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Financial and Socioeconomic Modelling
Financial Markets are hierarchical systems, with layers of complexity.
Complementary statistical measures and diverse models are necessary to
sift the available evidence on bubbles, crashes and other market movements.
In the group, we explore the potential for prediction of a multi-layered
approach, incorporating three principal strands, (using sourced data).
We aim
- To identify statistical measures of market co-movement by analysis of
eigenvalues of the variance-covariance matrix of daily price indices and
associated time series.
- To identify noise elements of this matrix, by use of a novel fractional
calculus approach. This will provide a decision-tool to pinpoint future real
market changes.
- To explore the impact of preliminary indications on movement, by means
of statistical physics models, which emphasise connected (or co-operative)
behaviour
These may be described in terms of agent interaction or herd influence in
market trading but, while some limited exposure has been given to these
ideas, our focus is on the granularity of the market response to measurable
change in coherence.
The approach proposed is highly inter-disciplinary and will provide for
analysis of comparative market behaviour across different industrial sectors
both nationally and internationally. As a prototype method for early risk
assessment, we anticipate consequent benefit to informed and strategic
decision-making, together with potential for wider applicability to other
complex social systems. Furthermore, the integrated nature of the approach
will facilitate assessment of intervention strategies under negative market
co-movements.
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Traffic flow and transportation systems are classical examples, of socio-economic systems exhibiting complexity, and attempts to model and analyse these go back a long way. There are many layers to the complexity, not least the unpredictability of human behaviour, the heterogeneity of vehicles, the lack of regularity in road systems and the non-linear group dynamics. The development of the network level traffic approach was based on the two-fluid theory of town traffic, (Herman and Prigogine (1979,), Herman and Ardekani (1984) and subsequently), which related average speed of moving cars to the number in a street. Other work has involved development of fast simulation models for progression of cars along a street through bit-manipulation programmes, e.g. Cremer and Ludwig (1986). Cellular Automaton models have been popular since the early 90's (e.g. Nagel and Schreckenberg (1992) and use rule-sets to control car movement. For this reason they are also known as microscopic models, although aggregate traffic parameters (in the microscopic sense) are also used to describe the traffic behaviour. A typical road transport system includes obstacles, different road geometries and configurations, (i.e. intersections, roundabouts, multiple lanes etc.) as well as control features, like lights and crossings. In any multi-lane situation, the complexity increases as lane-changing rules must also be considered in addition to the usual manoeuvres. Further, neither traffic units, nor drivers are consistent and homogeneous in the real world, so that driver behaviour and traffic mix must also be taken into account and is often crucial in urban situations.
In recent years, models for both freeway and urban/inter-urban flow have been developed, with interest in the group predominantly focused on the latter. Most recently, models of gap-acceptance type, Brilon and Wu (1999), Tian et al. (2000) have been used, in conjunction with CA road and ring rules to form hybrid MAP models for heterogeneous and inconsistent driver behaviour at specific urban road features. Current work is focused on extending these hybrid models to incorporate vehicle heterogeneity and on looking at agent-based potential for intelligent MAPping.
References:
Brilon W.and Wu N. (1999) Transportation Research Part A 33, 275
Cremer M. and Ludwig J. (1986) Mathematics and Computers in Simulations, 28, 297
Herman R.and Prigogine I. (1979) Science, 204, 148
Herman R. and Ardekani S. (1984) Transportation Science, 18, 101
Nagel K. and Schreckenberg M. (1992) J. Phys. France 2, 2221
Tian Z. Z., Troutbeck R. , Kyte M., Brilon W., Vandehey M., Kittelson W. , Robinson B. (2000) Transportation Research CircularE-C108 : 4th Intl. Symposium on Highway Capacity, 397
Researchers: Jane Horgan, Gary Keogh, Martin Crane, Heather J. Ruskin, Yu Feng, Saba Sharifi, Yaw Bimpeh, Patricia Gunning, Adel Sharkasi, Justin Daly, Ruili Wang, Puspita Deo
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