A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study shows integrating OFI from multiple levels improves price impact explanation but not forecasting.
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
We construct a price impact model between stocks in a correlated market. For the price change of a given stock induced by the short-run liquidity of this stock itself and of the information about other stocks, we introduce a self- and a cross-impact function of the time lag. We model the average cross-response function…
Two models are identified for robust cross-impact analysis.
Investigates cross-impact kernels for financial asset prices.
We extend the "No-dynamic-arbitrage and market impact"-framework of Jim Gatheral [Quantitative Finance, 10(7): 749-759 (2010)] to the multi-dimensional case where trading in one asset has a cross-impact on the price of other assets. From the condition of absence of dynamical arbitrage we derive theoretical limits for t…
Estimates cross-impact on derivatives markets using E-Mini futures and options.
Estimates self- and cross-impact concavity and decay patterns in financial markets.
Optimal portfolio choice with cross-impact propagators, solving complex equations.
The study identifies features making cross-impact relevant in explaining price variance of US assets.
We model the impact costs of a strategy that trades a basket of correlated instruments, by extending to the multivariate case the linear propagator model previously used for single instruments. Our specification allows us to calibrate a cost model that is free of arbitrage and price manipulation. We illustrate our resu…
We reconsider the multivariate Kyle model in a risk-neutral setting with a single, perfectly informed rational insider and a rational competitive market maker, setting the price of n correlated securities. We prove the unicity of a symmetric, positive definite solution for the impact matrix and provide insights on its …
Model explains yield curve dynamics using order flow shocks.
A risk-averse agent hedges her exposure to a non-tradable risk factor using a correlated traded asset and accounts for the impact of her trades on both factors. The effect of the agent's trades on is referred to as cross-impact. By solving the agent's stochastic control problem, we obtain a closed-form expr…
Study optimizes trading in multiple assets with cross-effects.
Paper solves optimal portfolio deleveraging with cross asset impacts.
Algorithm detects lead-lag relationships in multivariate time series.
Detects lead-lag clusters in US equity market time series.
Deep learning methods improve time series forecasting by optimizing lag selection.
DOLCE improves off-policy evaluation and learning by decomposing effects.
We develop methods to estimate lag and parameters for multiple stable autoregressive processes.
NAPLES resolves lead-lag analysis challenges in non-synchronous high-frequency data.
Method detects lead-lag relationships in multivariate time series.
Modeling delayed Granger causality in Hawkes processes.
The study finds that factor momentum is significant only at short lags compared to stock momentum.
The existence of time-lagged cross-correlations between the returns of a pair of assets, which is known as the lead-lag relationship, is a well-known stylized fact in financial econometrics. Recently some continuous-time models have been proposed to take account of the lead-lag relationship. Such a model does not follo…
We propose a novel framework to investigate lead-lag relationships between two financial assets. Our framework bridges a gap between continuous-time modeling based on Brownian motion and the existing wavelet methods for lead-lag analysis based on discrete-time models and enables us to analyze the multi-scale structure …
Novel framework detects lead-lag relationships in Chinese A-share market.
Abstract: A new approach to technical indicators without lag.
New method for estimating lead-lag times between non-synchronously observed point processes.
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional a…
We provide an asymptotic expansion of the value function of a multidimensional utility maximization problem from consumption with small non-linear price impact. In our model cross-impacts between assets are allowed. In the limit for small price impact, we determine the asymptotic expansion of the value function around …
To reduce the long training time of large deep neural network (DNN) models, distributed synchronous stochastic gradient descent (S-SGD) is commonly used on a cluster of workers. However, the speedup brought by multiple workers is limited by the communication overhead. Two approaches, namely pipelining and gradient spar…
New technique identifies lead-lag relationships in FX market during pandemic.
This study examines lead-lag relationships in Chinese futures markets using high-frequency data.
Clusters asset classes to identify lead-lag relationships in market regimes.
Lead-lag relationships among assets represent a useful tool for analyzing high frequency financial data. However, research on these relationships predominantly focuses on correlation analyses for the dynamics of stock prices, spots and futures on market indexes, whereas foreign exchange data have been less explored. To…
We present the symmetric thermal optimal path (TOPS) method to determine the time-dependent lead-lag relationship between two stochastic time series. This novel version of the previously introduced TOP method alleviates some inconsistencies by imposing that the lead-lag relationship should be invariant with respect to …
Pairs Trading is carried out in the financial market to earn huge profits from known equilibrium relation between pairs of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead-lag relationship has been empirically studied in various financial markets. Earli…
We study the probability distribution of stock returns at mesoscopic time lags (return horizons) ranging from about an hour to about a month. While at shorter microscopic time lags the distribution has power-law tails, for mesoscopic times the bulk of the distribution (more than 99% of the probability) follows an expon…
One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their)…
We employ the thermal optimal path method to explore both the long-term and short-term interaction patterns between the onshore CNY and offshore CNH exchange rates (2012-2015). For the daily data, the CNY and CNH exchange rates show a weak alternate lead-lag structure in most of the time periods. When CNY and CNH displ…
Financial markets worldwide do not have the same working hours. As a consequence, the study of correlation or causality between financial market indices becomes dependent on wether we should consider in computations of correlation matrices all indices in the same day or lagged indices. The answer this article proposes …
In our previous study we have presented an approach to studying lead--lag effect in financial markets using information and network theories. Methodology presented there, as well as previous studies using Pearson's correlation for the same purpose, approached the concept of lead--lag effect in a naive way. In this pape…
Methodology to measure lag relevance in time series models.
Bayesian framework selects features and lags for time series forecasting.
Modeling lead-lag relationship between two text corpora for improved topic modeling.
Variable selection in linear models plays a pivotal role in modern statistics. Hard-thresholding methods such as regularization are theoretically ideal but computationally infeasible. In this paper, we propose a new approach, called the LAGS, short for "least absulute gradient selector", to this challenging yet i…