As ever, these are items that have come in over the various and sundry preprint services and lists I watch; listing them here not because they’re good, but because they’ve caught my eye, and they’re related to What I Do these days.

Having my eye caught is What I Do, actually.

So, in no particular order:

“Forecasting Realized Volatility by Decomposition”:

Abstract: Forecasts of the realized volatility of the exchange rate returns of the Euro against the U.S. Dollar obtained directly and through decomposition are compared. Decomposing the realized volatility into its continuous sample path and jump components and modeling and forecasting them separately instead of directly forecasting the realized volatility is shown to lead to improved out-of-sample forecasts. Moreover, gains in forecast accuracy are robust with respect to the details of the decomposition.

Abstract: Generalized Response Surface Methodology (GRSM) is a novel general-purpose metaheuristic based on Box and Wilson.s Response Surface Methodology (RSM). Both GRSM and RSM estimate local gradients to search for the optimal solution. These gradients use local first-order polynomials. GRSM, however, uses these gradients to estimate a better search direction than the steepest ascent direction used by RSM. Moreover, GRSM allows multiple responses, selecting one response as goal and the other responses as constrained variables. Finally, these estimated gradients may be used to test whether the estimated solution is indeed optimal. The focus of this paper is optimization of simulated systems.

Abstract: We analyze a large-stakes prisoner’s dilemma game played on a TV show. Players cooperate 40% of the time, demonstrating that social preferences are important; however, cooperation is significantly below the 50% threshold that is required for inequity aversion to sustain cooperation. Women cooperate significantly more than men, while players who have “earned” more of the stake cooperate less. A player’s promise to cooperate is also a good predictor of his decision. Surprisingly, a player’s probability of cooperation is unrelated to the opponent’s characteristics or promise. We argue that inequity aversion alone cannot adequately explain these results; reputational concerns in a public setting might be more important.

Abstract: Transition is not just transition of formal institutions, convergence of price levels and living standards. The closure or the gap in formal institutions is probably less time demanding than the closure of ideological or mental gap, created in many fields in academy or social life. Social sciences have been erased during half a century and post-soviets still struggle for academic prestige of these areas. We have seen many misunderstandings concerning the interrelations, hierarchy and even object of study in social sciences. Superiority of economics is sometimes created by market signals, or superiority of some other discipline by “political signals”. Our aim is to show that in the body of social sciences economics is a normal science which can be defined by method, not by subject matter. We will introduce the alternative methodological approaches to rational choice and indicate their advantages and disadvantages. Mainly two questions are answered. First, is there some alternative methodology which has been more successful in producing efficient predictions and explanations of social affairs? Second, how methodological criticism has changed rational choice perspectives and can these changes be justified? Finally, changes in methodology of economics are discussed showing that there is no clear answer — how to parcel our social sciences?

Abstract: Consider the nonparametric regression model Y = m(X)+e, where the function m is smooth, but unknown. We construct tests for the independence of e and X, based on n independent copies of (X; Y ). The testing procedures are based on differences of neighboring Y ’s. We establish asymptotic results for the proposed tests statistics, investigate their finite sample properties through a simulation study and present an econometric application to household data. The proofs are based on delicate empirical process theory.

Abstract: This entry for the New Palgrave covers developments in nonlinear time series analysis over the last 25 years.

Abstract: An explanation for motivation crowding-out phenomena is developed in a social preferences framework. Besides selfish and fair or altruistic types a third type of agents is introduced: These ‘conformists’ have social preferences if they believe that sufficiently many of the others do too. When there is asymmetric information about the distribution of preferences (the ’social norm’), the incentive scheme offered or autonomy granted can reveal a principal’s beliefs about that norm. High-powered incentives may crowd out motivation as pessimism about the norm is conveyed. But by choosing fixed wages or granting autonomy the principal may signal trust in a favorable social norm.

Abstract: The satisficing approach with its three constituent processes, aspiration formation, satisficing, and aspiration adjustment, is formally elaborated for a specific class of portfolio selection tasks. It is partly poorly confirmed by experimental data, indicating that bounded rationality requires teaching or, respectively, consulting, and learning. It is also discussed and tested experimentally whether satisficing is task transcending (are there individual constants in satisficing behavior for related tasks?) and absorbable (do we stick to satisficing behavior when becoming aware of it?).

Abstract: Using monthly data for the period 1953–2003, we apply a real-time modeling approach to investigate the implications of U.S. political stock market anomalies for forecasting excess stock returns. Our empirical findings show that political variables, selected on the basis of widely used model selection criteria, are often included in real-time forecasting models. However, they do not contribute to systematically improving the performance of simple trading rules. For this reason, political stock market anomalies are not necessarily an indication of market inefficiency.

Abstract: The stock market is a complex system that affects economic and financial activities around the world. Analysis of stock price data can improve our understanding of the past price movements of stocks. In this work, we develop a method to determine the highly interconnected subsystems of the stock market. Our method relies on a k-core decomposition scheme to analyze large networks. Our approach illustrates that the stock market is a nearly decomposable system which comprises hierarchic subsystems. This work also presents results from the analysis of a network derived from a large data set of stock prices. This network analysis technique is a new promising approach to analyze and classify stocks based on price interactions and to decompose the complex system embodied in the stock market.

Abstract: Weather derivatives are financial contracts for which the underlying is not a traded asset. Therefore, they cannot be priced by the traditional financial theory based on the hedging portfolio and on the arbitrage-free argument. Some authors suggest to use the actuarial pricing approach to value the weather derivatives. But this method suffers from the fact that it is only based on the modelling of the temperature. The market information is not necessary to value the weather derivatives by this approach. On the contrary, the financial method needs to infer the market price of weather risk since the market is incomplete for the weather derivatives. We suggest in this paper to compute and to compare the prices stemming from the both approaches by using the New York weather futures quotations. Prices are calculated on the basis that the daily average temperature has a long memory since tests reveal its presence in the serie.

Bacteria and their bacteriophages are the most abundant, widespread and diverse groups of biological entities on the planet. In an attempt to understand how the interactions between bacteria, virulent phages and temperate phages might affect the diversity of these groups, we developed a novel stochastic network model for examining the co-evolution of these ecologies. In our approach, nodes represent whole species or strains of bacteria or phages, rather than individuals, with “speciation” and extinction modelled by duplication and removal of nodes. Phage-bacteria links represent host-parasite relationships and temperate-virulent phage links denote prophage-encoded resistance. The effect of horizontal transfer of genetic information between strains was also included in the dynamical rules. The observed networks evolved in a highly dynamic fashion but the ecosystems were prone to collapse (one or more entire groups going extinct). Diversity could be stably maintained in the model only if the probability of speciation was independent of the diversity. Such an effect could be achieved in real ecosystems if the speciation rate is primarily set by the availability of ecological niches.

In this paper we present a novel tool to evaluate problem solving systems. Instead of using a system to solve a problem, we suggest using the problem to evaluate the system. By finding a numerical representation of a problem’s complexity, one can implement genetic algorithm to search for the most complex problem the given system can solve. This allows a comparison between different systems that solve the same set of problems. In this paper we implement this approach on pattern recognition neural networks to try and find the most complex pattern a given configuration can solve. The complexity of the pattern is calculated using linguistic complexity. The results demonstrate the power of the problem evolution approach in ranking different neural network configurations according to their pattern recognition abilities. Future research and implementations of this technique are also discussed.

Abstract: As a group, market forecasters are egregiously overconfident. In conformity to the dynamic model of overconfidence of Gervais and Odean (2001), successful forecasters become more overconfident. What’s more, more experienced forecasters have “learned to be overconfident,” and hence are more susceptible to this behavioral flaw than their less experienced peers. It is not just individuals who are affected. Markets also become more overconfident when market returns have been high.