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.
A new method learns high-frequency components for better image reconstruction.
New Fourier-based diffusion model improves high-frequency generation quality.
FMMNN combines sine activations with multi-component, multi-layer structure for high-frequency function approximation.
Using recent advances in the econometrics literature, we disentangle from high frequency observations on the transaction prices of a large sample of NYSE stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to observable charac…
New DMEM models forecast volatility combining low- and high-frequency data.
In this paper, we study the adversarial attack and defence problem in deep learning from the perspective of Fourier analysis. We first explicitly compute the Fourier transform of deep ReLU neural networks and show that there exist decaying but non-zero high frequency components in the Fourier spectrum of neural network…
Robustly detects jumps in high-frequency CIR and CKLS models.
We propose a novel approach that allows to calculate Hilbert transform based complex correlation for unevenly spaced data. This method is especially suitable for high frequency trading data, which are of a particular interest in finance. Its most important feature is the ability to take into account lead-lag relations …
In this paper we will try to assess the multifractality displayed by the high-frequency returns of Madrid's Stock Exchange IBEX35 index. A Multifractal Detrended Fluctuation Analysis shows that this index has a wide singularity spectrum which is most likely caused by its long memory. Our findings also show that this lo…
GNIs induce a regulariser that penalizes high-frequency components in neural network activations.
Calibrates carbon futures option pricing using high-frequency data.
This paper aims to develop new techniques to describe joint behavior of stocks, beyond regression and correlation. For example, we want to identify the clusters of the stocks that move together. Our work is based on applying Kernel Principal Component Analysis(KPCA) and Functional Principal Component Analysis(FPCA) to …
We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. The architecture utilizes two networks: a low resolution network to efficiently approximate low frequency components and a high resolution ne…
A new model forecasts optimal portfolio weights from high-frequency data.
A new model captures irregularly spaced high-frequency prices and their volatility.
Deep neural networks can generalize by reducing high-frequency noise over time, not always following a monotonic learning bias.
Hybrid model combines VAR and neural network for OFI prediction.
The main purpose of this work is to examine the behavior of the implied volatility smiles around jumps, contributing to the literature with a high-frequency analysis of the smile dynamics based on intra-day option data. From our high-frequency SPX S\&P500 index option dataset, we utilize the first three principal compo…
Estimates graph process with high-frequency data, proving asymptotic properties.
This paper models CSI 300 index volatility using machine learning and addresses jump prediction.
Study examines cryptocurrency volatility factors using high-frequency data.
Study predicts intraday stock trading volume using ML models.
Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains. In addition, recent work has shown that constraining the attack space to a low frequency regime is …
We analyzed multifractal properties of 5-minute stock returns from a period of over two years for 100 highly capitalized American companies. The two sources: fat-tailed probability distributions and nonlinear temporal correlations, vitally contribute to the observed multifractal dynamics of the returns. For majority of…
A new search-control strategy improves Dyna's efficiency.
We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most ( 99.6%) of the wavelet coefficients can be neglected for th…
New method estimates volatility for Lévy processes with unbounded jumps efficiently.
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
Paper develops models for better HFT and algorithmic trading.
This study proposes a fully convolutional network (FCN) model for raw waveform-based speech enhancement. The proposed system performs speech enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which dif-fers from most existing denoising methods that process the magnitude spectrum (e.g., log power …
This work analyzes how frequency components affect CNN predictions and robustness.
Cryptocurrency market activity is decomposed into recurring and noise components, revealing patterns tied to macroeconomic reports.
Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with accuracy. In this work, we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased…
HyFAD improves time series imputation by combining time and frequency diffusion.
Study finds anomalies in high-frequency S&P 500 price changes.
SSMs have a built-in bias towards low-frequency components, which can be adjusted.
In quantitative finance, we often model asset prices as semimartingales, with drift, diffusion and jump components. The jump activity index measures the strength of the jumps at high frequencies, and is of interest both in model selection and fitting, and in volatility estimation. In this paper, we give a novel estimat…
New method estimates volatility for processes with jumps of unbounded variation.
Volatility of intra-day stock market indices computed at various time horizons exhibits a scaling behaviour that differs from what would be expected from fractional Brownian motion (fBm). We investigate this anomalous scaling by using empirical mode decomposition (EMD), a method which separates time series into a set o…
We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronol…
In high-frequency financial data not only returns, but also waiting times between consecutive trades are random variables. Therefore, it is possible to apply continuous-time random walks (CTRWs) as phenomenological models of the high-frequency price dynamics. An empirical analysis performed on the 30 DJIA stocks shows …
In high-frequency financial data not only returns, but also waiting times between consecutive trades are random variables. Therefore, it is possible to apply continuous-time random walks (CTRWs) as phenomenological models of the high-frequency price dynamics. An empirical analysis performed on the 30 DJIA stocks shows …
New Fourier transform method handles missing data and asynchronous observations.
We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…
Local convolutions bias neural networks towards high-frequency adversarial examples.
Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most aggressive such practices, and arrive at figures that are surprisingly modest. By "aggressive" we mean any trading …
The study tackles modeling high-frequency financial data using continuous distributions, finding them inadequate.
Deep learning tackles label imbalance in high-frequency trading.