Improved NMF using variance-reduced MU rule.
problem Slow convergence of multiplicative update in NMF.
method Introduces variance-reduced stochastic multiplicative update.
result Robustly outperforms state-of-the-art algorithms.
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arXiv research
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.
169,341 papers · 148 categories
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48 results for “Stochastic factors”
Optimal reinsurance strategy for risk models influenced by stochastic factors.
problem Optimal excess-of-loss reinsurance under stochastic risk factors.
method Stochastic control theory and Hamilton-Jacobi-Bellman equation.
result Optimal reinsurance strategy maximizing expected utility of terminal wealth.
Paper restricts non-negative matrix factorization to stochastic matrices for data analysis.
problem Analyzing unstructured data like topic models and face storage retrieval.
method Necessary and sufficient conditions for unique factorization, natural bounds on parameters, consistent estimator.
result Unique factorization conditions and parameter bounds for observed data.
Authors improve accuracy analysis for portfolio optimization with multiple timescale factors.
problem Asymptotic accuracy of portfolio optimization approximations for general utility functions and two timescale factors.
method Construct sub- and super-solutions to fully nonlinear problem.
result Rigorous justification of accuracy for portfolio optimization with general utility functions and two timescale factors.
We find multi-factor CIR models can exhibit unspanned stochastic volatility.
problem Unspanned stochastic volatility in fixed income markets.
method Formal review and necessary/sufficient conditions for multi-factor CIR models.
result We construct three-factor CIR models that exhibit unspanned stochastic volatility.
Improved growth strategies by incorporating stochastic factors in asset returns.
problem Drift uncertainty in asset returns makes growth optimization strategies sensitive.
method Study robust growth-optimization in high-dimensional incomplete markets under drift uncertainty and ergodicity.
result Utilizing stochastic factors improves robust growth rates and optimal strategies.
This paper tackles robust growth maximization with stochastic factors, finding optimal strategies independent of the factor process.
problem Maximizing asymptotic growth under model uncertainty with stochastic factor processes.
method Combines techniques from partial differential equations, calculus of variations, and generalized Dirichlet forms.
result Optimal trading strategy is functionally generated and independent of the stochastic factor process.
Investigates optimal reinsurance and investment strategies for insurance companies with stochastic factor effects.
problem Maximizing expected exponential utility of terminal wealth in a stochastic factor model.
method Classical stochastic control approach based on Hamilton-Jacobi-Bellman equation, solving two backward PDEs.
result Characterization of optimal reinsurance-investment strategy and verification of value function.
The paper solves investment problems with uncertain factors using game theory.
problem Optimal forward investment in an incomplete market with model uncertainty.
method Combining stochastic differential games and ergodic BSDE approach.
result Representation of robust forward performance processes in factor form.
Develops polynomial diffusion models for multi-factor commodity futures dynamics.
problem Modeling futures prices using latent state variables for short and long-term stochastic factors.
method Polynomial diffusion models to incorporate non-linear effects, two filtering methods for estimation.
result Accurate estimation of futures prices despite parameter identification issues in polynomial diffusion models.
Study forward investment performance in semimartingale markets with stochastic factors.
problem Investigate forward investment performance in incomplete semimartingale markets with power risk preferences and stochastic integrated factors.
method Develop necessary and sufficient conditions for FIPP existence, use integral representations, and solve ill-posed HJB equations.
result Explicit constructions for time-monotone FIPPs in semimartingale models, generalizing from Brownian to semimartingale markets.
Study optimal investment and consumption in a stochastic factor model.
problem Optimal investment and consumption decisions in a stochastic factor model.
method Characterization of well-posedness, numerical algorithm, and general theory of sub- and supersolutions for HJB equation.
result Proves existence and provides bounds for the solution to the HJB equation.
Quadratic approximation of slow factor of volatility in a Multi-factor Stochastic volatility Modelq-fin.PR
New model approximates slow volatility factor using parabolic arcs.
problem Modeling slow factor of volatility in stochastic volatility models.
method Perturbation technique to derive approximate European option prices.
result Simplified expression for European option prices around modified Black-Scholes price.
The Co-Pricing Factor Zooq-fin.PR
A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.
problem The redundancy of extensive bond factor literature in explaining corporate bond risk premia.
method Bayesian Model Averaging Stochastic Discount Factor analysis of 18 quadrillion models.
result A Bayesian Model Averaging SDF explains risk premia better than low-dimensional models, with an out-of-sample Sharpe ratio of 1.5 to 1.8.
Variational inference improves neural network matrix factorization for stochastic blockmodels.
problem Improving predictive performance of neural network matrix factorization for stochastic blockmodels.
method Construct Bayesian neural networks and fit with variational inference.
result Variational inference can achieve equivalent performance to neural networks on Movielens data.
Paper solves portfolio problem using improved stochastic methods.
problem Finite horizon consumption-investment problem under stochastic factor framework.
method Proves existence of classical solution for semilinear equation using gradient estimates.
result Proves existence of classical solution and provides all necessary estimates.
Method detects communities in networks using matrix factorization.
problem Community detection in complex networks.
method Orthogonal symmetric non-negative matrix tri-factorization of the normalized Laplacian matrix.
result Consistent for community detection in graphs from stochastic block models.
Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCAstat.ML
This work connects LLE, factor analysis, and probabilistic PCA through a stochastic perspective.
problem Exploring the theoretical connection between LLE, factor analysis, and probabilistic PCA.
method Solving the stochastic linear reconstruction of LLE using expectation maximization.
result LLE, factor analysis, and probabilistic PCA are shown to be connected through a stochastic perspective.
Optimizes portfolios with constraints and stochastic factors, deriving explicit solutions.
problem Optimizing expected utility in an incomplete market with stochastic factors and convex constraints.
method Fundamental duality results and HJB PDE, derived condition for exponential affine solutions.
result Explicit expressions for optimal allocations and Riccati ODE solutions in specific markets.
The paper studies the continuous-time dynamics of VIX with stochastic volatility and jumps in VIX and volatility. Built on the general parametric affine model with stochastic volatility and jump in logarithm of VIX, we derive a linear relation between the stochastic volatility factor and VVIX index. We detect the exist…
The paper represents performance processes in incomplete markets using BSDE.
problem Incomplete markets with stochastic factors.
method Ergodic and infinite horizon BSDEs for homothetic forward performance processes.
result Derivation of representations for power, exponential, and logarithmic forward performance processes.
Study asset price bubbles using random matching and stochastic factors.
problem Understanding and modeling asset price bubbles through investor contagion.
method Developed a stochastic model of liquidity-based asset price bubbles using random matching mechanism.
result Derived conditions for arbitrage-free financial market models.
The paper develops optimal strategies for high-dimensional statistical arbitrage using factor models and stochastic control.
problem Optimal strategies for high-dimensional statistical arbitrage in a factor model setting.
method Combines factor models with stochastic control to derive optimal strategies.
result Closed-form optimal strategies for market-neutral portfolios in a high-dimensional setting.
This paper investigates factors influencing SGD minima.
problem Understanding the factors that influence the minima found by SGD.
method Examined learning rate, batch size, Hessian, and gradient covariance; used stochastic differential equations to model SGD.
result The ratio of batch size to learning rate is a main factor in SGD dynamics.
Develops optimal portfolio selection for forward performance in stochastic factor models.
problem Optimal portfolio selection under forward investment performance criteria in an incomplete market.
method Constructs forward performance processes and optimal portfolios by solving non-linear PDEs for stock-factor correlation matrices with EVE structure.
result Establishes explicit forms of generalized Widder's theorem for Laplace inversion in time of solutions to linear PDEs.
Analyzes robust portfolio optimization with multi-factor stochastic volatility.
problem Optimizing portfolios under uncertainty and volatility risks.
method Analytical derivation of optimal strategy under worst-case scenarios, comparison with strategies ignoring uncertainty, and numerical experiments.
result Effects of ambiguity and derivative trading on optimal portfolio selection.
The paper calculates how fast optimal investment strategies approach CRRA strategies in stochastic factor models.
problem Understanding convergence rates of optimal investment strategies in stochastic factor models.
method Analyzes optimal feedback functions in nonlinear and quadratic term structure models, considering decay of bond prices and power-like utility at high wealth levels.
result Convergence rates of optimal investment strategies to CRRA strategies are determined by bond price decay and power-like utility behavior.
Paper shows LDA and SMF have similar generalization errors.
problem LDA and SMF's generalization performance is unknown.
method Algebraic and geometric method to show equivalence of LDA and SMF.
result LDA and SMF have asymptotically same Bayesian generalization error.
A new multi-factor model improves commodity pricing accuracy.
problem Enhancing accuracy in commodity pricing by integrating multiple risk factors.
method A four-factor model using Kalman filter for simultaneous estimation and state variable filtering.
result The four-factor model outperforms existing models in capturing futures term structures and crude oil pricing.
Calibrates hybrid LSV models with stochastic rates using particle method and control variates.
problem Calibrating complex foreign exchange models with stochastic volatility and stochastic rates.
method Combines particle method with variance reduction techniques and control variates.
result Accelerates convergence in calibration process for a wide class of hybrid LSV models.
The paper introduces a stochastic deflator for financial derivatives pricing.
problem Pricing financial derivatives under economic and financial risk factors.
method Implement a stochastic deflator with five factors: interest rates, market risk, stock prices, default intensities, and convenience yields.
result The deflator approach is reliable for pricing financial derivatives.
Estimating the Stochastic Discount Factor from Option Prices and Predicting the Equity Premiumq-fin.MF
The paper proposes a new SDF scaled by time-varying volatility from S&P 500 options.
problem Estimating the SDF from option prices and predicting the equity premium.
method Utilizes S&P 500 options data to recover a stable, non-monotonic SDF.
result The SDF exhibits a hump on the put side, which transitions into a W-shape with maturity.
Study market-to-book ratios using Stochastic Portfolio Theory.
problem Identify the value factor in stock returns.
method Develop functionally generated portfolios using book values and analyze their relative returns.
result The value factor (market-to-book ratio) affects portfolio performance.
Novel method for estimating currency option parameters with improved accuracy.
problem Improving currency option pricing accuracy and calibration process.
method Develops approximate formulas for two parameters in stochastic volatility models with exponentially-affine characteristic functions.
result Superior accuracy in parameter estimation for currency options.
This paper compares two extensions of the Heston model for option pricing.
problem Improving the accuracy of option pricing models.
method Empirical analysis and non-linear least square optimization of parameters.
result The multiscale stochastic volatility model outperforms the Heston model.
An ADRC-incorporated SGD algorithm improves latent factor analysis speed and accuracy.
problem Slow convergence in standard SGD for HDI matrix analysis.
method Incorporates ADRC principles to refine historical and future learning error states.
result Empirically outperforms state-of-the-art LFA models in HDI matrix prediction.
Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian matrix factorizat…
New method solves SLV models faster using Lie algebra.
problem Local stochastic volatility models.
method Wei-Norman factorization method and Lie algebraic techniques.
result Reduces time-dependent SLV models to autonomous PDEs.
Optimal portfolio under ratio-type periodic evaluation in incomplete markets with stochastic factorsq-fin.PM
This paper optimizes portfolio management in incomplete markets with stochastic factors, considering periodic wealth evaluations.
problem Optimizing portfolio performance in an incomplete market model with stochastic factors and periodic wealth evaluations.
method Developed a martingale duality approach to find optimal portfolio processes and dual minimizers.
result Established the existence of optimal portfolio processes and identified dual minimizers as the 'least favorable' market completion.
The purpose of this paper is to study the generalized Fong--Vasicek two-factor interest rate model with stochastic volatility. In this model the dispersion of the stochastic short rate (square of volatility) is assumed to be stochastic as well and it follows a non-negative process with volatility proportional to the sq…
Unified framework speeds up SMF algorithms via variance reduction.
problem Improving convergence speed and accuracy in stochastic matrix factorization.
method Unified framework using variance reduction for SMF.
result Consistently faster convergence and more accurate output.
Large and Deep Factor Modelsq-fin.ST
Deep neural networks decompose SDF into linear and nonlinear components.
problem Constructing accurate stochastic discount factors (SDFs) for pricing.
method Additive decomposition of a deep neural network trained to construct SDFs.
result The PTK representation delivers significant performance gains in equity data.
Dynamic model predicts user preferences over time.
problem Static user preferences in collaborative filtering.
method Compound Poisson Factorization with Gamma-Markov chains.
result DCPF achieves higher predictive accuracy than static models.
New algorithm improves stochastic volatility model quantization.
problem Efficiency in quantizing two-factor stochastic volatility models.
method Proposes a new algorithm that uses gradient-descent techniques to margin over volatility process.
result Significant decrease in computational effort compared to current methods.
We develop a scalable model for large asset returns without volatility matrix restrictions.
problem Inference for large multivariate stochastic volatility models.
method Factor multivariate stochastic volatility model with scalable MCMC algorithm.
result Scalable inference achieved for 571 stock returns over 10 years.
The Long Bond, Long Forward Measure and Long-Term Factorization in Heath-Jarrow-Morton Modelsq-fin.MF
Proves existence of long bond, long forward measure, and long-term factorization in HJM models.
problem Existence of long bond, long forward measure, and long-term factorization in HJM models.
method Function space framework of Filipovic (2001) and sufficient condition on the weight in the Hilbert space of forward rate volatility curves.
result Existence of long bond volatility process, long bond process, and long-term factorization of SDF.
Dynamic Factor Model-Based Multiperiod Mean-Variance Portfolio Selection with Portfolio Constraintsq-fin.PM
The paper solves multi-period portfolio selection with constraints using a dynamic factor model.
problem Multi-period mean-variance portfolio selection with constraints.
method Dynamic factor model, dynamic programming, piecewise linear feedback policy.
result Optimal portfolio policies determined by two stochastic processes.
Deep weight factorization improves neural network training through smooth optimization of sparse penalties.
problem Challenges in applying sparse regularization in neural networks due to non-differentiability of penalties.
method Introduces deep weight factorization, decomposing weights into multiple factors for smooth optimization of -penalized networks.
result Deep weight factorization outperforms shallow factorization and pruning methods consistently across various architectures and datasets.