Financial contagion: Evolutionary optimisation of a multinational agent-based model

Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during a crisis are referred to as financial contagion. We simulate crisis transmission in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a minority game approach, we develop an agent-based multinational model and investigate the reasons for contagion. Although the phenomenon has been extensively investigated in the financial literature, it has not been studied through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at an earlier stage, hence recognising financial crises with the potential to destabilise cross-market linkages. In the real world, such information would be extremely valuable in developing appropriate risk management strategies

Liquidity when it matters : QE and Tobin’s q

When financial markets freeze in fear, borrowing costs for solvent governments may fall towards zero in a flight to quality – but credit-worthy private borrowers can be starved of external funding. In Kiyotaki and Moore (2008), where liquidity crisis is captured by the effective rationing of private credit, tightening credit constraints have direct effects on investment. If prices are sticky, the effects on aggregate demand can be pronounced – as reported by FRBNY for the US economy using a calibrated DSGE-style framework modified to include such frictions. In such an environment, two factors stand out. First the recycling of credit flows by central banks can dramatically ease credit-rationing faced by private investors: this is the rationale for Quantitative Easing. Second, revenue-neutral fiscal transfers aimed at would-be investors can have similar effects. We show these features in a stripped- down macro model of inter-temporal optimisation subject to credit constraints

Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Welcome to OR&S! Where students, academics and professionals come together

In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities

Trading reliability targets within a supply chain using Shapley's value

The development of complex systems involves a multi-tier supply chain, with each organisation allocated a reliability target for their sub-system or component part apportioned from system requirements. Agreements about targets are made early in the system lifecycle when considerable uncertainty exists about the design detail and potential failure modes. Hence resources required to achieve reliability are unpredictable. Some types of contracts provide incentives for organisations to negotiate targets so that system reliability requirements are met, but at minimum cost to the supply chain. This paper proposes a mechanism for deriving a fair price for trading reliability targets between suppliers using information gained about potential failure modes through development and the costs of activities required to generate such information. The approach is based upon Shapley's value and is illustrated through examples for a particular reliability growth model, and associated empirical cost model, developed for problems motivated by the aerospace industry. The paper aims to demonstrate the feasibility of the method and discuss how it could be extended to other reliability allocation models

A Comparison of Electricity Market Designs in Networks

In the real world two classes of market designs are implemented to trade electricity in transmission constrained networks. Analytical results show that in two node networks integrated market designs reduce the ability of electricity generators to exercise market power relative to separated market designs. In multi node networks countervailing effects make an analytic analysis difficult. We present a formulation of both market designs as an equilibrium problem with equilibrium constraints. We find that in a realistic network, prices are lower with the integrated market design.

Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark

Financial deepening and economic growth

The core of Shapley-Shubik games and general equilibrium models with a Venn diagram is applied for a theory on the role of real finance in economic growth among advanced economies. Then the dynamic computable general equilibrium (DCGE) models for Germany, France, UK, Japan and USA are constructed to assess the validity of the over financing hypothesis that reappeared after the financial crisis of 2008. Actual financial deepening ratios observed in the non-consolidated balance sheet of the OECD exceeded by factors of 3.5, 2.4, 5.1, 11.6 and 4.8 to the optimal financial deepening ratios implied by DCGE models respectively in these countries because of excessive leveraging and bubbles up to 19 times of GDP which were responsible for this great recession. Containing such massive fluctuations for macroeconomic stability and growth in these economies is not possible in conventional fiscal and monetary policy models and requires a DCGE analysis like this along with adoption of separating equilibria strategy in line of Miller-Stiglitz-Roth mechanisms to avoid asymmetric information problems in process of financial intermediation so that the gap between actual and optimal ratios of financial deepening remain as small as possible

Corporate strategies – the institutional approach

The present paper introduces a model of corporate strategies, based on institutional theories of the firm and formalized with the concepts of the theory of games. Corporate strategies are balanced outcomes of four social games: capital market, corporate governance, product market and social responsibility. Two empirical applications of the model are then introduced: a qualitative one, consisting in comparative study of strategies deployed by Royal Dutch Shell and Israel Corporation, then a quantitative one, presenting a study of capital accumulation and operational efficiency in 79 companies listed in the Warsaw Stock Exchange.institutional economics, strategy, corporation