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.
FreDF improves forecasting by learning in the frequency domain.
This paper reviews deep time-series forecasting focusing on autocorrelation modeling.
Proposes QDF to improve multi-step time-series forecasting.
Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.
The paper examines how market trade values and volumes affect price autocorrelation.
This paper speeds up Gaussian process regression for autocorrelated data.
The paper uncovers the impact of price and payoff autocorrelations in multi-period asset pricing models.
This paper examines autocorrelation in major crypto markets, finding persistent correlations on short time frames.
Proposes adjusting neural network errors for time series forecasting.
Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.
This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalabili…
Novel method discovers causal relations in time series data, even with autocorrelation.
The paper calculates optimal trading turnover in terms of asset liquidity and alpha autocorrelation.
Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees have been recently proposed. This paper extends these robust portfo…
Estimates price elasticity from autocorrelated time series using causal graphs.
Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.
This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker), at economic sectors level of the NUTs III of mainland Portugal, from 1995 to 1999 and from 2000 to 2005 (taking in count the data availability and the Portuguese and European context), c…
A new RL framework handles autocorrelated actions for better learning and stability.
Introduces a new Hawkes model with CARMA(p,q) intensity to better model dependence structures.
Study of autocorrelation times in neural MCMC simulations for the 2D Ising model.
New method improves causal discovery in time series with latent confounders.
Optimizes portfolio with two controls to minimize trades and maintain signal integrity.
As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the timescale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according t…
Paper optimizes trend-following portfolios using autocorrelation models.
The consideration of spatial effects at a regional level is becoming increasingly frequent and the work of Anselin (1988), among others, has contributed to this. This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker) in the NUTs III economic …
In this manuscript we analyse the leading statistical properties of fluctuations of (log) 3-month US Treasury bill quotation in the secondary market, namely: probability density function, autocorrelation, absolute values autocorrelation, and absolute values persistency. We verify that this financial instrument, in spit…
Financial market dynamics is rigorously studied via the exact generalized Langevin equation. Assuming market Brownian self-similarity, the market return rate memory and autocorrelation functions are derived, which exhibit an oscillatory-decaying behavior with a long-time tail, similar to empirical observations. Individ…
This study analyses, through cross-section estimation methods, the influence of spatial effects and human capital in the conditional productivity convergence (product per worker) in the economic sectors of NUTs III of mainland Portugal between 1995 and 2002. To analyse the data, Moran's I statistics is considered, and …
Study non-local isoperimetric energies on spheres using a Riemannian autocorrelation function.
We describe the impact of the intra-day activity pattern on the autocorrelation function estimator. We obtain an exact formula relating estimators of the autocorrelation functions of non-stationary process to its stationary counterpart. Hence, we proved that the day seasonality of inter-transaction times extends the me…
It is the main goal of this article to address the bipartite ranking issue from the perspective of functional data analysis (FDA). Given a training set of independent realizations of a (possibly sampled) second-order random function with a (locally) smooth autocorrelation structure and to which a binary label is random…
This study analyses, through cross-section estimation methods, the influence of spatial effects in the conditional product convergence in the parishes' economies of mainland Portugal between 1991 and 2001 (the last year with data available for this spatial disaggregation level). To analyse the data, Moran's I statistic…
We develop a framework especially suited to the autocorrelation properties observed in financial times series, by borrowing from the physical picture of turbulence. The success of our approach as applied to high frequency foreign exchange data is demonstrated by the overlap of the curves in Figure (1), since we are abl…
We present an original and novel method based on random matrix approach that enables to distinguish the respective role of temporal autocorrelations inside given time series and cross correlations between various time series. The proposed algorithm is based on properties of Wigner eigenspectrum of random matrices inste…
New method identifies whether equity return predictability is due to magnitude shrinkage or directional reversal.
We study the activity, i.e., the number of transactions per unit time, of financial markets. Using the diffusion entropy technique we show that the autocorrelation of the activity is caused by the presence of peaks whose time distances are distributed following an asymptotic power law which ultimately recovers the Pois…
AUCRSS detects change points in partially observed multivariate autocorrelated data.
In this paper we propose new algorithm to reduce autocorrelation in Markov chain Monte-Carlo algorithms for euclidean field theories on the lattice. Our proposing algorithm is the Hybrid Monte-Carlo algorithm (HMC) with restricted Boltzmann machine. We examine the validity of the algorithm by employing the phi-fourth t…
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
Historical daily data for eleven years of the fifty constituent stocks of the NIFTY index traded on the National Stock Exchange have been analyzed to check for the stylized facts in the Indian market. It is observed that while some stylized facts of other markets are also observed in Indian market, there are significan…
The mean-field variant of the model of limit order driven market introduced recently by Maslov is formulated and solved. The agents do not have any strategies and the memory of the system is kept within the order book. We show that he evolution of the order book is governed by a matrix multiplicative process. The resul…
Improved deep probabilistic time series forecasting by learning error autocorrelation.
It is common to subsample Markov chain output to reduce the storage burden. Geyer (1992) shows that discarding out of every observations will not improve statistical efficiency, as quantified through variance in a given computational budget. That observation is often taken to mean that thinning MCMC output ca…
In this paper we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving-average (SARMA) models under the assumption that the errors are uncorrelated but not necessarily independent.We relax the standard independence assumption on the error term in order to extend the range …
Improved online changepoint detection for autocorrelated data.
New GLS estimator handles high-dimensional data with autocorrelated errors.
New adaptive temperature selection improves parallel tempering efficiency.
Reducing volatility proxy improves apparent market correlation dynamics.