Intraday jumps and trading volume: a nonlinear Tobit specification

Abstract

This paper investigates the relationship between trading volume and volatility for four international stock markets (US: S&P500, UK: FTSE100, France: CAC40 and Germany: DAX30) in a context of global financial crisis. Unlike previous related studies, we use intraday data and apply a nonlinear econometric model to assess this relationship. In particular, we first break down intraday realized volatility into its continuous and jump components using the non-parametric approach developed by Barndorff-Nielsen and Shephard (J Financ Econom 4:1-30, 2006). Second, we investigate the volume-volatility relationship and test whether it varies according to volatility components (jumps and continuous component). While Giot et al. (J Empir Finance 17:168-175, 2010), among others, investigated the volume-volatility relationship in a linear context, our study contributes by estimating different nonlinear specifications (threshold model, nonlinear Tobit model) that enable us to capture further asymmetry and time-variation to better apprehend the effect of trading volume on realized volatility. Accordingly, our study yields two interesting findings. On the one hand, as expected there is a significant and positive relationship between trading volume and realized volatility, as well as with its components, confirming the importance of trading volume as a key to characterizing volatility. On the other hand, we show that this relationship exhibits asymmetry and nonlinearity, and that threshold models are more appropriate than linear model to characterize the volume volatility relationship.

The asymmetric price-volume relation revisited: evidence from Qatar

“…Recently, to look deeper into the volume-volatility nexus, a stream of research decomposes return volatility into a continuous component, which is private information-driven, and jumps component, which is public information-driven. For example, Jawadi et al (2015) use intraday data from four major stock markets, namely, Frankfurt, London, New York and Paris. The results point to a significantly positive association between volume and volatility components (jumps and continuous part), suggesting a strong link between trading volume and both public and private information.…”

Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters

“…Kallsen et al [19] considered jumps, stochastic fluctuations and leverage effects in the application of the BN-S model, and gave option pricing according to the quadratic change of the stock price process. Jawadi et al [18] used the BN-S model to decompose the intra-day volatility into continuous volatility and jumping volatility, and studied the relationship between the trading volume and volatility of different international stock markets. Habtemicael [13] innovatively proposed an optimized BN-S model of superimposed Lévy processes driven by τ (ν, α) and inverse Gaussian distribution, and modeled to realize the exchange of variance and volatility.…”

Data analytic approach for manipulation detection in stock market

“…Before their studies, Chow et al (Chow, Hung, Liu, & Shiu, 2013) also investigated the manipulation cases on Taiwan markets and found that expiration day effect partially contributes to the market close manipulation attempts. Based on the study, Huang et al (Huang & Cheng, 2014) studied the new closing mechanism on the market variables and the aggressiveness of the trading activity while Jawadi et al (Jawadi, Louhichi, Cheffou, & Randrianarivony, 2016) further studied the trading volumes and volatility across four international markets.…”