arXiv · arXiv · 2026
Leveraged ETFs (L-ETFs) are exchange-traded funds that achieve price movements several times greater than an index by holding index-linked futures such as Nikkei Stock Average Index futures. It is known that when the price of an L-ETF falls, the L-ETF uses the liquidity of futures to limit the decline through arbitrage trading. Conversely, when the price of a futures contract falls, the futures contract uses the liqu…
Ryuki Hayase, Takanobu Mizuta, Isao Yagi
OpenAlex · European Finance Review · 2014 · cites 64
Abstract Following the “flash crash” on May 6, 2010, warning signals for impending market stress have been in high demand, yet only the VPIN metric of Easley, López de Prado, and O’Hara (ELO) has claimed success. In addition, ELO find the metric useful in predicting short-term volatility. VPIN involves decomposing volume into active buys and sells. We utilize quotes and trade data to construct an accurate trade class…
Torben G. Andersen, Oleg Bondarenko
arXiv · arXiv · 2026
Building event-conditioned market models requires separating macro-event labels from persistent microstructure state. We study this distinction in Binance BTCUSDT and ETHUSDT futures from 2023-2026, combining top-20 L2 order book data, trade-flow records, and macro-event windows. We define a supervised discrete L2 liquidity-state transition task, distinct from latent-regime detection and price-direction prediction, a…
Joohyoung Jeon
arXiv · arXiv · 2026
We introduce $\textbf{Slippage-at-Risk (SaR)}$, a quantitative framework for measuring liquidity risk in perpetual futures exchanges. Unlike backward-looking metrics such as Value-at-Risk computed on historical returns or realized deficit distributions, SaR provides a \emph{forward-looking} assessment of liquidation execution risk derived from current order book microstructure. The framework comprises three complemen…
Otar Sepper
arXiv · arXiv · 2026
We propose a microstructural model for the order flow in financial markets that distinguishes between {\it core orders} and {\it reaction flow}, both modeled as Hawkes processes. This model has a natural scaling limit that reconciles a number of salient empirical properties: persistent signed order flow, rough trading volume and volatility, and power-law market impact. In our framework, all these quantities are pinne…
Johannes Muhle-Karbe, Youssef Ouazzani Chahdi, Mathieu Rosenbaum, Grégoire Szymanski
arXiv · arXiv · 2025
An automated market maker (AMM) provides a method for creating a decentralized exchange on the blockchain. For this purpose, individual investors lend liquidity to the AMM pool in exchange for a stream of fees earned from its operations as a market maker. Within this work, we reinterpret the loss-versus-rebalancing as the implied fee stream generated by an AMM so that a risk-neutral investor is indifferent in the dec…
Maxim Bichuch, Zachary Feinstein
arXiv · arXiv · 2024
The Capital Asset Pricing Model (CAPM) relates a well-diversified stock portfolio to a benchmark portfolio. We insert size effect in the CAPM, capturing the observation that small stocks have higher risk and return than large stocks, on average. Our goal is to make the resulting linear regressions have independent identically distributed Gaussian residuals. In some cases, we find that including the Volatility Index a…
Abraham Atsiwo, Andrey Sarantsev
arXiv · arXiv · 2024
We develop a liquidity-sensitive multivariate volatility framework to improve the estimation of time-varying covariance structures under market frictions. We introduce two novel portfolio-level liquidity measures, liquidity jump and liquidity diffusion, which capture magnitude and volatility of liquidity fluctuation, respectively, and construct liquidity-adjusted return and volatility that reflect real-time liquidity…
Qi Deng
arXiv · arXiv · 2023
We establish innovative liquidity premium measures, and construct liquidity-adjusted return and volatility to model assets with extreme liquidity, represented by a portfolio of selected crypto assets, and upon which we develop a set of liquidity-adjusted ARMA-GARCH/EGARCH models. We demonstrate that these models produce superior predictability at extreme liquidity to their traditional counterparts. We provide empiric…
Qi Deng, Zhong-guo Zhou
arXiv · arXiv · 2023
Introduction: The paper addresses the challenging problem of predicting the short-term realized volatility of the Bitcoin price using order flow information. The inherent stochastic nature and anti-persistence of price pose difficulties in accurate prediction. Methods: To address this, we propose a method that transforms order flow data over a fixed time interval (snapshots) into images. The order flow includes trade…
Artem Lensky, Mingyu Hao
arXiv · arXiv · 2022
This paper considers liquidity as an explanation for the positive association between expected idiosyncratic volatility (IV) and expected stock returns. Liquidity costs may affect the stock returns, through bid-ask bounce and other microstructure-induced noise, which will affect the estimation of IV. We use a novel method (developed by Weaver, 1991) to eliminate microstructure influences from stock closing price-base…
M. Reza Bradrania, Maurice Peat, Stephen Satchell
arXiv · arXiv · 2022
Based on the commentary data of the Shenzhen Stock Index bar on the EastMoney website from January 1, 2018 to December 31, 2019. This paper extracts the embedded investor sentiment by using a deep learning BERT model and investigates the time-varying linkage between investment sentiment, stock market liquidity and volatility using a TVP-VAR model. The results show that the impact of investor sentiment on stock market…
Chenrui Zhang, Xinyi Wu, Hailu Deng, Huiwei Zhang
arXiv · arXiv · 2021
We consider the problem of calculating risk-neutral implied volatilities of European options without relying on option mid prices but solely on bid and ask prices. We provide an approach, based on the conic finance paradigm, that allows to uniquely strip risk-neutral implied volatilities from bid and ask quotes, and that does not require restrictive assumptions. Our methodology also allows to jointly calculate the im…
Matteo Michielon, Asma Khedher, Peter Spreij
arXiv · arXiv · 2020
Previous research has found that high-frequency traders will vary the bid or offer price rapidly over periods of milliseconds. This is a benefit to fast traders who can time their trades with microsecond precision, however it is a cost to the average market participant due to increased trade execution price uncertainty. In this analysis we attempt to construct real-time methods for determining whether the liquidity o…
Matthew Brigida
arXiv · arXiv · 2019
In this paper, we study term structure movements in the spirit of Heath, Jarrow, and Morton [Econometrica 60(1), 77-105] under volatility uncertainty. We model the instantaneous forward rate as a diffusion process driven by a G-Brownian motion. The G-Brownian motion represents the uncertainty about the volatility. Within this framework, we derive a sufficient condition for the absence of arbitrage, known as the drift…
Julian Hölzermann
arXiv · arXiv · 2011
In a market with one safe and one risky asset, an investor with a long horizon, constant investment opportunities, and constant relative risk aversion trades with small proportional transaction costs. We derive explicit formulas for the optimal investment policy, its implied welfare, liquidity premium, and trading volume. At the first order, the liquidity premium equals the spread, times share turnover, times a unive…
Stefan Gerhold, Paolo Guasoni, Johannes Muhle-Karbe, Walter Schachermayer
arXiv · arXiv · 2007
We develop a behavioral model for liquidity and volatility based on empirical regularities in trading order flow in the London Stock Exchange. This can be viewed as a very simple agent based model in which all components of the model are validated against real data. Our empirical studies of order flow uncover several interesting regularities in the way trading orders are placed and cancelled. The resulting simple mod…
Szabolcs Mike, J. Doyne Farmer
arXiv · arXiv · 2026
Loss-versus-Rebalancing (LVR) is the dominant adverse-selection cost borne by liquidity providers on automated market makers. Under geometric Brownian motion, arbitrage profit scales with the probability of a profitable block, which vanishes as the block time $Δt \to 0$; this is the standing argument for ever-shorter blocks. Modeling the reference price instead as a jump-diffusion, I show that the constant-product LV…
Nils Bundi