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on Market Microstructure |
| By: | Oleg Sokolinskiy |
| Abstract: | This paper estimates trading costs in the off-the-run Treasury market using comprehensive transactions data and machine learning techniques. The analysis reveals several key findings that enhance the understanding of the off-the-run Treasury market liquidity. First, the indicative bid-ask spread is shown to be a biased measure of liquidity, even when not considering transaction volume. Specifically, bid-ask spreads systematically overstate trading costs of more seasoned Treasuries, and the liquidity of benchmark, on-the-run securities affects how off-the-run bid-ask spreads map to trading costs. Second, the paper demonstrates that trading costs may scale non-monotonically with transaction volume, which suggests selective, opportunistic liquidity-taking. Additionally, transaction size has greater effect on off-the-run securities' trading costs when benchmark, on-the-run liquidity is lower. Finally, indicative bid-ask spreads may notably overstate trading costs for larger orders of relatively less liquid securities. These findings contribute to our understanding of actual liquidity in the off-the-run Treasury market, while highlighting the limitations of a traditional liquidity measure. By providing a more nuanced view of trading costs, this study contributes valuable insights for supporting financial stability and optimal asset allocation. |
| Keywords: | Liquidity; Treasury market; Off-the-run; Effective bid-ask spread |
| JEL: | G10 G12 |
| Date: | 2025–07–07 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-49 |
| By: | Gao, Can; Han, Brandon Yueyang |
| Abstract: | We demonstrate the asset pricing implications of investors' belief heterogeneity in the frequency of news arrival and its joint impact with heterogeneous beliefs about news content. Investors trade volatility derivatives against each other to speculate on the rate of news arrival: greater disagreement of this kind gives rise to more extreme derivative positions. When disagreement about news arrival frequency is low, volatility exhibits mean reversion because extreme optimists and pessimists incur substantial wealth losses amid intense market swings. In contrast, high disagreement about the news arrival rate leads to volatility persistence. When news is absent in such environments, volatility sellers dominate, and extreme payoffs are underweighted in the formation of market expectations, resulting in lower implied volatility. In this context, "no news" effectively becomes good news for risky asset valuations. |
| Keywords: | News arrival, heterogeneous beliefs, derivatives, volatility |
| JEL: | G11 G12 D83 D84 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:320435 |
| By: | Daan Schoemaker (Vrije Universiteit Amsterdam and Tinbergen Institute); André Lucas (Vrije Universiteit Amsterdam and Tinbergen Institute); Anne Opschoor (Vrije Universiteit Amsterdam and Tinbergen Institute) |
| Abstract: | We investigate the conditional tail behaviour of asset price changes at high (10-second) frequencies using a new dynamic model for integer-valued tickdata. The model has fat tails, scale dynamics, and allows for possible over- or under-representation of zero price changes. The model can be easily estimated using standard maximum likelihood methods and accommodates both polynomially (fat) and geometrically declining tails. In an application to stock, cryptocurrency and foreign exchange markets during the COVID-19 crisis, we find that conditional fat-tailedness is empirically important for many assets, even at such high frequencies. The new model outperforms the thin-tailed (zero-initiated) dynamic benchmark Skellam model by a wide margin, both insample and out-of-sample. |
| Keywords: | high frequency tick data, polynomial tails, discrete data, Hurwitz zeta function, score-driven dynamics |
| JEL: | C22 C46 C58 |
| Date: | 2025–06–26 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20250039 |
| By: | I. Kalaitzoglou (Audencia Business School) |
| Abstract: | This paper revisits the informational efficiency of the EU ETS at a micro level, by introducing a novel time variant structural decomposition of variance. The new modelling introduces GARCH-like effects into a structural price modelling. With this, all variance components, including public information and price discreteness, can be estimated, for the first time, in a continuously updated setup that is free of sampling bias. The empirical findings report that although all variance components decrease in magnitude, this is primarily due to higher overall market liquidity that results in less price discovery per trade. On a proportional basis, though, the EU ETS appears to be increasingly inefficient prior to the introduction of MiFID II rules, with the situation reversing after their implementation. This is evidence that transparency is vital in rendering emission allowances a policy rather than a speculative instrument. |
| Keywords: | EU ETS MiFID II Algorithmic trading |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05133749 |
| By: | Papastaikoudis, I.; Watson, J.; Lestas, I. |
| Abstract: | This paper presents a cyber-physical systems (CPS) framework to model the interplay between market price dynamics and social belief formation in a decentralized setting. The physical layer captures the evolution of prices through a networked market system governed by linear supply, demand, and crossprice elasticity relationships. The cyber layer represents belief formation via a hypergraph-structured learning model, where agents update expectations through distributed Kalman filters based on noisy price observations and group-level interactions. We analyze how informational frictions—driven by social structure, media influence, or cognitive limitations—induce delays in belief con-vergence to equilibrium prices. These delays, in turn, generate dynamic welfare losses due to suboptimal economic decisions. By linking convergence rates to hypergraph Laplacian spectra, we show how group-level information structures determine the speed and equity of learning processes. Our findings provide a theoretical foundation for studying misinformation and its economic costs in markets shaped by decentralized learning and social influence. |
| Keywords: | Cybernetics of Economic Networks, Distributed Kalman filter, Social Welfare |
| JEL: | C32 D47 D85 |
| Date: | 2025–06–20 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2546 |