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on Market Microstructure |
| By: | Chen, Shi; Härdle, Wolfgang; Schienle, Melanie |
| Abstract: | This paper provides statistical learning techniques for determining the full own-price market impact and the relevance and effect of cross-price and cross-asset spillover channels from intraday transactions data. The novel tools allow extracting comprehensive information contained in the limit order books (LOB) and quantify their impacts on the size and structure of price interdependencies across stocks. For correct empirical network determination of such dynamic liquidity price effects even in small portfolios, we require high-dimensional statistical learning methods with an integrated general bootstrap procedure. We document the importance of LOB liquidity network spillovers even for a small blue-chip NASDAQ portfolio. |
| Keywords: | limit order book,high-dimensional statistical learning,liquidity networks,high frequency dynamics,market impact,bootstrap,network |
| JEL: | C02 C13 C22 C45 G12 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021015&r= |
| By: | Carol Alexander; Jun Deng; Jianfen Feng; Huning Wan |
| Abstract: | How do supply and demand from informed traders drive market prices of bitcoin options? Deribit options tick-level data supports the limits-to-arbitrage hypothesis about market maker's supply. The main demand-side effects are that at-the-money option prices are largely driven by volatility traders and out-of-the-money options are simultaneously driven by volatility traders and those with proprietary information about the direction of future bitcoin price movements. The demand-side trading results contrast with prior studies on established options markets in the US and Asia, but we also show that Deribit is rapidly evolving into a more efficient channel for aggregating information from informed traders. |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2109.02776&r= |
| By: | Liao, Jingchi; Peng, Cameron; Zhu, Ning |
| Abstract: | We propose an extrapolative model of bubbles to explain the sharp rise in prices and volume observed in historical financial bubbles. The model generates a novel mechanism for volume: because of the interaction between extrapolative beliefs and disposition effects, investors are quick to not only buy assets with positive past returns but also sell them if good returns continue. Using account-level transaction data on the 2014–2015 Chinese stock market bubble, we test and confirm the model’s predictions about trading volume. We quantify the magnitude of the proposed mechanism and show that it can increase trading volume by another 30%. |
| Keywords: | OUP deal |
| JEL: | G11 G12 |
| Date: | 2021–06–18 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:110514&r= |