Recently, a number of authors have argued that the standard search model cannot generate the observed business-cycle-frequency fluctuations in unemployment and job vacancies, given shocks of a plausible magnitude. We propose a new calibration strategy of the standard model that uses data on the cost of vacancy creation and cyclicality of wages to identify the two key parameters - the value of nonmarket activity and the bargaining weights. Our calibration implies that the model is consistent with the data.
Knowledge of the statistical distribution of the prices of emission allowances, and their forecastability, are crucial in constructing, among other things, purchasing and risk management strategies in the emissions-constrained markets. This paper analyzes the two emission permits markets, CO2 in Europe, and SO2 in the US, and investigates a model for dealing with the unique stylized facts of this type of data. Its effectiveness in terms of model fit and out-of-sample value-at-risk-forecasting, as compared to models commonly used in risk-forecasting contexts, is demonstrated.
Starting from the reward-risk model for portfolio selection introduced in De Giorgi (2004), we derive the reward-risk Capital Asset Pricing Model (CAPM) analogously to the classical mean-variance CAPM. The reward-risk portfolio selection arises from an axiomatic definition of reward and risk measures based on few basic principles, including consistency with second order stochastic dominance. With complete markets, we show that at any financial market equilibrium, investors’ optimal allocations are comonotonic and therefore the capital market equilibrium model can be reduced to a representative investor model. Moreover, the pricing kernel is an explicitly given, monotone function of the market portfolio return, corresponding to the increments
of the distortion function characterizing the epresentative investor’s risk perceptions. Finally, an empirical application shows that the reward-risk CAPM better captures
the cross-section of US stock returns than the ean-variance CAPM does.
Empirical studies show that people tend to be overconfident about the precision of their knowledge, leading to miscalibration. Consistent with this, we found that on overage the decision makers of Swiss pension plans provide too narrow confidence intervals when asked to estimate the past return of various assets. Their confidence intervals are also systematically too narrow in their forecast of future returns, in comparison with the historical volatility. They are less miscalibrated, however, than our laymen sample. Individual differences between the participants’ degree of overconfidence are large and stable across those two different tasks. In a linear regression model we present evi-dence that miscalibration is linked to individual characteristics. In our sample younger people with an education from university and with more experience in finance or pension plans are less over-confident than older people without such an education and with less experience.
In this paper, I discuss some recent research in the area of economic growth and development emphasizing the endogenous dynamics of policies and organizational forms in a world characterized by credit-market and labor-market imperfections. I present a simple model of technological convergence featuring an endogenous evolution of contractual arrangements. The key assumption is that economic growth is associated with investments as well as with the adoption and imitation of existing technologies in economies lying far from the technology frontier. In contrast, growth is increasingly driven by innovation as economies approach the technological frontier. The theory predicts that contractual arrangements evolve and adapt spontaneously to the changing needs of technological progress. However, this evolution is neither necessary nor serendipitous. Economies that fail to introduce
economic reforms as they advance may become stuck in non-convergence traps. I discuss a number of empirical applications, including the wave of reforms of industrial policy in India in the 1980s and 1990s.
Ratios of quadratic forms in correlated normal variables which introduce noncentrality into the quadratic forms are considered. The denominator is assumed to be positive (with probability 1). Various serial correlation estimates such as least-squares, Yule–Walker and Burg, as well as Durbin–Watson statistics, provide important examples of such ratios. The cumulative distribution function (c.d.f.) and density for such ratios admit saddlepoint approximations. These approximations are shown to preserve uniformity of relative error over the entire range of support. Furthermore, explicit values for the limiting relative errors at the extreme edges of support are derived.
A large proportion of the most viable time series models used in empirical finance for density and value-at-risk forecasting are estimated with maximum likelihood methods. By way of its definition, the likelihood implicitly places equal weight on each of the observations in the sample, but this need not be optimal, depending on the extent to which the model and the true data generating process deviate. For example, in the context of modeling financial asset returns, schemes that place relatively more weight on observations in the recent past result in considerable improvement of out-of-sample density forecasts, compared with the default of equal weights. If instead of accurate forecasting of the, entire density, interest is restricted to just downside risk and risk model validation, then it would seem wise to (also) place more weight on the negative observations in the sample. In this paper, such weighted likelihood schemes are proposed and demonstrated to yield considerable improvements in forecast accuracy using a variety of data sets and different GARCH models. Further improvement is realized by combining the two weighting schemes, giving rise to a doubly weighted asymmetric risk forecasting method or, in short, a DWARF-like method.
Empirische Untersuchungen zeigen, dass sich einfach strukturierte Produkte für die Anleger am meisten lohnen. Wegen mangelnder Transparenz seitens der Anbieter und wegen psychologischer Fehleinschätzungen kaufen viele Investoren aber oft komplexe und teure Vehikel.
Comments on the article by E. Brandstätter, G. Gigerenzer, and R. Hertwig. The authors discuss the priority heuristic, a recent model for decisions under risk. They reanalyze the experimental validity of this approach and discuss how these results compare with cumulative prospect theory, the currently most established model in behavioral economics. They also discuss how general models for decisions under risk based on a heuristic approach can be understood mathematically to gain some insight in their limitations. They finally consider whether the priority heuristic model can lead to some understanding of the decision process of individuals or whether it is better seen as an as-if model.
