Drawdown and speed of market crash on high frequency data

Jeudi 12 mars 2020, 10:15 à 11:15

Salle de séminaires M.0.1.

Groupe de travail de Statistique
Bice Di Basilio

Université G. d'Annunzio de Chieti-Pescara, Italie

Financial markets are characterized by continuous upward or downward fluctuations in prices, caused by the vast amount of information they receive. A strong price instability historically and cyclically caused strong market collapses that prompted investors to control the risk related to the excessive fluctuation of the prices in order to prevent significant portfolio losses. An extensive literature related to risk measures has developed. In this paper, we focus on the Drawdown of a fixel level K and on the Speed of market crash. These measures are useful to study the first time that the current drop reaches a threshold K and the speed at which the drawdown occurs, respectively (see [7]). Indeed the former is a measure of losses and the latter provides a measure of how fast a market crash takes place and thus how quickly such losses occur.

We compute these indicators using a Weighted-Indexed Semi-Markov Chain (WISMC) which is suitable for the study of high-frequency data (HFD) as shown in [1]-[5].

In our application we test the ability of WISMC model both to reproduce the volatility autocorrelation and to describe the two risk measures. We also compare our results with those obtained using the inflated GARCH models (see [6]).

 

Keywords: Market Crash, Drawdown, Speed of Market Crash, WISMC.

References:

[1]-G. D'Amico, A. Lika, F. Petroni, "Change point dynamics for financial data: an indexed Markov chain approach", Annals of Finance (2019), 247-266.

[2]-G. D'Amico, F. Petroni, "Copula based multivariate semi-Markov models with applications in high-frequency finance", European Journal of Operational Research 267, (2018), 765–777.

[3]-G. D'Amico, F. Petroni, “A semi-Markov model for price returns”, Physica A: Statistical Mechanics and its applications 391, (2012), 4867-4876.

[4]-G. D'Amico, F. Petroni, "A semi-Markov model with memory for price changes", Journal of Statistical Mechanics: Theory and Experiment, (2011).

[5]-G. D'Amico, F. Petroni, "Weighted-indexed semi-Markov models for modeling financial returns", Journal of Statistical Mechanics: Theory and Experiment, (2012).

[6]-B.V. M. Mendes, R. Pereira, "Maximum Drawdown: Models and Applications", SSRN Electronic Journal, (2003).

[7]-H. Zhang, O. Hadjiliadis,"Drawdowns and the Speed of Market Crash", Methodology And Computing In Applied Probability, (2011).

Exposé