scholarly journals COMPREHENSIVE MARKET MICROSTRUCTURE MODEL: CONSIDERING THE INVENTORY HOLDING COSTS

2017 ◽  
Vol 18 (2) ◽  
pp. 183-201 ◽  
Author(s):  
Doojin RYU
Keyword(s):  
Information Content ◽  
Market Microstructure ◽  
Futures Market ◽  
Market Liquidity ◽  
Significant Information ◽  
Holding Costs ◽  
Spread Dynamics ◽  
Microstructure Model ◽  
Inventory Holding ◽  
Indicator Model

The purpose of this study is to propose a structural market microstructure model and examine the intraday price and spread dynamics in a highly liquid market. We extend the model of Madhavan, Richardson, and Roomans to devise a comprehensive order indicator model that considers the order duration, order size, market liquidity, and most importantly, inventory holding costs. Our empirical analyses on the KOSPI200 futures market indicate that the inventory holding costs of liquidity suppliers explain a significant portion of model-implied spreads. Meanwhile, the duration and size of traded orders convey significant information content on the inventory holding component. Market liquidity is also an important consideration for futures traders who have to manage their inventory holding costs.

scholarly journals INFORMATION CONTENT OF INTER-TRANSACTION TIME: A STRUCTURAL APPROACH

2015 ◽  
Vol 16 (4) ◽  
pp. 697-711 ◽  
Author(s):  
Doojin Ryu
Keyword(s):  
Regression Analysis ◽  
Information Content ◽  
Market Microstructure ◽  
Futures Market ◽  
Informed Trading ◽  
Significant Information ◽  
Trade Size ◽  
Microstructure Model ◽  
Intraday Data

This study examines the information role of inter-transaction time by employing a structural market microstructure model. By analyzing the intraday data of the KOSPI200 futures market, we find that the inter-transaction time (i.e., time between two consecu- tive trades) reveals significant information, and that fast trading is indicative of informed trading. This result remains robust when the effect of trade size is incorporated into the model. Our regression analysis indicates that the information role of inter-transaction time becomes more important when informed trading is less concentrated, liquidity is lower, and the market is more volatile.

scholarly journals The Impact of Underlying Market Closure on Futures Market Liquidity: Evidence from China

2012 ◽  
Author(s):  
Wang Chun Wei ◽  
Alex Frino
Keyword(s):  
Futures Market ◽  
Market Liquidity ◽  
Stock Index ◽  
Stock Index Futures ◽  
Contagion Effect ◽  
Index Futures ◽  
Empirical Results ◽  
Open Session ◽  
The Impact ◽  
Closure Theory

This study investigates the trading activity of Chinese stock index futures, recently introduced at the open and close of the underlying trading. We document the impact of the underlying spot on the futures market liquidity as well as volatility as discussed in earlier works on market closure theory. Our empirical results support previous literature on the impact of the underlying, particularly during the open session, as a contagion effect, which is clearly at play. We find significant U-shaped patterns in liquidity factors and intraday volatility during open and close trades in the morning.  

scholarly journals Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Yanhui Xi ◽  
Hui Peng ◽  
Yemei Qin
Keyword(s):  
Time Series ◽  
Market Microstructure ◽  
Financial Time Series ◽  
Gaussian White Noise ◽  
Composite Index ◽  
Theoretical Explanation ◽  
Model Specification ◽  
Leverage Effect ◽  
Financial Time ◽  
Microstructure Model

The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation. With the new representations, a theoretical explanation of leverage effect is provided. Simulated data and daily stock market indices (Shanghai composite index, Shenzhen component index, and Standard and Poor’s 500 Composite index) via Bayesian Markov Chain Monte Carlo (MCMC) method are used to estimate the leverage market microstructure model. The results verify the effectiveness of the model and its estimation approach proposed in the paper and also indicate that the stock markets have strong leverage effects. Compared with the classical leverage stochastic volatility (SV) model in terms of DIC (Deviance Information Criterion), the leverage market microstructure model fits the data better.

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