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on Market Microstructure |
| By: | Julien Hambuckers; Li Sun; Luca Trapin |
| Abstract: | We study tail risk dynamics in high-frequency financial markets and their connection with trading activity and market uncertainty. We introduce a dynamic extreme value regression model accommodating both stationary and local unit-root predictors to appropriately capture the time-varying behaviour of the distribution of high-frequency extreme losses. To characterize trading activity and market uncertainty, we consider several volatility and liquidity predictors, and propose a two-step adaptive $L_1$-regularized maximum likelihood estimator to select the most appropriate ones. We establish the oracle property of the proposed estimator for selecting both stationary and local unit-root predictors, and show its good finite sample properties in an extensive simulation study. Studying the high-frequency extreme losses of nine large liquid U.S. stocks using 42 liquidity and volatility predictors, we find the severity of extreme losses to be well predicted by low levels of price impact in period of high volatility of liquidity and volatility. |
| Date: | 2023–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2301.01362&r=mst |
| By: | Gábor Pintér (Bank of England); Chaojun Wang (Wharton); Junyuan Zou (INSEAD) |
| Abstract: | We show that larger trades incur lower trading costs in government bond markets (“size discount”), but costs increase in trade size after controlling for clients’ identities (“size penalty”). The size discount is driven by the cross-client variation of larger traders obtaining better prices, consistent with theories of trading with imperfect competition. The size penalty, driven by the within-client variation, is larger for corporate bonds and during major macroeconomic surprises as well as during COVID-19. These differences are larger among more sophisticated clients, consistent with theories of asymmetric information. We propose a trading model with bilateral bargaining and adverse selection to rationalise the co-existence of the size penalty and discount. |
| Keywords: | Trading Costs, Government and Corporate Bonds, Trader Identities, Size Discount, Size Penalty |
| JEL: | G12 G14 G24 |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:cfm:wpaper:2114&r=mst |
| By: | Gregory Boadu-Sebbe |
| Abstract: | A critical aspect of trading Exchange-Traded Funds (ETFs) is the arbitrage trading strategy taken by authorized participants (APs) to keep ETF prices in line with their net asset values (NAVs). ETF arbitrage trading is a strategy that exploits the discrepancies between an ETF price and the value of the ETF’s underlying assets. In this study, I quantitatively examine the effect of ETF arbitrage on the underlying assets of an ETF. I develop a dynamic state-space model that jointly estimates the price dynamics of an ETF and its underlying assets by explicitly incorporating the ETF arbitrage. The model is estimated individually for the Dow Jones Industrial Average ETF (DIA) and the VanEck Vectors Semiconductor ETF (SMH). The empirical results show that ETF liquidity shocks propagate to the underlying assets via the ETF arbitrage mechanism. These ETF liquidity shocks add a permanent layer of transitory volatility to the underlying asset prices. I find that a unit of liquidity shock to DIA brings a range of 0.1% to 0.93% of extra volatility to the underlying assets of DIA. Similarly, a unit of liquidity shock to SMH adds a range of 0.33% to 0.95% of additional volatility to the underlying assets. In addition, I show that it takes APs longer to correct deviations between the ETF price and its NAV. It takes approximately 4 and 10 minutes for APs to perform the ETF arbitrage for DIA and SMH, respectively. Finally, the findings suggest that an ETF arbitrage transaction speeds up the price discovery process in the ETF markets. There are approximately 74% and 67% variations in the premiums of DIA and SMH due to price discovery, respectively. |
| Keywords: | Exchange-Traded Funds; Underlying assets; ETF arbitrage mechanism; Liquidity shocks; Net asset value (NAV); Price discovery process; |
| JEL: | B26 C32 C58 E44 G12 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:cer:papers:wp738&r=mst |