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on Market Microstructure |
| By: | Saef, Danial; Nagy, Odett; Sizov, Sergej; Härdle, Wolfgang |
| Abstract: | While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models. However, we need better econometric methods for capturing the specific market microstructure of cryptos. All calculations are reproducible via the quantlet.com technology. |
| Keywords: | jumps,market microstructure noise,high frequency data,cryptocurrencies,CRIX,option pricing |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021019&r= |
| By: | Jeremy D. Turiel; Tomaso Aste |
| Abstract: | With the rise of computing and artificial intelligence, advanced modeling and forecasting has been applied to High Frequency markets. A crucial element of solid production modeling though relies on the investigation of data distributions and how they relate to modeling assumptions. In this work we investigate volume distributions during anomalous price events and show how their tail exponents |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.13718&r= |
| By: | Campi, Luciano; Zabaljauregui, Diego |
| Abstract: | Starting from the Avellaneda–Stoikov framework, we consider a market maker who wants to optimally set bid/ask quotes over a finite time horizon, to maximize her expected utility. The intensities of the orders she receives depend not only on the spreads she quotes but also on unobservable factors modelled by a hidden Markov chain. We tackle this stochastic control problem under partial information with a model that unifies and generalizes many existing ones under full information, combining several risk metrics and constraints, and using general decreasing intensity functionals. We use stochastic filtering, control and piecewise-deterministic Markov processes theory, to reduce the dimensionality of the problem and characterize the reduced value function as the unique continuous viscosity solution of its dynamic programming equation. We then solve the analogous full information problem and compare the results numerically through a concrete example. We show that the optimal full information spreads are biased when the exact market regime is unknown, and the market maker needs to adjust for additional regime uncertainty in terms of P&L sensitivity and observed order flow volatility. This effect becomes higher, the longer the waiting time in between orders. |
| Keywords: | algorithmic trading; hidden Markov model; high-frequency trading; Market making; piecewise-deterministic Markov processes; stochastic filtering; stochastic optimal control; viscosity solutions |
| JEL: | L81 F3 G3 |
| Date: | 2020–05–12 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:104612&r= |
| By: | Stefano Mengoli (Università di Bologna); Marco Pagano (University of Naples Federico II, CSEF and EIEF.); Pierpaolo Pattitoni (Università di Bologna) |
| Abstract: | Retail investors pay over twice as much attention to local companies than non-local ones, based on Google searches. News volume and volatility amplify this attention gap. Attention appears causally related to perceived proximity: first, acquisition by a nonlocal company is associated with less attention by locals, and more by nonlocals close to the acquirer; second, COVID-19 travel restrictions correlate with a drop in relative attention to nonlocal companies, especially in locations with fewer ights after the outbreak. Finally, local attention predicts volatility, bid-ask spreads and nonlocal attention, not viceversa. These findings are consistent with local investors having an information-processing advantage. |
| Keywords: | attention, retail investors, local investors, distance, news, liquidity, volatility. |
| JEL: | D83 G11 G12 G14 G50 L86 R32 |
| Date: | 2021–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:sef:csefwp:630&r= |