Alternatives to the normal model of stock returns: Gaussian mixture, generalised logF and generalised hyperbolic models

Simple parametric models of the marginal distribution of stock returns are an essential building block in many areas of applied finance. Even though it is well known that the normal distribution fails to represent most of the “stylised” facts characterising return distributions, it still dominates much of the applied work in finance. Using monthly S&P 500 stock index returns (1871–2005) as well as daily returns (2001–2005), we investigate the viability of three alternative parametric families to represent both the stylised and empirical facts: the generalised hyperbolic distribution, the generalised logF distribution, and finite mixtures of Gaussians. For monthly return data, all three alternatives give reasonable fits for all sub-periods. However, the generalised hyperbolic distribution fails to describe some features of the marginal distributions in some sub-periods. The daily return data are much more symmetric and expose another problem for all three distributions: the parameters describing the behaviour of the tails also influence the scale so that simpler alternatives or restricted parameterisations are called for.      

Trading gains: terms-of-trade and real-exchange-rate effects

This paper considers alternative measures of a country's trading gains, i.e., the extra income that it earns (or loses) as the result of changes in the relative prices relevant for international trade, and which makes up the difference between real gross domestic product (GDP) and real gross domestic income (GDI). Looking at both the Laspeyres and the Törnqvist aggregation, we show that the trading gains really consist of two components, a terms-of-trade effect and a real-exchange-rate effect. Nearly all national statistical agencies, receiving no firm guidance from international organizations in this matter, merely consider the first effect, which suggests that the so-called trading-gain estimates they publish are incomplete and misnamed. Even more seriously, it implies that the corresponding measures of real GDI they derive are conceptually flawed. A straightforward way to circumvent these difficulties is to use the gross domestic final expenditure price index as deflator when computing real GDI and the trading gains. Some numerical estimates for Australia are provided as an illustration. The paper also identifies the underlying linear and Translog real GDI functions for which the Laspeyres and Törnqvist terms-of-trade and real-exchange-rate effects are exact.     

Utilising farm-level panel data to estimate climate change impacts and adaptation potentials

We combine farm accounting data with high-resolution meteorological data, and climate scenarios to estimate climate change impacts and adaptation potentials at the farm level. To do so, we adapt the seminal model of Moore and Lobell (2014) who applied panel data econometrics to data aggregated from the farm to the regional (subnational) level. We discuss and empirically investigate the advantages and challenges of applying such models to farm-level data, including issues of endogeneity of explanatory variables, heterogeneity of farm responses to weather shocks, measurement errors in meteorological variables, and aggregation bias. Empirical investigations into these issues reveal that endogeneity due to measurement errors in temperature and precipitation variables, as well as heterogeneous responses of farms toward climate change may be problematic. Moreover, depending on how data are aggregated, results differ substantially compared to farm-level analysis. Based on data from Austria and two climate scenarios (Effective Measures and High Emission) for 2040, we estimate that the profits of farms will decline, on average, by 4.4% (Effective Measures) and 10% (High Emission). Adaptation options help to considerably ameliorate the adverse situation under both scenarios. Our results reinforce the need for mitigation and adaptation to climate change.