Heterogeneity, convergence, and autocorrelations

This paper is a contribution to the literature on the explanatory power and calibration of heterogeneous asset pricing models. We set out a new stochastic market-fraction asset pricing model of fundamentalists and trend followers under a market maker. Our model explains key features of financial market behaviour such as market dominance, convergence to the fundamental price and under- and over-reaction. We use the dynamics of the underlying deterministic system to characterize these features and statistical properties, including convergence of the limiting distribution and autocorrelation structure. We confirm these properties using Monte Carlo simulations.

An architecture for agent-enhanced network service provisioning through SLA negotiation

This paper focuses on two main areas. We first investigate various aspects of subscription and session Service Level Agreement (SLA) issues such as negotiating and setting up network services with Quality of Service (QoS) and pricing preferences. We then introduce an agent-enhanced service architecture that facilitates these services. A prototype system consisting of real-time agents that represent various network stakeholders was developed. A novel approach is presented where the agent system is allowed to communicate with a simulated network. This allows functional and dynamic behaviour of the network to be investigated under various agent-supported scenarios. This paper also highlights the effects of SLA negotiation and dynamic pricing in a competitive multi-operator networks environment.

A general framework for measuring inconsistency through minimal inconsistent sets

Hunter and Konieczny explored the relationships between measures of inconsistency for a belief base and the minimal inconsistent subsets of that belief base in several of their papers. In particular, an inconsistency value termed MIVC, defined from minimal inconsistent subsets, can be considered as a Shapley Inconsistency Value. Moreover, it can be axiomatized completely in terms of five simple axioms. MinInc, one of the five axioms, states that each minimal inconsistent set has the same amount of conflict. However, it conflicts with the intuition illustrated by the lottery paradox, which states that as the size of a minimal inconsistent belief base increases, the degree of inconsistency of that belief base becomes smaller. To address this, we present two kinds of revised inconsistency measures for a belief base from its minimal inconsistent subsets. Each of these measures considers the size of each minimal inconsistent subset as well as the number of minimal inconsistent subsets of a belief base. More specifically, we first present a vectorial measure to capture the inconsistency for a belief base, which is more discriminative than MIVC. Then we present a family of weighted inconsistency measures based on the vectorial inconsistency measure, which allow us to capture the inconsistency for a belief base in terms of a single numerical value as usual. We also show that each of the two kinds of revised inconsistency measures can be considered as a particular Shapley Inconsistency Value, and can be axiomatically characterized by the corresponding revised axioms presented in this paper.

Commonality in Returns, Order Flows, and Liquidity in the Greek Stock Market

Using a unique high-frequency data-set on a comprehensive sample of Greek blue-chip stocks, spanning from September 2003 through March 2006, this note assesses the extent and role of commonality in returns, order flows (OFs), and liquidity. It also formally models aggregate equity returns in terms of aggregate equity OF, in an effort to clarify OF's importance in explaining returns for the Athens Exchange market. Almost a quarter of the daily returns in the FTSE/ATHEX20 index is explained by aggregate own OF. In a second step, using principal components and canonical correlation analyses, we document substantial common movements in returns, OFs, and liquidity, both on a market-wide basis and on an individual security basis. These results emphasize that asset pricing and liquidity cannot be analyzed in isolation from each other.

Remanufacturer-Manufacturer Collaborative Model in the Smae Channel

This paper adds to the growing literature of market competition related to the remanufacturer by analyzing the model where the remanufacturer and the manufacturer collaborate with each other in the same channel. This paper investigates a single-period deterministic model which keeps the analysis simple so as to obtain sharper insights. The results characterize the optimal remanufacturing and pricing strategies for the remanufacturer and the manufacturer in the collaborative model

The Effect of Risk Sensitivity on Collaborative New Product Development in a Supply Chain

The increasing risks and costs of new product development require firms to collaborate with their supply chain partners in product management. In this paper, a supply chain model is proposed with one risk-neutral supplier and one risk-averse manufacturer. The manufacturer has an opportunity to enhance demand by developing a new product, but both the actual demand for new product and the supplier’s wholesale price are uncertain. The supplier has an incentive to share risks of new product development via an advance commitment to wholesale price for its own profit maximization. The effects of the manufacturer’s risk sensitivity on the players’ optimal strategies are analyzed and the trade-off between innovation incentives and pricing flexibility is investigated from the perspective of the supplier. The results highlight the significant role of risk sensitivity in collaborative new product development, and it is found that the manufacturer’s innovation level and retail price are always decreasing in the risk sensitivity, and the supplier prefers commitment to wholesale price only when the risk sensitivity is below a certain threshold.

Measuring inconsistency in network intrusion rules

In this preliminary case study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. Finally, we measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. We conclude that such measures are useful for the network intrusion domain assuming that incorporating domain knowledge for correlation of rules is feasible.

Measuring Inconsistency in a Network Intrusion Detection Rule Set Based on Snort

In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. Finally, we propose a new measure of inconsistency for prioritized knowledge which incorporates the normalized number of atoms in a language involved in inconsistency to provide a deeper inspection of inconsistent formulae. We conclude that such measures are useful for the network intrusion domain assuming that introducing expert knowledge for correlation of rules is feasible.

Unfunded Pension Liabilities and the Corporate CDS Market

This article examines the impact of pension deficits on default risk as measured by the premia on corporate credit default swaps (CDS). We find highly significant evidence that unfunded pension liabilities raise one- and five-year CDS premia. However, this relation is not homogeneous across countries, with the U.S. CDS market leading its European counterparts in the pricing of defined-benefit pension risk.

A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

A new method for estimating liquidity risk: Insights from a liquidity-adjusted CAPM framework

This paper proposes a new non-parametric method for estimating model-free, time-varying liquidity betas which builds on realized covariance and volatility theory. Working under a liquidity-adjusted CAPM framework we provide evidence that liquidity risk is a factor priced in the Greek stock market, mainly arising from the covariation of individual liquidity with local market liquidity, however, the level of liquidity seems to be an irrelevant variable in asset pricing. Our findings provide support to the notion that liquidity shocks transmitted across securities can cause market-wide effects and can have important implications for portfolio diversification strategies. ©2012 Elsevier B.V. All rights reserved.

Product standardisation as a tool to control prescribing costs a case study of alginate liquid preparations

Introduction Product standardisation involves promoting the prescribing of pre-selected products within a particular category across a healthcare region and is designed to improve patient safety by promoting continuity of medicine use across the primary/secondary care interface, in addition to cost containment without compromising clinical care (i.e. maintaining safety and efficacy). Objectives To examine the impact of product standardisation on the prescribing of compound alginate preparations within primary care in Northern Ireland. Methods Data were obtained on alginate prescribing from the Northern Ireland Central Services Agency (Prescription Pricing Branch), covering a period of 43 months. Two standardisation promotion interventions were carried out at months 18 and 33. In addition to conventional statistical analyses, a simple interrupted time series analysis approach, using graphical interpretation, was used to facilitate interpretation of the data. Results There was a significant increase in the prescribed share of the preferred alginate product in each of the four health boards in Northern Ireland and a decrease in the cost per Defined Daily Dose for alginate liquid preparations overall. Compliance with the standardisation policy was, however, incomplete and was influenced to a marked degree by the activities of the pharmaceutical industry. The overall economic impact of the prescribing changes during the study was small (3.1%). Conclusion The findings suggested that product standardisation significantly influenced the prescribing pattern for compound alginate liquid preparations within primary care across Northern Ireland. © 2012 The Authors. IJPP © 2012 Royal Pharmaceutical Society.

Legislating about Unhealthy Food: A Millian Approach

Tackling food-related health conditions is becoming one of the most pressing issues in the policy agendas of western liberal democratic governments. In this article, I intend to illustrate what the liberal philosopher John Stuart Mill would have said about legislation on unhealthy food and I focus especially on the arguments advanced by Mill in his classic essay On Liberty ([1859] 2006). Mill is normally considered as the archetype of liberal anti-paternalism and his ideas are often invoked by those who oppose state paternalism, including those who reject legislation that restricts the consumption of unhealthy food. Furthermore, his views have been applied to related policy areas such as alcohol minimum pricing (Saunders 2013) and genetically modified food (Holtug 2001). My analysis proceeds as follows. First, I show that Mill’s account warrants some restrictions on food advertising and justifies various forms of food labelling. Second, I assess whether and to what extent Mill’s ‘harm principle’ justifies social and legal non-paternalistic penalties against unhealthy eaters who are guilty of other-regarding harm. Finally, I show that Mill’s account warrants taxing unhealthy foods, thus restricting the freedom of both responsible and irresponsible eaters and de facto justifying what I call ‘secondary paternalism’.

Is Mortality Spatial or Social?

Mortality modelling for the purposes of demographic forecasting and actuarial pricing is generally done at an aggregate level using national data. Modelling at this level fails to capture the variation in mortality within country and potentially leads to a mis-specification of mortality forecasts for a subset of the population. This can have detrimental effects for pricing and reserving in the actuarial context. In this paper we consider mortality rates at a regional level and analyse the variation in those rates. We consider whether variation in mortality rates within a country can be explained using local economic and social variables. Using Northern Ireland data on mortality and measures of deprivation we identify the variables explaining mortality variation. We create a population polarisation variable and find that this variable is significant in explaining some of the variation in mortality rates. Further, we consider whether spatial and non-spatial models have a part to play in explaining mortality differentials.

On the Viability of Microservers for Financial Analytics

Energy consumption and total cost of ownership are daunting challenges for Datacenters, because they scale disproportionately with performance. Datacenters running financial analytics may incur extremely high operational costs in order to meet performance and latency requirements of their hosted applications. Recently, ARM-based microservers have emerged as a viable alternative to high-end servers, promising scalable performance via scale-out approaches and low energy consumption. In this paper, we investigate the viability of ARM-based microservers for option pricing, using the Monte Carlo and Binomial Tree kernels. We compare an ARM-based microserver against a state-of-the-art x86 server. We define application-related but platform-independent energy and performance metrics to compare those platforms fairly in the context of datacenters for financial analytics and give insight on the particular requirements of option pricing. Our experiments show that through scaling out energyefficient compute nodes within a 2U rack-mounted unit, an ARM-based microserver consumes as little as about 60% of the energy per option pricing compared to an x86 server, despite having significantly slower cores. We also find that the ARM microserver scales enough to meet a high fraction of market throughput demand, while consuming up to 30% less energy than an Intel server