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on Market Microstructure |
| By: | Sagade, Satchit; Scharnowski, Stefan; Theissen, Erik; Westheide, Christian |
| Abstract: | We examine the impact of increasing competition among the fastest traders by analyzing a new low-latency microwave network connecting exchanges trading the same stocks. Using a difference-in-differences approach comparing German stocks with similar French stocks, we find improved market integration, faster incorporation of stock-specific information, and an increased contribution to price discovery by the smaller exchange. Liquidity worsens for large caps due to increased sniping but improves for mid caps due to fast liquidity provision. Trading volume on the smaller exchange declines across all stocks. We thus uncover nuanced effects of fast trader participation that depend on their prior involvement. |
| Keywords: | Latency, Market Fragmentation, Arbitrage, Liquidity, Price Efficiency, High-Frequency Trading |
| JEL: | G10 G14 G15 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:303051 |
| By: | Li, D.; Linton, O. B.; Zhang, H. |
| Abstract: | We propose a new estimator of high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure from noisy and asynchronous high-frequency data collected for an ultra-large number of assets. The noise processes are allowed to be temporally correlated, heteroskedastic, asymptotically vanishing and dependent on the efficient prices. We define a kernel-weighted pre-averaging method to jointly tackle the microstructure noise and asynchronicity issues, and we obtain uniformly consistent estimates for latent prices. We impose a continuous-time factor model with time-varying factor loadings on the price processes, and estimate the common factors and loadings via a local principal component analysis. Assuming a uniform sparsity condition on the idiosyncratic volatility structure, we combine the POET and kernel-smoothing techniques to estimate the spot volatility matrices for both the latent prices and idiosyncratic errors. Under some mild restrictions, the estimated spot volatility matrices are shown to be uniformly consistent under various matrix norms. We provide Monte-Carlo simulation and empirical studies to examine the numerical performance of the developed estimation methodology. |
| Keywords: | Continuous Semimartingale, Kernel Smoothing, Microstructure Noise, PCA, Spot Volatility, Time-Varying Factor Models |
| JEL: | G12 G14 C14 |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camjip:2424 |
| By: | Li, D.; Linton, O. B.; Zhang, H. |
| Abstract: | We propose a new estimator of high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure from noisy and asynchronous high-frequency data collected for an ultra-large number of assets. The noise processes are allowed to be temporally correlated, heteroskedastic, asymptotically vanishing and dependent on the efficient prices. We define a kernel-weighted pre-averaging method to jointly tackle the microstructure noise and asynchronicity issues, and we obtain uniformly consistent estimates for latent prices. We impose a continuous-time factor model with time-varying factor loadings on the price processes, and estimate the common factors and loadings via a local principal component analysis. Assuming a uniform sparsity condition on the idiosyncratic volatility structure, we combine the POET and kernel-smoothing techniques to estimate the spot volatility matrices for both the latent prices and idiosyncratic errors. Under some mild restrictions, the estimated spot volatility matrices are shown to be uniformly consistent under various matrix norms. We provide Monte-Carlo simulation and empirical studies to examine the numerical performance of the developed estimation methodology. |
| Keywords: | Continuous Semimartingale, Kernel Smoothing, Microstructure Noise, PCA, Spot Volatility, Time-Varying Factor Models |
| JEL: | G12 G14 C14 |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2454 |
| By: | Dominik Schmidt (UP1 UFR02 - Université Paris 1 Panthéon-Sorbonne - École d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne, UP1 - Université Paris 1 Panthéon-Sorbonne); Thomas Stöckl; Stefan Palan |
| Abstract: | Capital markets often regulate insider trading, but whether such regulation aligns with traders' preferences is an open question. This study examined traders' regulation preferences conditional on their prospects of becoming informed. Of 64 referenda, traders decided 41 (64%) against regulation. Moreover, traders' prospects of becoming informed significantly impacted the outcomes of the referenda. In markets in which a group of traders has no chance of receiving inside information, 47% of the referenda are decided against regulation. When all traders could get such information, 81% are. Individual votes reveal that traders who know they will remain uninformed support regulation in 69.27% of the cases, while informed traders do so only 8.33% of the time. Traders who may or may not become informed support regulation 33.33% of the time. |
| Keywords: | Experimental finance, Asset market, Insider trading regulation, Vote/referendum, Distributional preferences, JEL classification: G10 G18 G40 Experimental finance Asset market Insider trading regulation |
| Date: | 2024–08–22 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04692482 |
| By: | Henry Dyer; Michael J. Fleming; Or Shachar |
| Abstract: | Trading activity in benchmark U.S. Treasury securities now concentrates on the last trading day of the month. Moreover, this stepped-up activity is associated with lower transaction costs, as shown by a smaller price impact of trades. We conjecture that increased turn-of-month portfolio rebalancing by passive investment funds that manage relative to fixed-income indices helps explain these patterns. |
| Keywords: | end of month; Portfolio rebalancing; index; Treasury securities |
| JEL: | G12 |
| Date: | 2024–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:98822 |
| By: | Kei Nakagawa; Masanori Hirano; Kentaro Minami; Takanobu Mizuta |
| Abstract: | The AI traders in financial markets have sparked significant interest in their effects on price formation mechanisms and market volatility, raising important questions for market stability and regulation. Despite this interest, a comprehensive model to quantitatively assess the specific impacts of AI traders remains undeveloped. This study aims to address this gap by modeling the influence of AI traders on market price formation and volatility within a multi-agent framework, leveraging the concept of microfoundations. Microfoundations involve understanding macroeconomic phenomena, such as market price formation, through the decision-making and interactions of individual economic agents. While widely acknowledged in macroeconomics, microfoundational approaches remain unexplored in empirical finance, particularly for models like the GARCH model, which captures key financial statistical properties such as volatility clustering and fat tails. This study proposes a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. By mathematically aggregating the micro-structure of these agents, we establish the microfoundations of the GARCH model. We validate this model through multi-agent simulations, confirming its ability to reproduce the stylized facts of financial markets. Finally, we analyze the impact of AI traders using parameters derived from these microfoundations, contributing to a deeper understanding of their role in market dynamics. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.12516 |
| By: | Michael J. Fleming |
| Abstract: | Standard metrics point to an improvement in Treasury market liquidity in 2024 to levels last seen before the start of the current monetary policy tightening cycle. Volatility has also trended down, consistent with the improved liquidity. While at least one market functioning metric has worsened in recent months, that measure is an indirect gauge of market liquidity and suggests a level of current functioning that is far better than at the peak seen during the global financial crisis (GFC). |
| Keywords: | market liquidity; Treasury market; Treasury securities |
| JEL: | G12 |
| Date: | 2024–09–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:98808 |
| By: | Tejas Ramdas; Martin T. Wells |
| Abstract: | In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predicting future market movements. Then, we utilize the information from this successful neural network predictor to pinpoint the individual trades within each data point (trading window) that had the most impact on the optimized neural network's prediction of future price movements. This approach helps us uncover important insights about the heterogeneity in information content provided by trades of different sizes, venues, trading contexts, and over time. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.05192 |