Method infers graph of conditional independence for high-dimensional uncorrelated time series.
problem Inferring conditional independence structure in high-dimensional, uncorrelated time series.
method Testing conditional variances of small subsets of components.
result Successful selection with high probability under certain sample size conditions.
This paper tackles unpaired data in multi-view learning, proposing a new framework and models.
problem Handling unpaired data in multi-view learning, which is more common than paired data.
method Generalized uncorrelated multi-view subspace learning framework with successive alternating approximation (SAA) method.
result Proposed models perform competitively or better than baselines in multi-view feature extraction and multi-modality classification.
Recently, the visibility graph has been introduced as a novel view for analyzing time series, which maps it to a complex network. In this paper, we introduce new algorithm of visibility, "cross-visibility", which reveals the conjugation of two coupled time series. The correspondence between the two time series is mappe…
Deep learning identifies space objects from uncorrelated observations.
problem Finding small groups of observations of the same space objects from a large set of uncorrelated data.
method Training a deep learning model on a large data set of uncorrelated observations to identify groups of observations likely of the same space objects.
result The model correctly identified 83.1% of observation pairs as belonging to the same space object.
We discuss martingales, detrending data, and the efficient market hypothesis for stochastic processes x(t) with arbitrary diffusion coefficients D(x,t). Beginning with x-independent drift coefficients R(t) we show that Martingale stochastic processes generate uncorrelated, generally nonstationary increments. Generally,…
We investigated distributions of short term price trends for high frequency stock market data. A number of trends as a function of their lengths was measured. We found that such a distribution does not fit to results following from an uncorrelated stochastic process. We proposed a simple model with a memory that gives …
We consider the Black--Scholes model of financial market modified to capture the stochastic nature of volatility observed at real financial markets. For volatility driven by the Ornstein--Uhlenbeck process, we establish the existence of equivalent martingale measure in the market model. The option is priced with respec…
Study tests adequacy of FARIMA models with uncorrelated but non-independent errors.
problem Testing adequacy of FARIMA models with specific error characteristics.
method Derive asymptotic distributions of residual autocovariances and autocorrelations, propose self-normalization approach.
result Asymptotic distributions of modified portmanteau statistics for weak FARIMA models.
Study on Gaussian models reveals moment explosions under certain volatility conditions.
problem Understanding the behavior of asset price processes in Gaussian stochastic volatility models.
method Established large and moderate deviation principles, analyzed exit probabilities, and proved moment explosion results.
result If volatility grows faster than linearly, all moments of order greater than one are infinite for asset price processes.
Truncated Lévy flights are random walks in which the arbitrarily large steps of a Lévy flight are eliminated. Since this makes the variance finite, the central limit theorem applies, and as time increases the probability distribution of the increments becomes Gaussian. Here, truncated Lévy flights with correlated fluct…
Study uses Malliavin calculus to find exact option pricing under stochastic volatility.
problem Exact pricing of European options in markets with stochastic volatility.
method Applying Malliavin calculus to models defined by Ornstein-Uhlenbeck or Cox-Ingersoll-Ross processes.
result Established the density function of the volatility average over time to maturity.
We present a simple and general result that the sign of the variations or increments of uncorrelated times series are predictable with a remarkably high success probability of 75% for symmetric sign distributions. The origin of this paradoxical result is explained in details. We also present some tests on synthetic, fi…
Study finds sample size needed for non-stationary model selection.
problem Accurately selecting graphical models from non-stationary data.
method Analyzed a specific model selection method for non-stationary Gaussian processes.
result Derived a sufficient condition for sample size based on non-stationary data.
New model selects uncorrelated and discriminative features for unsupervised feature selection.
problem Selecting uncorrelated and discriminative features in high-dimensional data.
method Adaptive graph-based generalized regression model with uncorrelated constraint and ℓ2,1-norm regularization. result The model effectively selects uncorrelated and discriminative features, improving clustering performance.
Improved option pricing for SABR model using Gauss-Hermite quadrature.
problem Improving accuracy of option pricing in the SABR model.
method Using Gauss-Hermite quadrature for numerical integration of the integrated variance.
result New method provides accurate option prices across all strike prices.
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features a…
Polynomial-time method solves complex combinatorial semi-bandits.
problem Optimal strategies for combinatorial semi-bandits with uncorrelated Gaussian rewards.
method Proposes a polynomial-time method to solve the Graves-Lai optimization problem for various combinatorial structures.
result First known approach to implement asymptotically optimal algorithms in polynomial time for combinatorial semi-bandits.
Gen-RKM framework generates multi-view data with uncorrelated features.
problem Joint multi-view generation and disentangled feature learning.
method Joint multi-view generation using shared representation; primal and dual formulations; novel training procedure for neural networks.
result Joint multi-view generation and uncorrelated feature learning achieved.
We study the mass at the origin in the uncorrelated SABR stochastic volatility model, and derive several tractable expressions, in particular when time becomes small or large. As an application--in fact the original motivation for this paper--we derive small-strike expansions for the implied volatility when the maturit…
Alternative method proposed to avoid Procrustes in regularized MVA.
problem Optimizing regularized MVA solutions while preserving feature uncorrelation.
method Solves an eigenvalue problem instead of the Procrustes approach.
result Ensures preservation of feature uncorrelation in regularized MVA.
We provide analytical pricing formula of corporate defaultable bond with both expected and unexpected default in the case with stochastic default intensity. In the case with constant short rate and exogenous default recovery using PDE method, we gave some pricing formula of the defaultable bond under the conditions tha…
We improve DGP models by using importance-weighted variational inference for better accuracy.
problem Accurate modeling of non-Gaussian marginals in deep Gaussian processes.
method Introduced noisy latent covariates and an importance-weighted objective for variational inference.
result The importance-weighted objective consistently outperforms classical variational inference, especially for deeper models.
The covariance matrix is formulated in the framework of a linear multivariate ARCH process with long memory, where the natural cross product structure of the covariance is generalized by adding two linear terms with their respective parameter. The residuals of the linear ARCH process are computed using historical data …
Efficiently selects predictors in sparse regression without approximations.
problem High computational cost in subset selection for sparse regression.
method Conditional uncorrelation formula and efficient non-approximate method.
result Significant reduction in computational complexity for subset selection.
A method for pricing two-asset options using a finite element approach for Levy processes.
problem Pricing two-asset options with Levy process under exponential model.
method Finite element method (FEM) for a partial integro-differential equation (PIDE).
result Good performance of the proposed method for pricing two-asset options.
The paper extends SARMA models by relaxing independence assumptions on error terms.
problem Testing adequacy of SARMA models with non-independent errors.
method Study of asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations under weak noise assumptions.
result Established asymptotic behavior of portmanteau tests for SARMA models.
Arguably the most important problem in quantitative finance is to understand the nature of stochastic processes that underlie market dynamics. One aspect of the solution to this problem involves determining characteristics of the distribution of fluctuations in returns. Empirical studies conducted over the last decade …
We introduce a generalisation of the well-known ARCH process, widely used for generating uncorrelated stochastic time series with long-term non-Gaussian distributions and long-lasting correlations in the (instantaneous) standard deviation exhibiting a clustering profile. Specifically, inspired by the fact that in a var…
Agnostic Risk Parity improves diversification by minimizing unknown-unknown risks.
problem Markowitz' optimal portfolio theory fails to deliver diversification out-of-sample.
method Symmetry arguments lead to Eigenrisk Parity portfolios, then specialize to AGP minimizing unknown-unknown risks.
result AGP portfolios minimize unknown-unknown risks and perform well with technical strategies.
Two new models for volatility in Markov-switching environments capture financial time-series properties.
problem Modeling volatility in environments with regime switches and exogenous jumps.
method Generalizations of COGARCH and Barndorff-Nielsen-Shephard models using Markov-modulated generalized Ornstein-Uhlenbeck processes.
result Models inherit properties of original models and capture stylized facts of financial time-series.
Based on the Multifractal Detrended Fluctuation Analysis (MFDFA) and on the Wavelet Transform Modulus Maxima (WTMM) methods we investigate the origin of multifractality in the time series. Series fluctuating according to a qGaussian distribution, both uncorrelated and correlated in time, are used. For the uncorrelated …
Modeling social network activity through user and topic interaction.
problem Understanding complex dynamics of opinion formation in social networks.
method Mixture of Interacting Cascades (MIC) model of marked multidimensional Hawkes processes.
result MIC achieves superior performance in modeling information cascade spread.
Graphical lasso models ASR utterance dependencies for consistent WER estimation.
problem Modeling dependent structure among ASR utterances for accurate significance analysis.
method Graphical lasso for dependency modeling, followed by blockwise bootstrap resampling.
result Statistically consistent variance estimator of WER under mild conditions.
We consider uncorrelated Stein-Stein, Heston, and Hull-White models and their perturbations by compound Poisson processes with jump amplitudes distributed according to a double exponential law. Similar perturbations of the Black-Scholes model were studied by S. Kou. For perturbed stochastic volatility models, we obtain…
Ensemble of models with uncorrelated loss functions improves adversarial robustness.
problem Adversarial attacks on deep neural networks.
method Diversity Training: training an ensemble of models with uncorrelated loss functions.
result Our method significantly improves adversarial robustness of ensembles.
The correlation matrix formalism is used to study temporal aspects of the stock market evolution. This formalism allows to decompose the financial dynamics into noise as well as into some coherent repeatable intraday structures. The present study is based on the high-frequency Deutsche Aktienindex (DAX) data over the t…
Complex-valued signals are used in the modeling of many systems in engineering and science, hence being of fundamental interest. Often, random complex-valued signals are considered to be proper. A proper complex random variable or process is uncorrelated with its complex conjugate. This assumption is a good model of th…
Anti-correlated noise improves machine learning model generalization.
problem Improving machine learning model generalization.
method Injecting anticorrelated noise into gradient descent steps.
result Anti-correlated noise leads to better model generalization than uncorrelated noise.
New spatiotemporal Besov process improves CT image reconstruction and other inverse problems.
problem Handling abrupt changes and sharp contrasts in spatiotemporal data.
method Generalized Besov process (STBP) with Q-exponential process for temporal correlation.
result STBP outperforms traditional methods in dynamic reconstruction and inverse problems.
Paper solves multi-dimensional passport option pricing problem using machine learning.
problem Pricing multi-dimensional passport options in correlated markets remains unsolved.
method Discrete-time solution for multi-dimensional BS markets with uncorrelated assets; machine learning approaches.
result Machine learning-powered approaches successfully price passport options in both 1D and multi-dimensional uncorrelated BS markets.
We develop an efficient method to calibrate CDS spreads using asymptotic approximations.
problem Calibrating CDS spreads in the SSRD model with correlated processes.
method Asymptotic coefficient expansion to approximate solutions of nonlinear PDEs.
result Our approximation does not require uncorrelated interest rate and default intensity processes.
Proposes mean-correction for SVMs with correlated errors to model stock market returns.
problem Unrealistic assumption of uncorrelated errors in stochastic volatility models.
method Introduces mean-correction and calculates higher moments of log-return.
result Closed-form expressions for higher moments and lead-lag correlations.
The paper maps time-series onto networks to reveal hidden joint information.
problem Extract hidden joint information from uncorrelated time-series.
method Discretize time-series amplitudes, map onto networks, measure coupling deviations, and compare with Gaussian distributions.
result Markets may possess joint patterns even if initially uncorrelated.
Paper presents a new framework for optimal asset and signal combination.
problem Optimal asset and signal combination problem.
method Two-stage approach: reformulate dynamic portfolio selection problem, then use Canonical Correlation Analysis.
result Improved performance of proposed method over natural benchmarks.
This paper uses deep reinforcement learning to generate profitable trading signals in financial markets.
problem Generating consistent profitable trading signals in any financial market.
method Developed a novel Markov decision process (MDP) model to capture financial market dynamics and used deep reinforcement learning to learn profitable trading strategies.
result The model generates positively robust performance in two different financial markets.
The paper develops a Gaussian process model for predicting chemical efficacy.
problem Statistical methodologies for analyzing chemical databases are limited.
method Conditional Gaussian process models with Tanimoto distance and a scaling parameter.
result Predictive performance improves when accounting for chemical space correlation.
New algorithm reduces combinatorial semi-bandit regret efficiently.
problem Optimizing rewards from uncorrelated items in combinatorial semi-bandits.
method Developed an approximate version of ESCB with polynomial complexity.
result Achieved statistically efficient and polynomial time algorithm for combinatorial semi-bandits.
ARCH and GARCH models assume either i.i.d. or (what economists lable as) white noise as is usual in regression analysis while assuming memory in a conditional mean square fluctuation with stationary increments. We will show that ARCH/GARCH is inconsistent with uncorrelated increments, violating the i.i.d. and white ass…