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 uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
Using high-frequency time series of stock prices and share volumes sizes from January 2002-May 2009, this paper investigates whether the effects of the onset of high-frequency trading, most prominent since 2005, are apparent in the dynamics of the dollar traded volume. Indeed it is found in almost all of 14 heavily tra…
Study predicts price predictability in ultra-high frequency financial data using entropy tests.
Model assesses systemic risk in crude oil and gasoline futures markets.
Study uses sentiment analysis to predict implied volatility surface, improving prediction accuracy.
A new model forecasts optimal portfolio weights from high-frequency data.
Study financial markets using synchronization measures and clustering algorithms.
Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability …
Estimates financial networks using high-frequency trade data.
sgboost reduces variable selection bias in boosting with balanced group selection.
We investigated financial market data to determine which factors affect information flow between stocks. Two factors, the time dependency and the degree of efficiency, were considered in the analysis of Korean, the Japanese, the Taiwanese, the Canadian, and US market data. We found that the frequency of the significant…
New method for embedding large networks without attributes, achieving state-of-the-art performance.
According to the leading models in modern finance, the presence of intraday lead-lag relationships between financial assets is negligible in efficient markets. With the advance of technology, however, markets have become more sophisticated. To determine whether this has resulted in an improved market efficiency, we inv…
Log-normal continuous random cascades form a class of multifractal processes that has already been successfully used in various fields. Several statistical issues related to this model are studied. We first make a quick but extensive review of their main properties and show that most of these properties can be analytic…
We show that typical behaviors of market participants at the high frequency scale generate leverage effect and rough volatility. To do so, we build a simple microscopic model for the price of an asset based on Hawkes processes. We encode in this model some of the main features of market microstructure in the context of…
New method explains computational barriers in high-dimensional statistical models.
We empirically analyze the scaling properties of daily Foreign Exchange rates, Stock Market indices and Bond futures across different financial markets. We study the scaling behaviour of the time series by using a generalized Hurst exponent approach. We verify the robustness of this approach and we compare the results …
Thanks to the access to labeled orders on the Cac40 index future provided by Euronext, we are able to quantify market participants contributions to the volatility in the diffusive limit. To achieve this result we leverage the branching properties of Hawkes point processes. We find that fast intermediaries (e.g., market…
Paper develops methods for estimating and simulating a Student-t Lévy regression model.
Maximum likelihood estimation applied to high-frequency data allows us to quantify intermittency in the fluctu- ations of asset prices. From time records as short as one month these methods permit extraction of a meaningful intermittency parameter λ characterising the degree of volatility clustering of asset prices. We…
This study examines how financial tick data becomes more random with time aggregation.
We study the spectral properties of curl, a linear differential operator of first order acting on differential forms of appropriate degree on an odd-dimensional closed oriented Riemannian manifold. In three dimensions its eigenvalues are the electromagnetic oscillation frequencies in vacuum without external sources. In…
Study finds time-varying volatility and multifractality in Bitcoin, with asymmetry weakening as market efficiency increases.
Changes in collateralization have been implicated in significant default (or near-default) events during the financial crisis, most notably with AIG. We have developed a framework for quantifying this effect based on moving between Merton-type and Black-Cox-type structural default models. Our framework leads to a singl…
The scaling properties encompass in a simple analysis many of the volatility characteristics of financial markets. That is why we use them to probe the different degree of markets development. We empirically study the scaling properties of daily Foreign Exchange rates, Stock Market indices and fixed income instruments …
A new approach of solving the ill-conditioned inverse problem for analytical continuation is proposed. The root of the problem lies in the fact that even tiny noise of imaginary-time input data has a serious impact on the inferred real-frequency spectra. By means of a modern regularization technique, we eliminate redun…
We study the complexity of training neural network models with one hidden nonlinear activation layer and an output weighted sum layer. We analyze Gradient Descent applied to learning a bounded target function on real-valued inputs. We give an agnostic learning guarantee for GD: starting from a randomly initialized …
For a dataset of label-count pairs, an anonymized histogram is the multiset of counts. Anonymized histograms appear in various potentially sensitive contexts such as password-frequency lists, degree distribution in social networks, and estimation of symmetric properties of discrete distributions. Motivated by these app…
HyFAD improves time series imputation by combining time and frequency diffusion.
A study ranks critical Lean Six Sigma tools for implementation in Portuguese companies.
The paper models financial order books using geometric shears and directional liquidity.
SSMs have a built-in bias towards low-frequency components, which can be adjusted.
Geometrically interprets frequency in electric circuits.
In this paper, we explore the detection of clusters of stocks that are in synergy in the Indian Stock Market and understand their behaviour in different circumstances. We have based our study on high frequency data for the year 2014. This was a year when general elections were held in India, keeping this in mind our da…
We show that degenerate horizons exhibit a new trapping effect. Specifically, we obtain a non-degenerate Morawetz estimate for the wave equation in the domain of outer communications of extremal Reissner-Nordstrom up to and including the future event horizon. We show that such an estimate requires 1) a higher degree of…
Trading affects grid frequency fluctuations, making them more extreme.
Study on frequencies of non-simple curves in surfaces of large genus.
CNNs show sensitivity to low-frequency signals due to image frequency distribution.
The paper examines how parabolic frequency behaves under Ricci flow and Ricci-harmonic flow on manifolds.
New method constrains CNN filter frequencies to improve robustness.
Paper extends SI method for detecting CPs in complex systems' frequency domain.
We present a careful analysis of possible issues on the application of the self-excited Hawkes process to high-frequency financial data. We carefully analyze a set of effects leading to significant biases in the estimation of the "criticality index" n that quantifies the degree of endogeneity of how much past events tr…
The paper defines a frequency for mean curvature flow and proves its monotonicity.
Paper defines parabolic frequency for Ricci flow solutions, proving monotonicity and uniqueness.
Proves monotonicity of parabolic frequency on all manifolds without curvature assumptions.
New Fourier-based diffusion model improves high-frequency generation quality.
Proposes a conservative LR estimator for infrequent data near a frequency threshold.
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…