Characterizes super-replication prices in a financial market model.
problem Characterizing prices in a financial market model.
method Characterizes prices as the supremum of mono-prior super-replication prices through extreme priors and martingale measures.
result Super-replication prices are the supremum of mono-prior super-replication prices.
Fine-tuning a time series model improves financial price prediction accuracy.
problem Improving accuracy in predicting financial market prices using large models.
method Continual pre-training of a time series foundation model on financial data to fine-tune its performance for price prediction.
result The fine-tuned model outperforms the baseline in various financial metrics.
The paper discovers and evaluates support and resistance levels in financial time series.
problem Understanding and predicting support and resistance levels in financial markets.
method Developed a heuristic discovery algorithm to identify SR levels in intraday price series.
result Discovered SR levels statistically significantly reverse price trends and have a decay aspect over time.
Quantum theory explains price dynamics in financial markets, capturing bid-ask spread and ergodicity.
problem Nature of price formation in financial markets and bid-ask spread dynamics.
method Developed a quantum coupled-wave theory using a 2x2 price operator with eigenvalues representing bid and ask prices.
result The theory adequately models bid-ask spread and directional price movement due to quantum-chaotic interaction.
Model shows how relaxed leverage can lead to asset price bubbles.
problem Understanding how financial leverage affects asset prices and growth.
method Developed a macro-finance model with feedback loops between investment and land prices.
result Relaxed leverage can cause unbalanced growth and asset price bubbles.
Model explains stock price bubbles through debt crises and financial crashes.
problem Analyzing financial fragility and stock price bubbles.
method Stock-flow consistent model integrating macroeconomic and financial market dynamics.
result Model demonstrates how credit expansion and crash risk lead to recurrent boom-bust cycles.
Proposes a model for clearing prices in financial markets due to margin calls.
problem Determining prices in financial markets following margin calls and short squeezes.
method Developed an explicit formulation for clearing prices after margin calls and short squeezes.
result Identified a threshold short interest ratio leading to discontinuity in clearing prices.
LLMs improve stock price forecasting from financial news and reports.
problem Predicting stock prices with high accuracy and robustness.
method Analyzing financial news, reports, and transcripts using LLMs.
result LLMs can improve stock price forecasting but face practical challenges.
Neural models price financial options without assuming underlying price forms.
problem Pricing financial options under flexible price processes.
method Apply neural SDEs as universal approximators, use Wasserstein distance for training.
result Error in option prices bounded by Wasserstein distance used for training.
The problem of hedging and pricing sequences of contingent claims in large financial markets is studied. Connection between asymptotic arbitrage and behavior of the α~-~quantile price is shown. The large Black-Scholes model is carefully examined.
Modeling financial markets with sandpile model to understand price volatility and arbitrage constraints.
problem Understanding price volatility and arbitrage constraints in financial markets.
method Uses a sandpile model to represent information and price changes, linking size of price volatility to the scaling law of avalanches.
result Identifies a structural tension between non-arbitrage condition and price adjustments consistent with a constant Sharpe ratio.
Nonparametric pricing and hedging of exotic derivatives using signature payoffs.
problem Pricing and hedging exotic derivatives accurately and efficiently.
method Introducing signature payoffs and using them to approximate and price exotic derivatives nonparametrically.
result Signature payoffs enable accurate and computationally tractable pricing and hedging of exotic derivatives.
New financial model revises risk measure under NA condition.
problem Revising classical financial mathematics with coherent risk measure on L0. method Developed a new version of the fundamental theorem of asset pricing and provided dual representations.
result Set of risk-hedging prices is closed under NA condition.
Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.
problem Understanding the dependence structure between insurance and financial risks.
method Dynamic equilibrium analysis of insurance pricing with worst-case beliefs.
result Correlation ambiguity does not necessarily increase insurance prices or reduce insurers' utility.
Informer improves option pricing accuracy in volatile markets.
problem Challenges in accurate option pricing due to market volatility and traditional model limitations.
method Applying Informer, a Transformer-based neural network, for option pricing.
result Informer outperforms traditional models in option pricing accuracy.
FNSPID dataset integrates financial news and stock prices for improved market predictions.
problem Lack of comprehensive datasets combining quantitative and qualitative financial data.
method Developed a large-scale dataset (FNSPID) with 29.7M stock prices and 15.7M financial news records.
result FNSPID significantly boosts market prediction accuracy and sentiment analysis.
Deep learning for financial derivatives pricing and hedging.
problem Model-free pricing and optimal hedging of financial derivatives.
method Neural networks for offline training and online application.
result Accurate model-free price bounds and optimal hedging strategies.
We introduce, in continuous time, an axiomatic approach to assign to any financial position a dynamic ask (resp. bid) price process. Taking into account both transaction costs and liquidity risk this leads to the convexity (resp. concavity) of the ask (resp. bid) price. Time consistency is a crucial property for dynami…
We introduce an autoregressive-type model of prices in financial market taking into account the self-modulation effect. We find that traders are mainly using strategies with weighted feedbacks of past prices. These feedbacks are responsible for the slow diffusion in short times, apparent trends and power law distributi…
Extended speculation game improves Hurst exponent of financial time series.
problem Anti-persistent market price behavior resulting in small Hurst exponent.
method Introduced a perturbative part to price change considering additional effects.
result Improved Hurst exponent value of financial time series.
Modeling price-mediated contagion in financial systems with capital requirements.
problem Understanding and quantifying the cost of capital requirements on financial stability.
method Developed a two-tier pricing structure and conditions for clearing prices, providing sensitivity analysis.
result Quantified the cost of regulation and value of bailouts in financial systems.
Recently, a novel adaptive wave model for financial option pricing has been proposed in the form of adaptive nonlinear Schrödinger (NLS) equation [Ivancevic a], as a high-complexity alternative to the linear Black-Scholes-Merton model [Black-Scholes-Merton]. Its quantum-mechanical basis has been elaborated in [Ivancevi…
The paper models insurance market dynamics under uncertainty and financial frictions.
problem Modeling insurer behavior under uncertainty and financial frictions.
method Dynamic equilibrium model of insurance market with competitive insurers maximizing shareholder value.
result Investment can lead to lower insurance prices and negative loadings under certain conditions.
Coronavirus impacts oil prices through volatility and direct effects.
problem Impact of coronavirus on oil prices and volatility.
method ARDL estimation controlling for financial volatility and US economic policy uncertainty.
result COVID-19 daily infections have a negative long-term impact on oil prices.
This paper explains how predictable order flow can lead to Brownian motion in financial prices.
problem Why financial prices exhibit Brownian motion despite predictable order flow.
method Generalized Lillo-Mike-Farmer model to nonlinear price-impact dynamics, mapping to Lévy-walk model.
result Price dynamics remain diffusive under the square-root law, even with persistent order flow.
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 …
The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they shared. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviated from Kolmogorov monofractal scaling of the velocity fluctu…
A simple quantitative example of a reflexive feedback process and the resulting price dynamics after an exogenous price shock to a financial network is presented. Furthermore, an outline of a theory that connects financial reflexivity, which stems from cross-ownership and delayed or incomplete information, and no-arbit…
A new pricing model from game theory fits financial data well.
problem Financial models lack economic justification and randomness assumptions.
method CMMV pricing model based on game theory and information asymmetry.
result The CMMV model predicts option prices and volatility surface well.
Investigates cross-impact kernels for financial asset prices.
problem Understanding and parameterizing cross-impact kernels for financial asset prices.
method Examined martingale-admissible and no-statistical-arbitrage-admissible kernels, determined their overlap, and provided calibration formulas.
result Identified the overlap between martingale-admissible and no-statistical-arbitrage-admissible kernels and provided formulas for their calibration.
Paper uses IGA for efficient pricing of financial derivatives, comparing it to FDM and FEM.
problem Efficiently pricing complex financial derivatives with high accuracy.
method Isogeometric Analysis (IGA) for solving nonlinear Black-Scholes PDEs.
result IGA provides very accurate solutions with fewer knots, significantly reducing computational time.
Predict stock price movements using financial data and news articles with LLMs.
problem Predicting stock price movements using financial data and news articles.
method Combining financial data and news articles, employing pre-trained LLMs, and using retrieval augmentation techniques.
result Predicted stock price movements with a weighted F1-score of 58.5% and 59.1%.
We introduce a new Self-Organized Criticality (SOC) model for simulating price evolution in an artificial financial market, based on a multilayer network of traders. The model also implements, in a quite realistic way with respect to previous studies, the order book dy- namics, by considering two assets with variable f…
New financial price model using earning yield derived from CIR process.
problem Excess volatility and equity premium puzzles in financial markets.
method Proposes a new financial price process based on earning yield and Cox-Ingersoll-Ross (CIR) process.
result Derives analytically stylized facts of financial prices and returns, including power law distribution of returns and fat-tailed distribution of prices.
Enhances option pricing with fractional order Black-Scholes-Merton model.
problem Improving precision and authenticity of option pricing.
method Integrates fractional order Black-Scholes-Merton with neural networks.
result Improves accuracy in capturing complex diffusion dynamics and memory effects.
In this paper, we implement a stochastic deflator with five economic and financial risk factors: interest rates, market price of risk, stock prices, default intensities, and convenience yields. We examine the deflator with different financial assets, such as stocks, zero-coupon bonds, vanilla options, and corporate cou…
Improved price bounds for financial derivatives using time-homogeneous stock movements.
problem Deriving robust price bounds for financial derivatives under time-homogeneous stock movements.
method Variant of martingale optimal transport problem with time-homogeneity assumption.
result Improved price bounds are derived, incorporating market data from multiple time points.
Financial derivatives based on road travel times for hedging and pricing.
problem Market risk in crypto and banking sectors.
method Modeling travel time data with CARMA models and applying risk-neutral pricing.
result Derivatives pricing based on travel time and its volatility.
Quantum computing speeds up Bermudan option pricing.
problem Efficient pricing of financial derivatives, especially Bermudan options.
method Quantum amplitude estimation combined with Chebyshev interpolation.
result Quadratic speed-up over classical methods.
Study shows stock prices influence news more than the other way around.
problem Understanding the interdependency between stock market and financial news.
method Time series analysis using five classification models.
result Stock prices have a greater impact on news contents than the other way around.
The paper analyzes financial market turbulence using mathematical physics.
problem Understanding price fluctuations caused by information asymmetry.
method Spectrum analysis to decompose pricing patterns.
result Identifies phase correlations in financial stock market turbulence.
Risk management in financial derivative markets requires inevitably the calculation of the different price sensitivities. The literature contains an abundant amount of research works that have studied the computation of these important values. Most of these works consider the well-known Black and Scholes model where th…
The use of kinetic modelling based on partial differential equations for the dynamics of stock price formation in financial markets is briefly reviewed. The importance of behavioral aspects in market booms and crashes and the role of agents' heterogeneity in emerging power laws for price distributions is emphasized and…
We price financial models using optimization and probability theory.
problem Financial model pricing under risk-averse investors.
method Infinite dimensional optimization, probabilistic and functional analytic tools.
result Existence of optimal strategies and convergence of reservation prices.
This paper uses Malliavin calculus to price and compute delta of financial derivatives in jump-diffusion models.
problem Pricing and delta computation of financial derivatives in jump-diffusion models with stochastic intensity.
method Utilizes Malliavin calculus to price and compute delta, applying the Euler scheme for convergence analysis.
result Established the convergence of approximated solution, financial derivative, and its delta Greeks.
New financial model with sandwiched volatility for option pricing.
problem Developing a new financial model for option pricing.
method Introducing a new model with stochastic volatility driven by a Gaussian Volterra process, ensuring the solution is sandwiched between two arbitrary Hölder continuous functions.
result Developed an algorithm for pricing options with discontinuous payoffs using Malliavin calculus.
Model predicts asset prices from initial shocks using neural networks.
problem Missing data on actual asset liquidations limits model calibration.
method Dual neural network structure, first stage maps shocks to liquidations, second stage uses liquidations to predict prices.
result Model accurately predicts equilibrium prices from initial shocks without liquidation data.
Machine learning models outperform traditional CAPM in forecasting financial asset prices.
problem Predicting and forecasting financial asset prices and returns.
method Comparison of modern Machine Learning algorithms with the Capital Asset Pricing Model (CAPM) on U.S. equities data.
result Implemented Machine Learning models significantly outperform the CAPM on out-of-sample test data.