"Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH"

DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the multivariate density support. This paper presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasimaximum likelihood estimators. The paper also presents an empirical example to highlight the usefulness of the new model.

Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH

DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the multivariate density support. This paper presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasi-maximum likelihood estimators. The paper also presents an empirical example to highlight the usefulness of the new modelmultivariate asymmetry, conditional variance, stationarity conditions, asymptotic theory, multivariate news impact curve.

Persistent disequilibrium dynamics and economic policy

We develop a theoretical model involving temporary equilibria with quantity rationing in each period and price adjustment between periods. The resulting dynamic system may present a variety of dynamic behaviors, ranging from the convergence to stationary or quasi-stationary states, to complex or even chaotic dynamics. In particular, our framework has the property of being able to endogenously allow for the characterization of persistent disequilibrium phenomena — such us unemployment or deflation. It provides therefore for an ideal setup to investigate the effects and persistency of recessionary phases, and to study the effectiveness of different economic policies aimed at resolving them.non-tâtonnement, complex dynamics, Phillips curve, expectations, inventories, non-neutrality of money, deflation

Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models

A continuous time econometric modelling framework for multivariate financial market event (or 'transactions') data is developed in which the model is specified via the vector conditional intensity. This has the advantage that the conditioning information set is updated continuously in time as new information arrives. Generalised Hawkes (g-Hawkes) models are introduced that are sufficiently flexible to incorporate `inhibitory' events and dependence between trading days. Novel omnibus specification tests for parametric models based on a multivariate random time change theorem are proposed. A computationally efficient thinning algorithm for simulation of g-Hawkes processes is also developed. A continuous time, bivariate point process model of the timing of trades and mid-quote changes is presented for a New York Stock Exchange stock and the empirical findings are related to the market microstructure literature. The two-way interaction of trades and quote changes is found to be important empirically. Furthermore, the model delivers a continuous record of instantaneous volatility that is conditional on the timing of trades and quote changes.Point process, conditional intensity, Hawkes process, specification test, random time change, transactions data, market microstructure.

Open Source Development in a Differentiated Duopoly

Open source software is released under an open source license giving individuals the right to use, modify, and redistribute freely the programs. This paper proposes a model of differentiated duopoly in which firms invest in the development of proprietary or open source software. The main findings are: (i) firms invest more when the products are substitutes; (ii) for substitute products, firms’ investment in software development is greatest when the software is open source; (iii) for close to perfect complements, firms’ investment in software development is greatest when the software is proprietary; and (iv) for substitute products, investment in open source software yields higher profits than investment in proprietary software.

Thresholds, News Impact Surfaces and Dynamic Asymmetric Multivariate GARCH

DAMGARCH is a new model that extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. Analytical expressions for the news impact surface implied by the new model are also presented. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the multivariate density support. This paper presents the model structure, describes the implementation issues, and provides the conditions for the existence of a unique stationary solution, and for consistency and asymptotic normality of the quasi-maximum likelihood estimators. The paper also presents an empirical example to highlight the usefulness of the new model.Multivariate asymmetry; conditional variance; stationarity conditions; asymptotic theory; multivariate news impact curve

Identifying technology spillovers and product market rivalry

Support for many R&D and technology policies relies on empirical evidence that R&D "spills over" between firms. But there are two countervailing R&D spillovers: positive effects from technology spillovers and negative effects from business stealing by product market rivals. We develop a general framework showing that technology and product market spillovers have testable implications for a range of performance indicators, and exploits these using distinct measures of a firm's position in technology space and product market space. We show using panel data on U.S. firms between 1981 and 2001 that both technology and product market spillovers operate, but that net social returns are several times larger than private returns. The spillover effects are also revealed when we analyze three hightech sectors in detail - pharmaceuticals, computer hardware andtelecommunication equipment. Using the model we evaluate three R&Dsubsidy policies and show that the typical focus of support for small and medium firms may be misplaced.Spillovers, R&D, market value, patents.

Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models

A continuous time econometric modelling framework for multivariate financial market event (or `transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new information arrives. The class of generalised Hawkes models is introduced which allows the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and it is shown how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time, bivariate point process model of the timing of trades and mid-quote changes is presented for a New York Stock Exchange stock and the empirical findings are related to the theoretical and empirical market microstructure literature. The two-way interaction of trades and quote changes is found to be important empirically.Point and counting processes, multivariate, intensity, Hawkes process, diagnostics, goodness of fit, specification tests, change of time, transactions data, NYSE, market microstructure.

Modelling Security Market Events in Continuous Time: Intensity based, Multivariate Point Process Models

A continuous time econometric modelling framework for multivariate market event (or 'transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new information arrives. We introduce the class of generalised Hawkes models which allow the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and we show how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time scale. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time bivariate point process model of the timing of trades and mid-quote changes is presented for a NYSE stock and the empirical findings are related to the theoretical and empirical market microstructure literature.Point and counting processes, intensity, multivariate, diagnostics, goodness of fit, specification tests, change of timescale, transactions data, NYSE, NASDAQ, market microstructure

Environmental and Innovation Performance in a Dynamic Impure Public Good Framework

We model investment decisions regarding innovation and emissions abatement in a dynamic theoretical framework. Considering knowledge stock as an impure public good, we study the reaction function between one representative agent’s investments in innovation and the other agents’ investments in the public characteristic of the impure public good. We demonstrate that the reaction function has a positive slope under general conditions and that its sensitiveness is affected by assumptions on the elasticity of substitution in the benefit function. The positivity of the reaction function is then empirically tested in an econometric estimation. We exploit an original sector-based database by gathering innovation efforts as well as polluting emissions and economic dimensions over the time span 1996-2005 for 15 European countries and 23 manufacturing sectors. Empirical results show that sector-based innovation investment is positively driven by the public characteristics provided by other sectors. Different reactivity strength for different polluting emissions also allows us to disclose the role of complementarity in agents’ decisions.impure public goods; environmental externalities; innovation spillovers