Abstract: From complex financial models to simpler Hamilton-Jacobi equations.
problem Complex financial models for multi-dimensional Black-Scholes.
method Linked Hamilton-Jacobi equations to simplify financial models.
result Simplified financial models using Hamilton-Jacobi equations.
Modeling financial systemic risk with optimal control theory for stability.
problem Analyzing and stabilizing systemic risk in interconnected financial entities.
method Developed a theoretical model using optimal control theory, including steps for synthesizing stabilizing controllers.
result The model ensures that the H∞ norms of the mappings from disturbance to output are less than a predefined constant, stabilizing the system. The paper studies complex equations for financial optimal control problems.
problem Optimal control problems with unspecified dynamics in financial modeling.
method First Order BSPDEs in higher dimensions.
result Constructs value functions for optimal control problems.
PDGM uses neural nets to solve complex financial equations.
problem Solving path-dependent partial differential equations (PPDEs)
method Generalized Deep Galerkin Method (PDGM) combining feed-forward and LSTM architectures
result PDGM successfully models solutions to various PPDEs, including financial derivatives.
A new method for solving complex financial equations.
problem Solving complex financial equations with nested conditional expectations.
method Pathwise iteration for backward SDEs.
result Computes and iteratively improves upper and lower bounds on the true solution.
New quantum algorithm simplifies complex financial derivatives pricing.
problem Complex financial derivatives pricing with high dimensionality.
method Quantum-inspired variational algorithms combined with neural-network quantum states.
result Simplified pricing of European options with many correlated assets.
Kinetic theory explains financial Brownian motion from trader dynamics.
problem Understanding financial Brownian motion from high-frequency trading dynamics.
method Deriving time-evolution equations, Bogoliubov-Born-Green-Kirkwood-Yvon hierarchies, Boltzmann-like and Langevin-like equations.
result Mathematical foundation for financial Brownian motion parallels physical Brownian motion.
The paper solves optimal control problems for stochastic delay equations.
problem Optimal control of stochastic delay differential equations.
method Rewriting the problem in an infinite-dimensional Hilbert space, using dynamic programming and viscosity solutions.
result Characterizes the value function as the unique viscosity solution of the Hamilton-Jacobi-Bellman equation.
Neural networks model financial data with Lévy processes.
problem Forecasting chaotic financial time series with big jumps.
method Lévy-induced stochastic differential equation network approximated by neural networks.
result The method improves prediction accuracy using non-Gaussian Lévy processes.
The goal of this article is to describe the concepts of system dynamics and its applications to the simulation modeling of financial institutions daily activity. The hybrid method of the re-engineering of banking business processes based upon combination of system dynamics, queuing theory and tools of ordinary differen…
We develop an algebraic framework for the description and analysis of financial behaviours, that is, behaviours that consist of transferring certain amounts of money at planned times. To a large extent, analysis of financial products amounts to analysis of such behaviours. We formalize the cumulative interest compliant…
Quantum model investigates financial derivative price dynamics with quantum interference effects.
problem Investigate quantum drift in financial derivatives using Heisenberg Equation of Motion.
method Apply geometric techniques to integrate Heisenberg Equation of Motion, model financial market as quantum observable.
result Quantum interference effects can act as drag or boost on financial returns.
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.
Study optimal investment and consumption in financial markets using Ornstein-Uhlenbeck process.
problem Optimal consumption/investment problem in financial markets with logarithmic utility.
method Stochastic dynamical programming method and Hamilton-Jacobi-Bellman (HJB) equation.
result Explicit solution to the HJB equation and optimal financial strategies constructed.
Survey and new results link hydrodynamics, molecular physics, and financial engineering.
problem Understanding financial engineering topics like Asian options and volatility swaps.
method Linking Kevin waves, Klein-Kramers, and Kolmogorov equations to financial models.
result Corrected the original solution of the Kolmogorov equation.
Modeling high-frequency traders' behavior in financial markets using microscopic dynamics.
problem Capturing the collective motion of high-frequency traders in financial markets.
method Developed a microscopic model based on direct observation of HFTs' trajectories and derived Boltzmann-like and Langevin-like equations.
result First microscopic model validated through data analysis, exhibiting quantitative agreements with empirical results.
Mathematical models with time dependent parameters are of great interest in financial Mathematics because they capture real life scenarios in the financial market. In this study, via the Lie group technique, we analyse evolution-type equations with time dependent parameters and give the general symmetry structure of th…
Model non-stationary financial data using log-normal distributions and Langevin equations.
problem Modeling non-stationary volume-price distributions in finance.
method Model non-stationary volume-price distributions with a log-normal distribution. Derive Langevin equations from the series of log-normal parameters.
result Reconstructed statistics of volume-price distributions fit well empirical data.
The paper solves a complex financial optimization problem using a novel mathematical technique.
problem Optimizing portfolio selection in financial markets.
method Maximal monotone operator method and Riccati transformation.
result Existence and uniqueness of a solution to the transformed parabolic equation in a Sobolev space.
Smooth solutions found for complex financial control problems.
problem Discounted reward control with unbounded discount rate.
method General assumptions and verification reasoning for HJB equation.
result Smooth classical solutions to HJB equation exist.
Develops ML method for solving financial equations.
problem Solving financial equations efficiently and accurately.
method Combines semi-analytical and numerical techniques.
result Significantly faster and more accurate solutions.
Measures financial resilience using BSDEs and their properties.
problem Measuring financial resilience in dynamic risk environments.
method Developed stochastic calculus for BSDEs with jumps, revealing resilience rate as expectation of generator.
result Resilience rate can be represented as expectation of BSDE generator, revealing properties of dynamic risk measures.
Survey on nonlinear parabolic equations in finance.
problem Nonlinear extensions of the Black-Scholes theory.
method Qualitative and numerical analysis of nonlinear parabolic equations.
result Existence and uniqueness of solutions to nonlinear parabolic equations.
The study finds solutions to a financial equation related to volatility.
problem Finding solutions to a financial equation related to volatility.
method Using a zero-curvature condition and soliton theory, the study derives a variant of the Harry Dym equation and finds its travelling wave solutions.
result A family of travelling wave solutions to a variant of the Harry Dym equation is found.
The paper examines financial trading models and proves conditions for positive solutions.
problem Analyzing conditions for positive solutions in a financial trading model.
method Introduced thresholds α− and α+ to prove state positivity or bankruptcy. result For α<α−, state positivity is guaranteed for all time; for α>α+, state positivity is not guaranteed. We shall study backward stochastic differential equations and we will present a new approach for the existence of the solution. This type of equation appears very often in the valuation of financial derivatives in complete markets. Therefore, the identification of the solution as the unique element in a certain Banach …
Model uses Navier-Stokes equations to assess liquidity and systemic risk.
problem Traditional models fail to capture real market fluctuations and extreme events.
method Develops and validates a mathematical model based on Navier-Stokes equations, incorporating 13 macroeconomic and financial parameters.
result Model effectively describes liquidity dynamics, systemic risk, and extreme scenarios.
Illustrates a new self-financing equation's impact.
problem Traditional financial models' limitations.
method Introduces and analyzes a new self-financing condition.
result Enhanced hedging and market maker behavior.
Model predicts stock price volatility using stochastic differential equations.
problem Predicting stock price volatility in financial markets.
method Continuous cascade model using stochastic differential equations with two independent Brownian motions.
result The model accurately reproduces empirical volatility and multifractality.
Sliced Inverse Regression reduces parameter space for estimating complex financial models.
problem High-dimensional parameter space in stochastic differential equations.
method Sliced Inverse Regression for dimension reduction.
result Reduced computational costs in estimating parameters.
We derive a mesoscopic description of the behavior of a simple financial market where the agents can create their own portfolio between two investment alternatives: a stock and a bond. The model is derived starting from the Levy-Levy-Solomon microscopic model (Econ. Lett., 45, (1994), 103--111) using the methods of kin…
The paper connects financial vacuum conditions to spontaneous symmetry breaking in quantum finance.
problem Understanding the conditions under which the martingale condition is a non-degenerate vacuum.
method Expressing financial equations in Hamiltonian form and analyzing symmetry breaking.
result Conditions for the martingale condition to be a non-degenerate vacuum are identified.
This study connects financial volatility to quantum mechanics on hyperbolic manifolds.
problem Deriving a geometric interpretation of financial volatility.
method Mapping financial pricing to quantum Hamiltonians via transformations.
result Financial volatility is a diffusion process on a hyperbolic manifold.
New method identifies extreme risk propagation in financial networks.
problem Understanding extreme risk in financial networks.
method Max-linear structural equation model, hard-thresholding, Hamming distance.
result Sparse DAG for extreme risk propagation estimated.
Model financial network dynamics to avoid systemic risk.
problem Avoid systemic risk in financial networks.
method Model financial network as random liability graph, agents adapt strategies based on learning, analyze using ODE.
result Emerging strategies converge to evolutionary stable strategies (all risky or all less risky agents).
Develops a new method for financial term structure modeling.
problem Analyzing financial term structures with discontinuities.
method Cylindrical stochastic integration approach.
result Establishes a Heath-Jarrow-Morton framework.
This paper studies the transition from disequilibrium to equilibrium in financial markets.
problem Modeling financial markets as disequilibrium models and analyzing their transition to equilibrium.
method Mathematical analysis using asymptotic limits and Tikhonov-Fenichel reduction.
result Stability of the reduced equilibrium model and economic implications are discussed.
ANNs solve financial option valuation problems without numerical methods.
problem Valuation of European and American financial options.
method Unsupervised learning with artificial neural networks (ANNs) for solving PDEs.
result ANNs accurately compute option values for various stock scenarios.
The financial market is nonpredictable, as according to the Bachelier, the mathematical expectation of the speculator is zero. Nevertheless, we observe in the price fluctuations the two distinct scales, short and long time. Behaviour of a market in long terms, such as year intervals, is different from that in short ter…
Bayesian neural SDEs calibrate financial models robustly.
problem Calibrating financial models using neural SDEs for robustness.
method Bayesian framework with prior and likelihood, global approximation theorem, Langevin algorithm.
result Robust bounds on implied volatility surface learned from historical and option data.
Volatility clustering, long-range dependence, and non-Gaussian scaling are stylized facts of financial assets dynamics. They are ignored in the Black & Scholes framework, but have a relevant impact on the pricing of options written on financial assets. Using a recent model for market dynamics which adequately captures …
Applications of Quantum Tunneling effect have long gone beyond the traditional physical meaning. Initially created by Gamow to explain α-decay of nuclear particles, along the time, quantum tunneling found fertile domain of research in chemistry and recently in biology, where the new discipline of Quantum Biology emerge…
Neural networks improve financial derivative pricing accuracy.
problem Improving accuracy in financial derivative pricing.
method Use neural networks to model drift and volatility in SDE models, optimize using SGD for European options and PDE for American options.
result Neural network models outperform traditional models in pricing derivatives.
Deep Bellman Hedging uses reinforcement learning to optimize financial portfolio hedging.
problem Optimizing financial portfolio hedging with derivatives and trading frictions.
method Actor-critic reinforcement learning algorithm with continuous state and action spaces.
result Trained model provides optimal hedge for any initial portfolio and market state.
Develops trinomial models using cubature methods for financial derivative pricing.
problem Pricing financial derivatives in complex stochastic market models.
method Cubature methods applied to Wiener space for constructing trinomial models.
result Numerical solutions compare favorably with Black-Scholes model.
A method for risk valuation using backward stochastic differential equations.
problem Risk evaluation in financial markets.
method Dual representation and stochastic control problem conversion, followed by dynamic programming.
result Piecewise-constant dual control provides a good approximation for risk valuation.
Novel RKHS approach solves complex financial model equations.
problem Calibrating singular local stochastic volatility models.
method Reproducing Kernel Hilbert Space (RKHS) regularization.
result Regularized model is well-posed and replicates option prices.
Derives financial models for markets with multidimensional Hermite motions.
problem Modeling financial markets with multidimensional Hermite motions.
method Derives conditions for no-arbitrage and market completeness, prices perpetual derivatives and forwards.
result Derives partial and partial-differential equations for pricing.