for weather derivatives, will likely prove useful. Download Paper
Weather derivatives are a fascinating new type of Arrow-Debreu security, making pre-specified payouts if pre-specified weather events occur, and the market for such derivatives has grown rapidly. Weather modeling and forecasting are crucial to both the demand and supply sides of the weather derivatives market. On the demand side, to assess the potential for hedging against weather surprises and to formulate the appropriate hedging strategies, one needs to determine how much "weather noise" exists for weather derivatives to eliminate, and that requirees weather modeling and forecasting. On the supply side, standard approaches to arbitrage-free pricing are irrelevant in weather derivative contexts, and so the only way to price options reliably is again by modeling and forecasting the underlying weather variable. Curiously, however, little thought has been given to the crucial question of how best to approach weather modeling and forecasting in the context of weather derivative demand and supply. The vast majority of extant weather forecasting literature has a structural "atmospheric science" feel, and although such an approach may be best for forecasting six hours ahead, it is not obvious that it is best for the longer horizons relevant for weather derivatives, such as six days, six weeks, or six months. In particular, good forecasting does not necessarily require a structural model. In this paper, then, we take a seemingly-naive nonstructural time-series approach to modeling and forecasting daily average temperature in ten U.S. cities, and we inquire systematically as to whether it proves useful. The answer is, perhaps surprisingly, "yes." Time series modeling reveals a wealth of information about both conditional mean dynamics and the conditional variance dynamics of average daily temperature, some of which seems not to have been noticed previously, and it provides similarly sharp insights into both the distributions of weather and the distributions of weather surprises, and the key differences between them. The success of time-series modeling in capturing conditional mean dynamics translates into successful point forecasting, a fact which, together with the success of time-series modeling in identifying and characterizing the distributions of weather surprises, translates as well into successful density forecasting. Download Paper