limit order markets
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2020 ◽  
Vol 33 (5) ◽  
pp. 1545-1557
Author(s):  
Qiang Zhang ◽  
Chao Wang ◽  
Shancun Liu ◽  
Yaodong Yang

2019 ◽  
Vol 12 (4) ◽  
pp. 164 ◽  
Author(s):  
Eric Ghysels ◽  
Giang Nguyen

We examine price discovery and liquidity provision in the secondary market for bitcoin—an asset with a high level of speculative trading. Based on BTC-e’s full limit order book over the 2013–2014 period, we find that order informativeness increases with order aggressiveness within the first 10 tiers, but that this pattern reverses in outer tiers. In a high volatility environment, aggressive orders seem to be more attractive to informed agents, but market liquidity migrates outward in response to the information asymmetry. We also find support to the Markovian learning assumption often made in theoretical models of limit order markets.


2019 ◽  
Vol 55 (6) ◽  
pp. 1792-1839 ◽  
Author(s):  
Ioanid Roşu

How does informed trading affect liquidity in limit order markets, where traders can choose between market orders (demanding liquidity) and limit orders (providing liquidity)? In a dynamic model, informed trading overall helps liquidity: A higher share of informed traders i) improves liquidity as proxied by the bid–ask spread and market resiliency, and ii) has no effect on the price impact of orders. The model generates other testable implications, and suggests new measures of informed trading.


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