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Continuous-time random walks and fractional calculus: Theory and applications. Enrico Scalas (DISTA East-Piedmont University) www.econophysics.org. DIFI Genoa ( IT ) 2 0 October 2004. Summary. Introduction to CTRW and applications to Finance Applications to Physics Conclusions.

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continuous time random walks and fractional calculus theory and applications

Continuous-time random walks and fractional calculus: Theory and applications

Enrico Scalas (DISTA East-Piedmont University)

www.econophysics.org

DIFI Genoa (IT) 20 October 2004

summary
Summary
  • Introduction to CTRW and applications to Finance
  • Applications to Physics
  • Conclusions
1999 2004 five years of continuous time random walks in econophysics

1999-2004: Five years of continuous-time random walks in Econophysics

Enrico Scalas (DISTA East-Piedmont University)

www.econophysics.org

WEHIA 2004 - Kyoto (JP) 27-29 May 2004

summary1
Summary
  • Continuous-time random walks as models of market price dynamics
  • Limit theorem
  • Link to other models
  • Some applications
slide7

Theory (I)

Continuous-time random walk in finance

(basic quantities)

: price of an asset at timet

: log price

: joint probability density of jumps

and of waiting times

: probability density function of

finding the log price xat timet

slide8

Theory (II): Master equation

Permanence in x,t

Jump into x,t

Marginal jump pdf

Marginal waiting-time pdf

In case of independence:

Survival probability

slide9

Theory (III): Limit theorem, uncoupled case (I)

(Scalas, Mainardi, Gorenflo, PRE, 69, 011107, 2004)

Mittag-Leffler function

This is the characteristic

function of the log-price process

subordinated to a generalised

Poisson process.

Subordination: see Clark, Econometrica,

41, 135-156 (1973).

slide10

Theory (IV): Limit theorem, uncoupled case (II)

(Scalas, Gorenflo, Mainardi, PRE, 69, 011107, 2004)

Scaling of probability

density functions

Asymptotic behaviour

This is the characteristic

function for the Green

function of the fractional

diffusion equation.

slide11

Theory (V): Fractional diffusion

(Scalas, Gorenflo, Mainardi, PRE, 69, 011107, 2004)

Green function of the pseudo-differential equation (fractional diffusion equation):

Normal diffusion

for =2, =1.

slide12

Continuous-time random walks (CTRWs)

(Scalas, Gorenflo, Luckock, Mainardi, Mantelli, Raberto QF, submitted,

preliminary version cond-mat/0310305, or preprint:

www.maths.usyd.edu.au:8000/u/pubs/publist/publist.html?preprints/2004/scalas-14.html)

Diffusion

processes

Mathematics

Compound Poisson processes

as models of high-frequency

financial data

Fractional

calculus

Subordinated

processes

CTRWs

Physics

Finance and

Economics

Normal and

anomalous

diffusion in physical

systems

Cràmer-Lundberg

ruin theory for

insurance companies

slide13

Example: The normal compound Poisson process (=1)

Convolution of n Gaussians

The distribution of x is leptokurtic

slide14

Generalisations

  • Perturbations of the NCPP:
  • general waiting-time and log-return densities;
  • (with R. Gorenflo, Berlin, Germany and F. Mainardi, Bologna, Italy, PRE, 69, 011107, 2004);
  • variable trading activity (spectrum of rates);
  • (with H.Luckock, Sydney, Australia, QF submitted);
  • link to ACE;
  • (with S. Cincotti, S.M. Focardi, L. Ponta and M. Raberto, Genova, Italy, WEHIA 2004!);
  • dependence between waiting times and log-returns;
  • (with M. Meerschaert, Reno, USA, in preparation, but see P. Repetowicz and P. Richmond,
  • xxx.lanl.gov/abs/cond-mat/0310351);
  • other forms of dependence (autoregressive conditional
  • duration models, continuous-time Markov models);
  • (work in progress in connection to bioinformatics activity).
slide15

Applications

  • Portfolio management: simulation of a synthetic market
  • (E. Scalas et al.: www.mfn.unipmn.it/scalas/~wehia2003.html).
  • VaR estimates: e.g. speculative intra-day option pricing.
  • If g(x,T) is the payoff of a European option with delivery time T:
  • (E. Scalas, communication submitted to FDA ‘04).
  • Large scale simulations of synthetic markets with
  • supercomputers are envisaged.
slide16
Empirical results on the waiting-time survival function and their relevance for market models (Anderson-Darling test) (I)

Interval 1 (9-11): 16063 data; 0 = 7 s

Interval 2 (11-14): 20214 data; 0 = 11.3 s

Interval 3 (14-17): 19372 data; 0 =7.9 s

where 1 2  …  n

A12= 352; A22= 285; A32= 446>>1.957 (1% significance)

slide17
Empirical results on the waiting-time survival function and their relevance for market models (Anderson-Darling test) (II)
  • Non-exponential waiting-time survival function now observed by
  • many groups in many different markets (Mainardi et al. (LIFFE)
  • Sabatelli et al. (Irish market and ), K. Kim & S.-M. Yoon (Korean
  • Future Exchange)), but see also Kaizoji and Kaizoji (cond-mat/0312560)
  • Why should we bother? This has to do both with the market
  • price formation mechanism and with the bid-ask process.
  • If the bid-ask process is modelled by means of a Poisson
  • distribution (exponential survival function), its random thinning
  • should yield another Poisson distribution. This is not the case!
  • A clear discussion can be found in a recent contribution
  • by the GASM group.
  • Possible explanation related to variable daily activity!
slide20

Problem

  • Understanding the scaling of transport with domain size has become the critical issue in the design of fusion reactors.
  • It is a challenging task due to the overwhelming complexity of magnetically confined plasmas that are typically in a turbulent state.
  • Diffusive models have been used since the beginning.
slide21

Focus and method

  • Tracer transport in pressure-gradient-driven plasma turbulence.
  • Variations in pressure gradient trigger instabilities leading to intermittent and avalanchelike transport.
  • Non-linear equations for the motion of tracers are numerically solved.
  • The pdf of tracer position is non-Gaussian with algebraic decaying tails.
slide23

Solution I

  • There is tracer trapping due to turbulent eddies.
  • There are large jumps due to avalanchelike events.
  • These two effects are the source of anomalous diffusion.
slide24

Solution II

  • Fat tails (nearly three decades)
conclusions
Conclusions
  • CTRWs are suitable as phenomenological models for high-frequency market dynamics.
  • They are related to and generalise many models already used in econometrics.
  • They are suitable phenomenological models of anomalous diffusion.
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