Optimal Investment in the Development of Oil and Gas Field

Let an oil and gas field consists of clusters in each of which an investor can launch at most one project. During the implementation of a particular project, all characteristics are known, including annual production volumes, necessary investment volumes, and profit. The total amount of investments that the investor spends on developing the field during the entire planning period we know. It is required to determine which projects to implement in each cluster so that, within the total amount of investments, the profit for the entire planning period is maximum. The problem under consideration is NP-hard. However, it is solved by dynamic programming with pseudopolynomial time complexity. Nevertheless, in practice, there are additional constraints that do not allow solving the problem with acceptable accuracy at a reasonable time. Such restrictions, in particular, are annual production volumes. In this paper, we considered only the upper constraints that are dictated by the pipeline capacity. For the investment optimization problem with such additional restrictions, we obtain qualitative results, propose an approximate algorithm, and investigate its properties. Based on the results of a numerical experiment, we conclude that the developed algorithm builds a solution close (in terms of the objective function) to the optimal one

Life, Death and Preferential Attachment

Scientific communities are characterized by strong stratification. The highly skewed frequency distribution of citations of published scientific papers suggests a relatively small number of active, cited papers embedded in a sea of inactive and uncited papers. We propose an analytically soluble model which allows for the death of nodes. This model provides an excellent description of the citation distributions for live and dead papers in the SPIRES database. Further, this model suggests a novel and general mechanism for the generation of power law distributions in networks whenever the fraction of active nodes is small.Comment: 5 pages, 2 figure

An Optimal Execution Problem with Market Impact

We study an optimal execution problem in a continuous-time market model that considers market impact. We formulate the problem as a stochastic control problem and investigate properties of the corresponding value function. We find that right-continuity at the time origin is associated with the strength of market impact for large sales, otherwise the value function is continuous. Moreover, we show the semi-group property (Bellman principle) and characterise the value function as a viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. We introduce some examples where the forms of the optimal strategies change completely, depending on the amount of the trader's security holdings and where optimal strategies in the Black-Scholes type market with nonlinear market impact are not block liquidation but gradual liquidation, even when the trader is risk-neutral.Comment: 36 pages, 8 figures, a modified version of the article "An optimal execution problem with market impact" in Finance and Stochastics (2014

Generalized pricing formulas for stochastic volatility jump diffusion models applied to the exponential Vasicek model

Path integral techniques for the pricing of financial options are mostly based on models that can be recast in terms of a Fokker-Planck differential equation and that, consequently, neglect jumps and only describe drift and diffusion. We present a method to adapt formulas for both the path-integral propagators and the option prices themselves, so that jump processes are taken into account in conjunction with the usual drift and diffusion terms. In particular, we focus on stochastic volatility models, such as the exponential Vasicek model, and extend the pricing formulas and propagator of this model to incorporate jump diffusion with a given jump size distribution. This model is of importance to include non-Gaussian fluctuations beyond the Black-Scholes model, and moreover yields a lognormal distribution of the volatilities, in agreement with results from superstatistical analysis. The results obtained in the present formalism are checked with Monte Carlo simulations.Comment: 9 pages, 2 figures, 1 tabl

Evolutionary estimation of a Coupled Markov Chain credit risk model

There exists a range of different models for estimating and simulating credit risk transitions to optimally manage credit risk portfolios and products. In this chapter we present a Coupled Markov Chain approach to model rating transitions and thereby default probabilities of companies. As the likelihood of the model turns out to be a non-convex function of the parameters to be estimated, we apply heuristics to find the ML estimators. To this extent, we outline the model and its likelihood function, and present both a Particle Swarm Optimization algorithm, as well as an Evolutionary Optimization algorithm to maximize the likelihood function. Numerical results are shown which suggest a further application of evolutionary optimization techniques for credit risk management

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

WARNING: Physics Envy May Be Hazardous To Your Wealth!

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an "uncertainty checklist" with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.Comment: v3 adds 2 reference

Stochastic Cellular Automata Model for Stock Market Dynamics

In the present work we introduce a stochastic cellular automata model in order to simulate the dynamics of the stock market. A direct percolation method is used to create a hierarchy of clusters of active traders on a two dimensional grid. Active traders are characterised by the decision to buy, (+1), or sell, (-1), a stock at a certain discrete time step. The remaining cells are inactive,(0). The trading dynamics is then determined by the stochastic interaction between traders belonging to the same cluster. Most of the stylized aspects of the financial market time series are reproduced by the model.Comment: 17 pages and 7 figure

Hot Streaks in Artistic, Cultural, and Scientific Careers

The hot streak, loosely defined as winning begets more winnings, highlights a specific period during which an individual's performance is substantially higher than her typical performance. While widely debated in sports, gambling, and financial markets over the past several decades, little is known if hot streaks apply to individual careers. Here, building on rich literature on lifecycle of creativity, we collected large-scale career histories of individual artists, movie directors and scientists, tracing the artworks, movies, and scientific publications they produced. We find that, across all three domains, hit works within a career show a high degree of temporal regularity, each career being characterized by bursts of high-impact works occurring in sequence. We demonstrate that these observations can be explained by a simple hot-streak model we developed, allowing us to probe quantitatively the hot streak phenomenon governing individual careers, which we find to be remarkably universal across diverse domains we analyzed: The hot streaks are ubiquitous yet unique across different careers. While the vast majority of individuals have at least one hot streak, hot streaks are most likely to occur only once. The hot streak emerges randomly within an individual's sequence of works, is temporally localized, and is unassociated with any detectable change in productivity. We show that, since works produced during hot streaks garner significantly more impact, the uncovered hot streaks fundamentally drives the collective impact of an individual, ignoring which leads us to systematically over- or under-estimate the future impact of a career. These results not only deepen our quantitative understanding of patterns governing individual ingenuity and success, they may also have implications for decisions and policies involving predicting and nurturing individuals with lasting impact

Patenting and licensing of university research: promoting innovation or undermining academic values?

Since the 1980s in the US and the 1990s in Europe, patenting and licensing activities by universities have massively increased. This is strongly encouraged by governments throughout the Western world. Many regard academic patenting as essential to achieve 'knowledge transfer' from academia to industry. This trend has far-reaching consequences for access to the fruits of academic research and so the question arises whether the current policies are indeed promoting innovation or whether they are instead a symptom of a pro-intellectual property (IP) culture which is blind to adverse effects. Addressing this question requires both empirical analysis (how real is the link between academic patenting and licensing and 'development' of academic research by industry?) and normative assessment (which justifications are given for the current policies and to what extent do they threaten important academic values?). After illustrating the major rise of academic patenting and licensing in the US and Europe and commenting on the increasing trend of 'upstream' patenting and the focus on exclusive as opposed to non-exclusive licences, this paper will discuss five negative effects of these trends. Subsequently, the question as to why policymakers seem to ignore these adverse effects will be addressed. Finally, a number of proposals for improving university policies will be made