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on Computational Economics |
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Issue of 2016‒07‒23
nine papers chosen by |
| By: | Walter POHL (University of Zurich); Karl SCHMEDDERS (University of Zurich and Swiss Finance Institute); Ole WILMS (University of Zurich) |
| Abstract: | This paper presents an analysis of the higher-order dynamics of key financial quantities in asset-pricing models with recursive preferences. For this purpose, we first describe a projection-based algorithm for solving such models. The method outperforms common methods like discretization and log-linearization in terms of efficiency and accuracy. Our algorithm allows us to document the presence of strong nonlinear effects in the modern long-run risks models which cannot be captured by the common methods. For example, for a prominent recent calibration of a popular long-run risks model, the log-linearization approach overstates the equity premium by 100 basis points or 22.5%. The increasing complexity of state-of-the-art asset-pricing models leads to complex nonlinear equilibrium functions with considerable curvature which in turn have sizable economic implications. Therefore, these models require numerical solution methods, such as the projection methods presented in this paper, that can adequately describe the higher-order equilibrium features. |
| Keywords: | Asset pricing, discretization, log-linearization, nonlinear dynamics, projection methods |
| JEL: | G11 G12 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1468&r=cmp |
| By: | Didier SORNETTE (ETH Zurich and Swiss Finance Institute) |
| Abstract: | This short review presents a selected history of the mutual fertilization between physics and economics, from Isaac Newton and Adam Smith to the present. The fundamentally different perspectives embraced in theories developed in financial economics compared with physics are dissected with the examples of the volatility smile and of the excess volatility puzzle. The role of the Ising model of phase transitions to model social and financial systems is reviewed, with the concepts of random utilities and the logit model as the analog of the Boltzmann factor in statistic physics. Recent extensions in term of quantum decision theory are also covered. A wealth of models are discussed briefly that build on the Ising model and generalize it to account for the many stylized facts of financial markets. A summary of the relevance of the Ising model and its extensions is provided to account for financial bubbles and crashes. The review would be incomplete if it would not cover the dynamical field of agent based models (ABMs), also known as computational economic models, of which the Ising-type models are just special ABM implementations. We formulate the "Emerging Market Intelligence hypothesis" to reconcile the pervasive presence of "noise traders" with the near efficiency of financial markets. Finally, we note that evolutionary biology, more than physics, is now playing a growing role to inspire models of financial markets. |
| Keywords: | order book, Brownian particle, fluctuation-dissipation, dollar-yen OR from paper: Finance, physics, econophysics, Ising model, phase transitions, excess volatility puzzle, logit model, Boltzmann factor, bubbles, crashes, adaptive markets, ecologies |
| JEL: | A12 B41 C00 C44 C60 C73 D70 G01 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1425&r=cmp |
| By: | Zura Kakushadze; Willie Yu |
| Abstract: | We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Returns? The answer turns out to be neither and our backtests suggest that these details make a sizable difference. We also give an algorithm and source code for building "hybrid" industry classifications by improving off-the-shelf "fundamental" industry classifications by applying our statistical industry classification methods to them. The presentation is intended to be pedagogical and geared toward practical applications in quantitative trading. |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1607.04883&r=cmp |
| By: | Erik Canton; Jan In't Veld; Romanos Priftis |
| Abstract: | Structural reform measures underway in Italy, France, Spain and Portugal could have significant economic benefits and raise GDP, model simulations presented in this edition of the QREA show. Other chapters in this edition look at the effects of population ageing and slowing total factor productivity growth on GDP, inflation and interest rates; and the drivers of total factor productivity growth. |
| JEL: | A10 C10 C23 C54 D24 E00 E61 F15 F45 J21 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:euf:qreuro:0151&r=cmp |
| By: | Colasante, Annarita |
| Abstract: | This paper presents an investigation about cooperation in a Public Good Game using an Agent Based Model calibrated on experimental data. Starting from the experiment proposed in Colasante and Russo (2016), we analyze the dynamic of cooperation in a Public Good Game where agents receive an heterogeneous income and choose both the level of contribution and the distribution rule. The starting point is the calibration and the output validation of the model using the experimental results. Once tested the goodness of fit of the Agent Based Model, we run some policy experiment in order to verify how each distribution rule, i.e. equidistribution, proportional to contribution and progressive, affects the level of contribution in the simulated model. We find out that the share of cooperators decreases over time if we exogenously set the equidistribution rule. On the contrary, the share of cooperators converges to 100% if we impose the progressive rule. Finally, the most interesting result refers to the effect of the progressive rule. We observe that, in the case of high inequality, this rule is not able to reduce the heterogeneity of income. |
| Keywords: | Public Good Game, Cooperation, Social Influence |
| JEL: | C63 D71 H41 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:72577&r=cmp |
| By: | Christian Bayer; Markus Siebenmorgen; Raul Tempone |
| Abstract: | We consider the problem of pricing basket options in a multivariate Black Scholes or Variance Gamma model. From a numerical point of view, pricing such options corresponds to moderate and high dimensional numerical integration problems with non-smooth integrands. Due to this lack of regularity, higher order numerical integration techniques may not be directly available, requiring the use of methods like Monte Carlo specifically designed to work for non-regular problems. We propose to use the inherent smoothing property of the density of the underlying in the above models to mollify the payoff function by means of an exact conditional expectation. The resulting conditional expectation is unbiased and yields a smooth integrand, which is amenable to the efficient use of adaptive sparse grid cubature. Numerical examples indicate that the high-order method may perform orders of magnitude faster compared to Monte Carlo or Quasi Monte Carlo in dimensions up to 25. |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1607.05572&r=cmp |
| By: | Simone Righi; Karoly Takacs |
| Abstract: | It is not easy to rationalize how peer review, as the current grassroots of science, can work based on voluntary contributions of reviewers. There is no rationale to write impartial and thorough evaluations. Consequently, there is no risk in submitting lowquality work by authors. As a result, scientists face a social dilemma: if everyone acts according to his or her own self-interest, low scientific quality is produced. Still, in practice, reviewers as well as authors invest high effort in reviews and submissions. We examine how the increased relevance of public good benefits (journal impact factor), the editorial policy of handling incoming reviews, and the acceptance decisions that take into account reputational information can help the evolution of high-quality contributions from authors. High effort from the side of reviewers is problematic even if authors cooperate: reviewers are still best off by producing low-quality reviews, which does not hinder scientific development, just adds random noise and unnecessary costs to it. We show with agent-based simulations that tacit agreements between authors that are based on reciprocity might decrease these costs, but does not result in superior scientific quality. Our study underlines why certain self-emerged current practices, such as the increased importance of journal metrics, the reputation-based selection of reviewers, and the reputation bias in acceptance work efficiently for scientific development. Our results find no answers, however, how the system of peer review with impartial and thorough evaluations could be sustainable jointly with rapid scientifi9c development. |
| Keywords: | peer review; evolution of cooperation; reputation; agent based model. |
| Date: | 2016–07 |
| URL: | http://d.repec.org/n?u=RePEc:mod:cappmo:0144&r=cmp |
| By: | Weng, Shou; Chen, Lee; Odendaal, Kong |
| Abstract: | In the recent years, unit commitment (UC) has been increasingly directed towards improving the quality of power to satisfy the customers’ demand at a minimum cost. As a result, minimizing the cost function of the unit commitment problem has become a challenge for many research studies while assuring the power availability in distribution systems. In this paper, the new Bat Algorithm (BA) as an evolutionary algorithm is proposed to minimize the unit commitment cost function and to decrease the fluctuation of power in the distribution system. The cost function employs constraints including spinning reserve and generator ramp rate in addition to commonly used load balance, power limits, etc. Simulation studies on a 10-unit distribution system shows significant improvement in the convergence speed and minimum calculated cost when compared to the available methods. |
| Keywords: | Power System Operation, Unit Commitment, Optimization, Economic Dispatch, Smart Grids |
| JEL: | L00 |
| Date: | 2016–07–13 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:72528&r=cmp |
| By: | Zhao, Qi; Zhang, Yongfeng; Zhang, Yi; Friedman, Daniel |
| Abstract: | Recommender systems often recommend several products to a user at the same time, but with little consideration of the relationships among the recommended products. We argue that relationships such as substitutes and complements are crucial, since the utility of one product may depend on whether or not other products are purchased. For example, the utility of a camera lens is much higher if the user has the appropriate camera (complements), and the utility of one camera is lower if the user already has a similar camera (substitutes). In this paper, we propose multi-product utility maximization (MPUM) as a general approach to account for product relationships in recommendation systems. MPUM integrates the economic theory of consumer choice theory with personalized recommendation, and explicitly considers product relationships. It describes and predicts utility of product bundles for individual users. Based on MPUM, the system can recommend products by considering what the users already have, or recommend multiple products with maximum joint utility. As the estimated utility has mon- etary unit, other economic based evaluation metrics such as consumer surplus or total surplus can be incorporated naturally. We evaluate MPUM against several popular base- line recommendation algorithms on two offline E-commerce datasets. The experimental results showed that MPUM significantly outperformed baseline algorithm under top-K evaluation metric, which suggests that the expected number of accepted/purchased products given K recommendations are higher. |
| Keywords: | Recommendation Systems,Utility,Product Portfolio,Computational Economics |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:wzbmdn:spii2016503&r=cmp |