Paper detects intensity bursts in financial data using Hawkes processes.
problem Detecting and analyzing intensity bursts in high-frequency financial data.
method Proposes a novel Hawkes process-based method for detecting intensity bursts in financial data.
result Demonstrates the effectiveness of the method in detecting intensity bursts in FX markets.
Study uses multidimensional SE-NBD process to analyze default portfolios and identify shock amplification.
problem Analyzing interactions and shock propagation in default portfolios with multiple sectors.
method Applied multidimensional self-exciting negative binomial distribution (SE-NBD) process to 13 sectors.
result Identified upstream and downstream sectors, showing shock amplification in default portfolios.
Model detects market anomalies using a Hawkes process with hidden Markov chain.
problem Detecting high-frequency market manipulation in cryptocurrency trades.
method Developed a Markov-modulated Hawkes process with piecewise constant excitation kernels.
result Demonstrated the model's effectiveness in detecting suspicious trading activities.
A new method uses burst and inter-burst duration to test long-range memory in financial markets.
problem Varying results from long-range memory estimators in financial markets.
method Burst and inter-burst duration statistical analysis of limit order book data.
result The new method provides a more reliable evaluation of the Hurst exponent.
The paper confirms two groups of gamma-ray bursts using a new nonparametric metric.
problem Determining the number of inherent groups in gamma-ray bursts.
method A new nonparametric interpoint distance-based measure, combined with clustering methods.
result Confirms two groups of short and long gamma-ray bursts.
Study finds financial market data follows power-law exponents typical of stochastic processes.
problem Testing long-range memory in financial markets.
method Analyzed empirical return and trading activity time series from Forex.
result Power-law exponents of burst and inter-burst duration probability density functions are close to 3/2.
Consensual model explains spurious long-range memory in financial markets.
problem Understanding the origin of long-range memory in financial volatility.
method Non-linear stochastic differential equations.
result Empirical burst and inter-burst duration statistics can be explained by non-linear models.
MKPN predicts varying-sized kernels for burst image denoising.
problem Denoising burst images corrupted by noise.
method Deep neural network (MKPN) predicts and fuses kernels of varying sizes.
result MKPN outperforms state-of-the-art on synthetic datasets.
Model optimal liquidation in asset bubbles with varying entry times.
problem Optimal liquidation in asset bubbles with variable entry times and exogenous crashes.
method Mean field game (MFG) with varying entry times and progressive enlargement of filtrations.
result Existence of MFG equilibria and decomposition of equilibrium strategies.
New algorithm clusters GRBs into two groups: short and long duration.
problem Determining the number of clusters in gamma-ray bursts.
method Completely parameter-free clustering algorithm.
result Clusters indicate two main groups: short and long duration GRBs.
In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between th…
Paper proposes a new method to handle missing data in medical records using sequential variational autoencoders.
problem Missing data in medical records due to sensor off-times and uneven data collection.
method Sequential variational autoencoders (VAEs) with a new methodology called Shi-VAE.
result Shi-VAE achieves the best performance in terms of both metrics compared to state-of-the-art methods.
This working paper analyzes the gold price dynamics on the basis of methodology developed by Didier Sornette. Our calculations indicate that this dynamics is close to the one of the "bubbles" studied by Sornette and that the most probable timing of the "burst of the gold bubble" is April - June 2011. The obtained resul…
Quarter-hour market bursts predict algorithmic trading and returns in crypto futures.
problem Predicting returns in cryptocurrency futures markets using quarter-hour market bursts.
method Analysis of trade data and Autocorrelation Map to identify and quantify algorithmic trading activity.
result Quarter-hour market bursts are associated with algorithmic trading and can predict returns.
Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media, and an active line of research has studied how such cascades, which form as content is reshared from person to person, develop and subside. In this paper, we perform a large-scale analysis of cascades on Faceb…
Long-range memory in non-equilibrium systems is explained by spurious memory in SDEs, not fBm.
problem Understanding the origin of long-range memory in non-equilibrium systems.
method Analysis of burst and inter-burst duration PDFs in SDE-driven processes.
result Processes described by SDEs exhibit a power-law exponent of 3/2 for burst or inter-burst duration PDFs.
A new model adapts Hurst parameter in real-time for volatility forecasting.
problem Capturing volatility dynamics and clustering in financial markets.
method Rough Bergomi model with EWMA-driven time-dependent Hurst parameter.
result Empirical validation shows superior performance in diverse asset classes.
New algorithms recover differential equations from short bursts of data.
problem Locally recover unknown governing differential equations from measurement data.
method Approximate governing equations using standard basis functions and short bursts of trajectory data.
result Effective numerical algorithms recover accurate governing equations from short bursts of data.
Deep neural net improves low-light image denoising.
problem Noise in low-light images captured by mobile devices.
method Intelligent integration of multiple short noisy frames using a recurrent fully convolutional deep neural net (CNN).
result Achieves state-of-the-art denoising results on burst datasets.
We investigate large changes, bursts, of the continuous stochastic signals, when the exponent of multiplicativity is higher than one. Earlier we have proposed a general nonlinear stochastic model which can be transformed into Bessel process with known first hitting (first passage) time statistics. Using these results w…
Develops a hybrid MtFA approach for high-dimensional data clustering.
problem Scalability issues in traditional MtFA estimation methods for high-dimensional data.
method Integrates profile likelihood method into EM framework for efficient parameter estimation.
result Demonstrates superior computational efficiency and clustering accuracy compared to existing methods.
New deep learning model estimates scattering timescale of FRBs efficiently.
problem Estimating scattering timescale of fast radio bursts (FRBs) is a bottleneck.
method Multimodal Transformer Based Generic Mixture Density Network (MT-GMDN) that ingests dynamic spectrum and timeseries profile.
result Achieves 94% R2 on expected value of τ for measurable scattering. Syncytial clustering merges groups from standard algorithms to reveal complex data structures.
problem Challenges in finding clusters with irregular structures.
method Estimates nonparametric overlap between clusters and merges groups with high overlap.
result Always a top performer in identifying groups with regular and irregular structures.
TiK-means extends K-means for skewed groups, revealing structured clusters.
problem Clustering skewed groups using traditional K-means.
method Introduces TiK-means, a modified K-means algorithm that estimates skewness-transformation parameters.
result Reveals structured clusters that explain the skewness of groups.
The study identifies and analyzes different market regimes in equity markets using advanced signal processing techniques.
problem Understanding and quantifying the dynamics of different market regimes in equity markets.
method Data-driven Hilbert--Huang Transform for regime identification, Holo--Hilbert Spectral Analysis for profiling, and Variable-Length Markov Chains for return dynamics modeling.
result Developed markets normalize more effectively as stress subsides, while developing markets retain residual tail dependence and downside persistence.
Paper proposes a new Markov model for efficient PLC system design.
problem Efficient estimation of Markov model parameters for bursty error channels.
method Introduced a Block Diagonal Markov model and a modified Baum-Welch algorithm.
result Efficient estimation of state transition matrix Λ for PLC system design. Method extracts stochastic systems with Lévy noise from data.
problem Identifying stochastic dynamical systems with Lévy noise from short data.
method Estimate Lévy jump measure and noise intensity, approximate drift coefficient.
result Accurate and effective method for discovering stochastic laws.
Dr.VOT measures both positive and negative VOTs accurately in natural speech.
problem Accurate measurement of VOT in natural speech.
method Deep-learning model based on RNNs for structured prediction.
result Dr.VOT improves over state-of-the-art performance on VOT estimation.
Proposes a new birth-death process for better modeling of population dynamics.
problem Models of population or opinion dynamics with spurious long-range memory.
method Introduces Bessel-like birth-death process to address the spurious long-range memory.
result Derives equations for the burst and inter-burst duration of the new process.
Two classes of gamma-ray bursts (GRBs), short and long, have been determined without any doubts, and are usually ascribed to different progenitors, yet these classes overlap for a variety of descriptive parameters. A subsample of 46 long and 22 short Fermi GRBs with estimated Hurst Exponents (HEs), complemented by mi…
The refugee crisis is perhaps the single most challenging problem for Europe today. Hundreds of thousands of people have already traveled across dangerous sea passages from Turkish shores to Greek islands, resulting in thousands of dead and missing, despite the best rescue efforts from both sides. One of the main reaso…
Market makers use a new method to predict and respond to RFQs in the OTC market.
problem Predicting and managing RFQs in the OTC market with Hawkes kernels.
method Developed a hierarchy of Volterra-Riccati approximations for path-dependent control problems.
result The state-feedback Volterra-Riccati policy closely tracks the exact benchmark and improves inventory and P&L risk control.
In this paper, we quantitatively investigate the statistical properties of a statistical ensemble of stock prices. We selected 1200 stocks traded on the Tokyo Stock Exchange, and formed a statistical ensemble of daily stock prices for each trading day in the 3-year period from January 4, 1999 to December 28, 2001, corr…
Amid the current financial crisis, there has been one equity index beating all others: the Shanghai Composite. Our analysis of this main Chinese equity index shows clear signatures of a bubble build up and we go on to predict its most likely crash date: July 17-27, 2009 (20%/80% quantile confidence interval).
In the aftermath of the burst of the ``new economy'' bubble in 2000, the Federal Reserve aggressively reduced short-term rates yields in less than two years from 6.5% to 1.25% in an attempt to coax forth a stronger recovery of the US economy. But, there is growing apprehension that this is creating a new bubble in real…
In this paper, we quantitatively investigate the properties of a statistical ensemble of stock prices. We focus attention on the relative price defined as X(t)=S(t)/S(0), where S(0) is the initial price. We selected approximately 3200 stocks traded on the Japanese Stock Exchange and formed a statistical ensem…
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…
PINNs solve neuronal parameter and state estimation problems with limited data.
problem Estimating parameters and hidden state variables from noisy partial data in multiscale neuronal models.
method Physics-informed neural networks (PINNs) for joint state and parameter estimation.
result PINNs deliver robust and accurate parameter inference and state reconstruction, even with limited data.
Efficiently clusters incomplete data without imputation or full EM, faster and more accurate.
problem Clustering partially recorded data efficiently.
method Model-based approach using multivariate t-distributions, considering only observed values.
result Approach is more accurate and computationally efficient than alternatives.
It is widely believed that fluctuations in transaction volume, as reflected in the number of transactions and to a lesser extent their size, are the main cause of clustered volatility. Under this view bursts of rapid or slow price diffusion reflect bursts of frequent or less frequent trading, which cause both clustered…
Paper proposes using CNN for stock trading with data normalization.
problem Improving stock trading accuracy in volatile markets.
method Developed CNN-based trading framework with novel data normalization.
result CNN-based framework outperforms other methods on 29 stocks.
Divestment from fossil fuels can accelerate climate policy, study finds.
problem Achieving Paris climate agreement requires reducing fossil fuel reserves.
method Stochastic agent-based model of financial market and investors' beliefs.
result Small share of socially responsible investors can initiate decarbonization.
Neural Diffusion Intensity Models simplify Cox processes inference.
problem Intractable nonparametric estimation and posterior inference of latent stochastic intensity in Cox processes.
method Variational framework using neural SDEs, with theoretical guarantee of ELBO maximization coinciding with maximum likelihood estimation.
result Accurate recovery of latent intensity dynamics and posterior paths with significant speedup.
Examines GARCH intensity model for risk-neutral option pricing.
problem Volatility clustering, leverage effect, and conditional asymmetry in financial returns.
method Risk-neutral option pricing method under GARCH intensity model.
result Flexibility in volatility changes according to probability measure.
Bayesian approach for inhomogeneous Poisson process intensity estimation.
problem Intractable integral in likelihood of Gaussian Cox process.
method Joint modeling of intensity and cumulative intensity as transformed Gaussian process; exact MCMC sampler.
result Exact posterior inference without approximations.
Method uses deep learning to estimate traffic intensity.
problem Estimating stochastic intensity of traffic processes.
method Deep neural networks for nonlinear filtering.
result Deep learning method accurately estimates traffic intensity.
A new kernel method improves Poisson process intensity estimation.
problem Estimating intensity functions of inhomogeneous Poisson processes.
method Kernel method-based intensity estimator using least squares loss.
result K2IE achieves comparable predictive performance with improved efficiency. A taxonomy of large financial crashes proposed in the literature locates the burst of speculative bubbles due to endogenous causes in the framework of extreme stock market crashes, defined as falls of market prices that are outlier with respect to the bulk of drawdown price movement distribution. This paper goes on dee…