In this chapter, we discuss two risk measures based on drawdown process and closely related to market crises: the drawdown of a fixed level and the speed of market crash. They allow us to study the first time that the asset’s price deviates from its current maximum by a certain threshold value and the velocity at which this drop occurs, respectively. Consequently, the former, is a relative measure of the losses linked to an asset, while the latter, quantifies the speed at which these losses occur. In order to study these risk measures, we consider tick-by-tick prices of two assets, listed on the Italian Stock Exchange. We implement an empirical investigation involving estimation and simulation of widely used econometric models such as Autoregressive Moving Average (ARMA) models, Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and Exponential GARCH (EGARCH) models. We test the ability of each model to reproduce the volatility autocorrelation, a typical feature of financial time series, and then we analyze their capacity to reproduce the drawdown of fixed level and the speed of market crash, compared to real data.

An Econometric Analysis of Drawdown Based Measures

Guglielmo D’Amico
;
Bice Di Basilio;
2023-01-01

Abstract

In this chapter, we discuss two risk measures based on drawdown process and closely related to market crises: the drawdown of a fixed level and the speed of market crash. They allow us to study the first time that the asset’s price deviates from its current maximum by a certain threshold value and the velocity at which this drop occurs, respectively. Consequently, the former, is a relative measure of the losses linked to an asset, while the latter, quantifies the speed at which these losses occur. In order to study these risk measures, we consider tick-by-tick prices of two assets, listed on the Italian Stock Exchange. We implement an empirical investigation involving estimation and simulation of widely used econometric models such as Autoregressive Moving Average (ARMA) models, Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and Exponential GARCH (EGARCH) models. We test the ability of each model to reproduce the volatility autocorrelation, a typical feature of financial time series, and then we analyze their capacity to reproduce the drawdown of fixed level and the speed of market crash, compared to real data.
2023
Stochastic Processes, Statistical Methods, and Engineering Mathematics: SPAS 2019, Västerås, Sweden, September 30–October 2
Anatoliy Malyarenko, Ying Ni, Milica Rancic, Sergei Silvestrov
Inglese
STAMPA
489
510
22
978-3-031-17819-1
Springer Nature
SVIZZERA
Risk measure, Econometric models
no
2 Contributo in Volume::2.1 Contributo in volume (Capitolo o Saggio)
4
268
none
D'Amico, Guglielmo; DI BASILIO, Bice; Petroni, Filippo; Gismondi, Fulvio
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/800631
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