The paper predicts cryptocurrency prices using a path-dependent Monte Carlo simulation.
problem Forecasting cryptocurrency prices with volatility and jumps.
method Merton's jump diffusion model with machine learning, traditional, and statistical methods.
result Introduced a path-dependent Monte Carlo simulation for cryptocurrency price prediction.
Geometric Brownian motion simulates stock prices for Brazilian small caps index.
problem Simulating stock prices for the Brazilian small caps index.
method Used geometric Brownian motion to simulate stock prices of Brazilian small caps index using historical data.
result Simulated prices better for portfolios with higher returns, lower risks, and higher Sharpe Indexes.
Generative model simulates financial market price variations from order flow.
problem Simulating intra-day price variations driven by order flow.
method Sequence Generative Adversarial Networks framework applied to model order flow.
result Generated price sequences from generative model better match real price variations.
Adapts Monte Carlo method to price π-options related to maximum drawdown.
problem Pricing π-options in volatile market conditions.
method Monte Carlo algorithm with simulated price tree.
result Algorithm produces bounds converging to true price with tree depth.
Framework for pricing waterfall structures using simulation and uncertainty modeling.
problem Pricing complex structured finance instruments under uncertainty.
method Simulation-based uncertainty modeling, calibrated probability distributions, PyTorch implementation, Adjoint Algorithmic Differentiation (AAD).
result Efficient gradient computation for risk sensitivity analysis and optimization.
Exact path simulation of the underlying state variable is of great practical importance in simulating prices of financial derivatives or their sensitivities when there are no analytical solutions for their pricing formulas. However, in general, the complex dependence structure inherent in most nontrivial stochastic vol…
Developed a simulation method for 3/2 stochastic volatility model.
problem Pricing options in the 3/2 stochastic volatility model.
method Explicit weak solution for the 3/2 model, using inverse CIR process property.
result Simulation algorithm performance comparable to other methods.
AES scheme improves Bermudan and American option pricing for Heston models.
problem Pricing Bermudan and American options under Heston models efficiently.
method AES scheme using non-central chi-square distribution for variance process.
result AES achieves higher accuracy and computational efficiency for Bermudan options.
Study simulates Variance Gamma processes for energy derivatives pricing.
problem Simulating Variance Gamma processes for accurate energy derivative pricing.
method Three-step procedure to relate self-decomposability to increments, derived from Qu et al. (2019). Exact simulation of skeleton of Variance Gamma and symmetric Variance Gamma driven Ornstein-Uhlenbeck processes.
result Exact simulation of Variance Gamma and related processes without numerical inversion.
Pricing Chinese convertible bonds using Monte Carlo simulation and dynamic programming.
problem Pricing Chinese convertible bonds accurately.
method Monte Carlo simulation and dynamic programming with regression and backward induction.
result An underpriced strategy significantly outperforms benchmarks.
The pricing of options, warrants and other derivative securities is one of the great success of financial economics. These financial products can be modeled and simulated using quantum mechanical instruments based on a Hamiltonian formulation. We show here some applications of these methods for various potentials, whic…
The use of sequential Monte Carlo within simulation for path-dependent option pricing is proposed and evaluated. Recently, it was shown that explicit solutions and importance sampling are valuable for efficient simulation of spot price and volatility, especially for purposes of path-dependent option pricing. The result…
Fast, reliable, and error-bounded option pricing with neural networks
problem Fast, reliable, and error-bounded option pricing
method Mixture Density Network
result Out-of-sample CDF error of 1.4imes10−4 Develops methods to simulate option prices for a specific stochastic volatility model.
problem No method exists to compute option prices numerically for a non-martingale jump-type model.
method Develops two Monte Carlo simulation methods under change of measure.
result Conducts numerical experiments to validate the developed methods.
XGB-Chiarella model generates realistic intra-day financial price data using agent-based models.
problem Generating accurate intra-day financial price data for research and risk management.
method Agent-based financial market simulation with XGBoost machine learning calibration.
result XGB-Chiarella model accurately reflects real market behaviours and generates realistic price time series.
Study compares models for pricing multi-strike quanto call options with SV, SC, and SER.
problem Pricing multi-strike quanto call options with stochastic volatility, correlation, and exchange rates.
method Comparative analysis of SV, SC, and SER models; Monte Carlo simulation; Milstein scheme; antithetic variates; correlation risk parameters.
result GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER model combination performs best.
Simulates financial market orders using anomalous diffusion models.
problem Anomalous diffusion in financial market order dynamics.
method Discrete Time Random Walk with Sibuya waiting times, non-uniform sampling, and cubic spline interpolation.
result Demonstrates price impact for different forcing functions and model parameters.
The thesis examines stochastic calculus in option pricing with logistic models and numerical methods.
problem Exploring the application of stochastic calculus in option pricing.
method Monte-Carlo Simulation and machine learning algorithms.
result Insights from Peter Carr and Lorenzo Torricelli's convex duality in continuous models.
The Volterra Heston model is used to price American options.
problem Pricing American options in the Volterra Heston model.
method Kernel-based approximations and simulation techniques.
result Convergence of American option prices in approximating models to the Volterra Heston model.
Physics-Informed Neural Network improves option pricing accuracy.
problem Improving option pricing accuracy using machine learning.
method Physics-Informed Neural Network (PINN) applied to Black-Scholes equation.
result PINN model accurately captures option pricing behavior on both simulated and real market data.
Quantum computing speeds up CDO pricing models.
problem Efficiently pricing complex financial products like CDOs.
method Implemented quantum circuits for Gaussian and Normal Inverse Gaussian copula models, using quantum amplitude estimation.
result Quantum computing can significantly speed up CDO pricing compared to Monte Carlo simulations.
Quantum method prices options by evolving a state in imaginary time.
problem Pricing options in a quantum setting.
method Prepares an initial state, evolves it using imaginary time algorithms, and maps to quantum state.
result Numerical verification for European options; extension to path-dependent options.
Simulates multi-asset spot and option markets using normalizing flows.
problem High-dimensionality of market call prices and dynamic preservation across simulators.
method Normalizing flows for efficient low-dimensional representations, conditional invertibility for joint distribution calibration.
result Calibrated simulators maintain dynamics of each underlying and accurately represent market call prices.
In the paper, the pricing of Quanto options is studied, where the underlying foreign asset and the exchange rate are correlated with each other. Firstly, we adopt Bayesian methods to estimate unknown parameters entering the pricing formula of Quanto options, including the volatility of stock, the volatility of exchange…
Extensive neural networks eliminate the need for SABR pricing formulas.
problem Lack of exact pricing formulas for the SABR model.
method Used a GPU-based simulation and an extensive neural network to learn implied volatilities.
result Neural networks achieve high accuracy and efficiency comparable to Monte-Carlo simulations.
Efficiently simulates SABR model with novel sampling methods.
problem Sampling integrated variance and terminal forward price in SABR model.
method Moment-matched shifted lognormal approximation for integrated variance, CEV approximation for terminal forward price.
result Enhanced simulation scheme is highly efficient, accurate, and reliable.
Quantum algorithm solves financial option pricing using Hamiltonian simulation.
problem Efficiently solving the Black-Scholes equation for option pricing dynamics.
method Mapped Black-Scholes equation to Schrödinger equation, used efficient Hamiltonian simulation techniques.
result Quantum algorithm shows feasible approach for solving financial derivatives on a quantum computer.
The paper bounds payoffs and option prices in discrete models.
problem Measuring risk in discrete models and incomplete markets.
method Analytical and simulated bounds for payoff functions and option prices.
result Analytical and simulated bounds for European and American options.
Deep Hedging removes drift for cleaner option pricing.
problem Finding equivalent martingale measures in markets with frictions.
method Learning minimal near-martingale measures using deep learning.
result Clean hedges for exotic payoffs robust to estimation error.
This research improves option pricing models using Heston, GARCH, and jump diffusion models.
problem Inaccurate option pricing due to Black-Scholes assumptions.
method Monte Carlo simulation, GARCH model, Heston model, Merton jump-diffusion model.
result Heston model produces estimates closer to market prices, Merton model performs well for volatile assets, GARCH model improves volatility forecasts.
Study prices energy derivatives using specific stochastic processes.
problem Pricing energy derivatives in markets driven by specific stochastic processes.
method Calculated characteristic functions, derived non-arbitrage conditions, and developed efficient algorithms for simulation.
result Developed methods for pricing various energy contracts.
This note explores the consequences of nonlinear price impact functions on price dynamics within the chartist-fundamentalist framework. Price impact functions may be nonlinear with respect to trading volume. As indicated by recent empirical studies, a given transaction may cause a large (small) price change if market d…
Quantum-inspired tensor network speeds up financial risk assessment.
problem Efficiently pricing multi-asset derivatives in finance.
method Tensor network algorithms for multi-asset options pricing.
result Tensor network approach yields several orders of magnitude speedup.
We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real wor…
Sequential Monte Carlo (SMC) methods have successfully been used in many applications in engineering, statistics and physics. However, these are seldom used in financial option pricing literature and practice. This paper presents SMC method for pricing barrier options with continuous and discrete monitoring of the barr…
We study time consistent dynamic pricing mechanisms of European contingent claims under uncertainty by using G framework introduced by Peng ([24]). We consider a financial market consisting of a riskless asset and a risky stock with price process modelled by a geometric generalized G-Brownian motion, which features the…
Study normal tempered stable processes for energy derivative pricing.
problem Pricing energy derivatives with spot price models.
method Specified statistical properties, derived non-arbitrage conditions, developed efficient algorithm for trajectory generation.
result Validated pricing models for various energy contracts.
ABIDES-MARL uses MARL to study market behavior in a realistic financial simulation.
problem Understanding equilibrium behavior in complex financial market games.
method Combines MARL with a realistic LOB simulation to study market behavior.
result Validated approach by solving an extended Kyle model and showing how execution strategies shape market dynamics.
This paper speeds up PDV model calibration by learning SPX and VIX prices.
problem Slow calibration of the 4-factor PDV model due to expensive outer simulation.
method Learning SPX and VIX prices with neural networks to reduce outer simulation time.
result Calibration times reduced to just a few seconds.
Barrier options are one of the most widely traded exotic options on stock exchanges. In this paper, we develop a new stochastic simulation method for pricing barrier options and estimating the corresponding execution probabilities. We show that the proposed method always outperforms the standard Monte Carlo approach an…
Study evaluates and compares traditional and causal machine learning methods for estimating direct price effects of environmental amenities.
problem Estimating direct price effects of environmental amenities in housing markets.
method Empirical Monte Carlo simulation to compare traditional regression and causal machine learning approaches.
result Causal Machine Learning (CML) methods, particularly causal forest DID, perform comparably to generalized DID in most scenarios.
The paper models financial asset prices with jumps and evaluates European option prices using numerical methods.
problem Modeling and pricing European options with jumps in delayed stochastic systems.
method Existence, uniqueness, and positivity of solutions to delayed stochastic differential equations with jumps. Application of Fourier transformation for analytical pricing and Monte-Carlo simulation with a logarithmic Euler-Maruyama scheme for numerical approximation.
result The logarithmic Euler-Maruyama scheme provides a positive and convergent method for approximating the solution to the delayed stochastic differential equations with jumps.
Model simulates Perpetual Futures market with agent behavior.
problem Reproduce Perpetual Futures market dynamics.
method Agent-based model with heterogeneous agents trading via a central limit order book.
result Simulation accurately reproduces Perpetual Futures price pegging to Spot price.
Study models illiquid stock prices and finds low correlation due to constant prices.
problem Modeling illiquid stock prices and measuring correlation accurately.
method Combined Markov model with Ornstein Uhlenbeck and geometric Brownian motion.
result Low correlation in USE stocks due to constant prices and illiquidity.
Develops numerical methods for pricing exchange options in a market with limited liquidity.
problem Pricing European style exchange options in a market with finite liquidity.
method Integrates price impact into the dynamics of correlated assets using a controlled variate approach.
result Numerical pricing methods for exchange options are developed and validated.
Paper presents deep LSMC method for efficient variable annuity pricing.
problem Efficiently pricing variable annuities with guarantees using simulation methods.
method Modifies least-squares Monte Carlo (LSMC) algorithm for optimal stochastic control problems.
result Deep LSMC provides more stable and robust pricing performance for higher-dimensional problems.
A blockchain protocol uses bandit algorithms to dynamically price transactions.
problem Maximizing revenue from decentralized blockchain Indexers competing for queries.
method Dynamic pricing using Gaussian bandits for multiple agents.
result Improved revenue through dynamic pricing in a decentralized blockchain environment.
PRIME models cryptocurrency exchange market impact.
problem Understanding and predicting market impact in cryptocurrency exchanges.
method Developed a multi-agent simulation to model market impact.
result Allows better estimation of market slippage and knock-on consequences.