By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities rev…
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48 results for “inter-trading times”
New CTRW model explains volatility clustering in stock markets.
problem Missing models for long-term memory in time intervals between observations.
method Introduced a new family of CTRWs with correlated waiting times.
result Successfully describes the decay of nonlinear autocorrelation function in stock market returns.
This paper builds a model of high-frequency equity returns by separately modeling the dynamics of trade-time returns and trade arrivals. Our main contributions are threefold. First, we characterize the distributional behavior of high-frequency asset returns both in ordinary clock time and in trade time. We show that wh…
Modeling stock order book dynamics with bouncing GBMs.
problem Capturing the dynamics of order book prices in financial markets.
method Modeling order book bid and ask prices as bouncing geometric Brownian motions.
result The logarithmic trading price process converges to a standard Brownian motion as δ approaches 0.
Study finds strong long-range correlations in financial markets, especially over longer time scales.
problem Understanding long-range correlations in limit order book markets.
method Ultra-high frequency order book data from NASDAQ Nordic, detrended fluctuation analysis (DFA).
result Strong evidence of long-range correlation in inter-event durations, becoming stronger over longer time scales.
We present a simple microstructure model of financial returns that combines (i) the well-known ARFIMA process applied to tick-by-tick returns, (ii) the bid-ask bounce effect, (iii) the fat tail structure of the distribution of returns and (iv) the non-Poissonian statistics of inter-trade intervals. This model allows us…
Analyzes intrinsic time in financial markets, linking it to physical time.
problem Understanding the intrinsic nature of time in financial data.
method Presented an analytic relationship linking intrinsic and physical time, using empirical scaling laws.
result A novel empirical scaling law relating intrinsic time variability to overshoots.
New findings on GRW space-times with constant scalar curvature.
problem Understanding GRW space-times in different subspaces.
method Analyzing orthogonal subspaces of Gray's decomposition.
result Generalized quasi-Einstein GRW space-times reduce to known types of space-times.
Finite-Time Convergence of Continuous-Time Optimization Algorithms via Differential Inclusionsmath.OC
New continuous-time optimization algorithms converge in finite time to local minima.
problem Finding local minima in optimization problems.
method Discontinuous dynamical systems with finite-time convergence via Lyapunov-based differential inequality.
result Finite-time convergence to strict local minima with provable settling time.
We prove time series data forms a Kolmogorov space with hidden dimensions.
problem Understanding the structure of time series data.
method Defining cyclic coordinates and spinor fields in time series data.
result Time series data has hidden eight dimensions.
Consider power utility maximization of terminal wealth in a 1-dimensional continuous-time exponential Levy model with finite time horizon. We discretize the model by restricting portfolio adjustments to an equidistant discrete time grid. Under minimal assumptions we prove convergence of the optimal discrete-time strate…
New distances defined between space-times, proving some definite.
problem Defining distances between space-times.
method Introducing causal-null-compactifiable space-times and using cosmological time and null distance.
result Various definite distances defined, proving convergence of space-times.
Proposes a method to allocate time budgets in mixed criticality systems.
problem Managing execution time variability in mixed criticality systems.
method Quantifies execution time variability using statistical dispersion parameters and proposes a heuristic to allocate time budgets.
result The proposed heuristic reduces the probability of exceeding allocated budgets.
Paper analyzes venture capital exit decisions under inconsistent preferences.
problem Time-inconsistent preferences in venture capital exit timing.
method Modeling four types of venture capitalists with varying levels of inconsistency.
result Time-inconsistent venture capitalists exit earlier than consistent ones.
TSMB handles time delays in multivariate time series data.
problem Varying time delays in multivariate time series data complicate predictions.
method Time Series Model Bootstrap (TSMB) framework for nonparametric time delay estimation.
result TSMB improves model performance in dynamic data environments.
Modeling regime shifts in co-evolving time series with interactions and time-dependency.
problem Discovering and modeling regime shifts in multiple time series with relationships and time-dependent behaviors.
method Modeling interactions and time-dependency in co-evolving time series using a mapping grid and dynamic network representation for regime identification and time-dependent Cox regression for regime transition probabilities.
result A principled approach for modeling interactions and time-dependency in co-evolving time series.
Logarithmic regret for continuous-time reinforcement learning.
problem Continuous-time Markov decision processes with unknown transition probabilities and holding times.
method Upper confidence reinforcement learning, mean holding time estimation, stochastic comparison of point processes.
result Logarithmic regret bound achieved in finite time.
TTW aligns time-series faster and more accurately than existing methods.
problem Efficiently aligning multiple time-series signals with varying lengths.
method TTW uses a sinc convolutional kernel and gradient-based optimization for linear time and sequence complexity.
result TTW outperforms existing methods in time-series averaging and classification tasks.
CW's time change models for option pricing are flawed.
problem CW's models for time changes in option pricing are not measurable.
method Analysis of the measurability of time changes with respect to the underlying filtration.
result CW's models for time changes fail to satisfy the measurability assumption.
Extreme times in financial marketscond-mat.other
We apply the theory of continuous time random walks to study some aspects of the extreme value problem applied to financial time series. We focus our attention on extreme times, specifically the mean exit time and the mean first-passage time. We set the general equations for these extremes and evaluate the mean exit ti…
EMD reveals dynamic cross-correlations across financial indices at various time-scales.
problem Characterizing time-varying multidimensional cross-correlations in financial indices.
method Empirical Mode Decomposition applied to intraday time series of financial indices.
result Uncovered rich heterogeneity of interactions dependent on time-scale and led-lag relations.
Waiting-time distribution for a stock-market indexphysics.soc-ph
We investigate the waiting-time distribution of the absolute return in the Korean stock-market index KOSPI. We define the waiting time as a time interval during which the normalized absolute return remains continuously below a threshold . Through an exponential bin plot, we observe that the waiting-time distributi…
EDICT learns evidential distributions for irregular time series, improving predictions and uncertainty quantification.
problem Challenges in predicting and characterizing uncertainty for irregular time series data.
method EDICT (Evidential Distributions for Irregular Time Series) learns a continuous-time evidential distribution.
result EDICT achieves competitive performance on time series classification tasks and provides better uncertainty quantification.
GRATIS generates diverse time series for benchmarking.
problem Lack of diverse time series data for evaluation.
method Uses mixture autoregressive (MAR) models to generate time series.
result Generates diverse and controllable time series efficiently.
Infinite rank groups found in 3-manifolds with infinite fundamental groups.
problem Understanding the structure of diffeomorphism and homeomorphism groups of 3-manifolds with infinite fundamental groups.
method Analyzing actions of barbell diffeomorphisms on spaces of embedded arcs and configuration spaces.
result Groups of diffeomorphisms and homeomorphisms have infinite rank.
Proposes a new model to optimize investment plans with varying terminal times.
problem Improving the classical mean-variance model for continuous time investments.
method Uses stochastic optimal control and varying terminal time to determine optimal strategies.
result Optimal strategies and terminal times can be determined to minimize portfolio variance.
Study space-like and time-like surfaces in Robertson-Walker space-times with positive nullity.
problem Characterize space-like and time-like surfaces in Robertson-Walker space-times with positive relative nullity.
method Provide necessary and sufficient conditions, local classification theorems, and analyze special spaces.
result Local classification theorems for space-like and time-like surfaces in with positive relative nullity.
The paper examines properties of -curvature tensor in relativistic space-times.
problem Investigating the properties and implications of the -curvature tensor in relativistic space-times.
method Analyzing the semi-symmetry and divergence properties of the energy-momentum tensor in relation to the -curvature tensor.
result Space-times with specific properties of the -curvature tensor are classified as Einstein or Codazzi type.
OneShotSTL efficiently decomposes time series online, improving speed and accuracy.
problem Real-time analysis of time series data with low processing delay.
method Online seasonal-trend decomposition algorithm with O(1) update time complexity.
result 1,000 times faster than batch methods with comparable accuracy.
Proves compactness for timed-metric spaces using new distance and maps.
problem Weak convergence of space-times using timed-Hausdorff distance.
method Uses Gromov's original compactness theorem and introduces addresses.
result Establishes compactness theorem for intrinsic timed-Hausdorff convergence.
Time Reversal and Last Passage Time of Diffusions with Applications to Credit Risk Managementq-fin.MF
Study on time reversal and last passage time of diffusions for credit risk management.
problem Credit risk management using leverage process and alarming levels.
method Analysis of time reversal, last passage time, and -transform of linear diffusions.
result Developed a new risk management framework for companies.
Develops a kernel for financial time series analysis.
problem Measuring similarity between evolving financial networks.
method Commute time matrix, dynamic time warping, Shannon entropy.
result Proposes a kernel for financial time series analysis.
Quantifying the relationship between student enrollment patterns and student performancephysics.ed-ph
The study uses Hidden Markov Models to analyze student enrollment patterns and academic performance.
problem Limited understanding of how enrollment patterns affect academic performance.
method Applied Hidden Markov Models to categorize enrollment strategies and compare academic outcomes.
result Mixed enrollment strategies lead to better academic performance, especially during part-time semesters.
New deep learning method for real-time regression analysis.
problem Real-time regression analysis for time series data.
method Novel deep learning algorithms for real-time regression analysis.
result Demonstrated real-time regression analysis for time series data.
Compactness theorem for timed-metric spaces established.
problem Compactness of timed-metric spaces and causality.
method Timed-Gromov--Hausdorff distance and intrinsic timed-Hausdorff distance.
result Induces same notion of convergence as intrinsic timed-Hausdorff distance.
The Mathematics of Market Timingq-fin.PM
Market timing yields below median returns, even with perfect hindsight.
problem The likelihood of successful market timing strategies.
method Modeling and analysis of index mutual fund data over 1993-2017.
result The probability distribution of market timing returns is asymmetrical, with a higher probability of below median returns.
Two new data synthesizers generate multidimensional time series for analysis.
problem Evaluate distance functions on high-dimensional time series.
method Proposed two new data synthesizers: CBF and RAM.
result Evaluation of 1-nearest neighbor classifier using DTW on generated datasets.
Generative profiling improves real-time task timing for varied resource contexts.
problem Inaccurate task timing analysis for complex hardware architectures.
method Nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation for synthesizing context-dependent timing profiles.
result Maximum likelihood accurate execution profiles for unseen resource contexts.
This paper introduces intrinsic time, a new measure of time for complex systems.
problem Traditional time measures fail to capture the dynamic nature of real-world phenomena.
method Intrinsic time uses an event-based, algorithmic framework to analyze time series data.
result Intrinsic time reveals novel structures and regularities in financial markets.
The paper examines isotropic cosmological space-times with changing sectional curvature.
problem Cosmological space-times with changing sectional curvature.
method Analysis of a family of geometrically well-behaved cosmological space-times foliated by isotropic hypersurfaces.
result Only space-time isometries ensure the rigidity properties of isotropic cosmological space-times.
Proposes GDTW for aligning time series on different, incomparable spaces.
problem Dynamic time warping requires comparable spaces, but time series can live on different, incomparable spaces.
method Gromov dynamic time warping (GDTW) considers intra-relational geometry to avoid comparability requirements.
result Demonstrates effectiveness of GDTW in aligning, combining, and comparing time series on incomparable spaces.
Geometrizes time-dependent mechanics using second order tangent bundles.
problem Modeling time-dependent Lagrangian mechanics.
method Introducing time-dependent connections, semisprays, and vector bundle structures on second order tangent bundles.
result Established a framework for time-dependent mechanics using second order tangent bundles.
We investigate refocusing and strong refocusing of light rays in a space-time. A strongly refocusing space-time is refocusing. The converse is unknown. We construct examples of space-times which are refocusing, but not strongly so, at a particular point. These space-times are strongly refocusing at other points. The ge…
New bounds for causal effect identification in time series graphs with latent confounders.
problem Identifying causal effects in time series graphs with latent confounders over unbounded time intervals.
method Applying the Causal Identification algorithm to a constant-size segment of the time series graph.
result A bound on the number of past time steps needed for causal effect identification.
TimeCNN improves forecasting by refining cross-variable interactions over time.
problem Multivariate time series forecasting struggles with dynamic and multifaceted cross-variable correlations.
method TimeCNN uses timepoint-independent convolution kernels to capture evolving relationships among variables.
result TimeCNN outperforms state-of-the-art models in real-world datasets with significant computational and speed advantages.
A new method for modeling event sequences with ambiguous timestamps.
problem Handling event sequences with variable timestamps and time-shifts.
method Time-discounting convolution with dynamic pooling.
result Efficiently models event dependencies and robust against timestamp uncertainty.
Catch22 reduces time series feature space to 22 canonical characteristics for efficient analysis.
problem Efficiently capturing and comparing time series properties for diverse applications.
method Inference of minimal sets of time-series features from a comprehensive library.
result Catch22 (22 canonical characteristics) reduces computation time and complexity.
Paper develops a continuous-time framework for financial markets without stochastic calculus.
problem Developing continuous-time financial models without stochastic calculus.
method A general framework using conditional topologies and pseudo-distance topologies.
result No-arbitrage conditions hold in continuous time if and only if they hold in discrete time.