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

1. 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

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

3. Tick-by-tick price dynamics

4. 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

5. 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

6. 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).

7. 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.

8. 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.

9. 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

10. Example: The normal compound Poisson process (=1) Convolution of n Gaussians The distribution of x is leptokurtic

11. 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).

12. 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.

13. 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)

14. 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! See S. Cincotti presentation: Session 6/2!

15. 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 can be helpful in various applications.