Nonlinear Dynamic Characteristic Analysis of Conditional Jump Duration

Jiayi Xu,Lianqian Yin

doi:10.54691/bcpbm.v15i.291

Abstract

Based on the five-minute tick market data of the Shanghai Composite Index from 2015 to 2019, the time duration of the jumps is realized by extracting the dynamic behavior model of the asset price to achieve the jumps, the dynamic process of the jump changes is described, and the broad ACD (1,1) model is established for the time duration sequence, which achieves a good fit for the dynamic behavior of the jump time duration. The results show that there is also aggregation effect in the time duration of the jumps, which shows that the intermittent phenomenon of the jump in asset prices opens up new ideas for further studying the characteristics and movement laws of asset price jump behavior from the perspective of jumping intensity.

Highlights

In order to obtain more information included in market data, with the rapid development of computer technology and the reduction of the cost of data sample collection, recording, storage and analysis, more and more scholars begin to study the microstructure of financial markets more deeply based on high frequency data.At present, the research based on high frequency data to measure the level of market volatility has achieved some fruitful results
Almost all the values jump up and down within the confidence interval, which indicates that the time duration series has weak autocorrelation indicating there is no need to introduce autocorrelation in conditional expectation model to satisfy the mean equation of autoregressive conditional duration (ACD) model
Based on the theoretical framework of the jump and time duration model, a generalized ACD model is established for the time duration of the jump in this paper

Summary

Introduction

In order to obtain more information included in market data, with the rapid development of computer technology and the reduction of the cost of data sample collection, recording, storage and analysis, more and more scholars begin to study the microstructure of financial markets more deeply based on high frequency data. Merton(1980) points out that if the frequency of financial asset prices observed in a certain period of time tends to infinity, the square of the rate of return in each observation interval will converge to the secondary variation of the observation interval according to the probability Based on this idea, Andersen, Bollerslev and Huang (2007) further develop the theory of realizing volatility RV and double power variance BV, and estimate the jump of financial asset price by making difference between RV and BV, that is, discontinuous jump partial variation JV. This paper introduces the sparse sampling method proposed by Bandi and Russel(2006) to reduce the noise of the jump sequence This method observes the asset price income at equal intervals. High frequency information is lost, the original structure of the data is retained, letting it more suitable for jump estimation

Theoretical Analysis

Autoregressive Conditional Duration Model

Data Specification

Variance Test

Robustness Test

Conclusion