The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …
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48 results for “Uncertain market price”
Model quantifies uncertainty's impact on European option prices.
problem Uncertainty in market volatility risk affects option pricing.
method Hamilton-Jacobi-Bellman framework and finite element method.
result Dependence of Delta on uncertainty is nonlinear and varied.
In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…
Quantum methods model uncertain volatility in financial markets.
problem Modeling financial asset prices with uncertain volatility.
method Quantum stochastic calculus with unitary and non-unitary time evolution.
result Different volatility levels encoded in quantum states, leading to varied market price evolutions.
New model predicts dynamic volatility in uncertain financial markets.
problem Predicting dynamic volatility in financial markets with uncertainty.
method Generalized Barndorff-Nielsen and Shephard (BN-S) model considering delay and fuzziness.
result Effective prediction of dynamic volatility with improved performance.
Paper uses ML for high-dimensional option pricing under uncertain volatility model.
problem High-dimensional option pricing under uncertain volatility.
method Two ML approaches: GTU and NNU.
result Significant improvement in option pricing precision.
Robust Pricing of Equity-Indexed Annuities under Uncertain Volatility and Stochastic Interest Rateq-fin.PR
Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.
problem Pricing equity-indexed annuities with early surrender risk under uncertain market conditions.
method Advanced financial modeling techniques, including uncertain volatility framework and Hull-White model for interest rate dynamics. Numerical algorithm using tree-based framework with local volatility optimization.
result High effectiveness of the proposed numerical algorithm compared to machine learning-based methods.
A machine learning approach to compute Black-Scholes prices with uncertain volatility.
problem Approximating financial markets with continuous-time models like Black-Scholes when data is discrete.
method Generalized Polynomial Chaos (gPC) method combined with a machine learning technique called Bi-Fidelity.
result Efficient numerical method to quantify uncertainty in derivative pricing.
Study option pricing in sideways markets and target zones.
problem Option pricing in sideways markets and target zones.
method Closed-form option pricing formulas for sideways markets and target zones.
result Closed-form option pricing formulas for sideways markets and target zones.
Robust discrete-time super-hedging strategies under AIP condition and under price uncertaintyq-fin.PR
Solves super-hedging for financial models with uncertain prices.
problem Super-hedging European or Asian options in discrete-time models with uncertain prices.
method Numerical procedure under AIP condition to compute infimum price.
result Solves super-hedging problem under weak no-arbitrage condition.
This paper deals with an optimal position management problem for a market maker who has to face uncertain customer order flows in an illiquid market, where the market maker's continuous trading incurs a stochastic linear price impact. Although the execution timing is uncertain, the market maker can also ask its OTC cou…
The study challenges the reliability of VaR due to market randomness.
problem Reliability and accuracy of VaR predictions are compromised by market randomness.
method Introduces market-based probabilities of price and return, dependent on trade values and volumes.
result Market-based price volatility is more accurate than frequency-based VaR predictions.
We investigate financial markets under model risk caused by uncertain volatilities. For this purpose we consider a financial market that features volatility uncertainty. To have a mathematical consistent framework we use the notion of G-expectation and its corresponding G-Brownian motion recently introduced by Peng (20…
Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) tech…
Study a market with uncertain informed traders, finding price impact depends on both asset value and informed trader count distribution.
problem Uncertain participation of informed traders in a market with limit orders.
method Characterized equilibrium by a fixed point integral equation, analyzed large order asymptotics, solved numerically.
result Equilibrium price impact depends on both asset value and distribution of informed traders, not just expected number of informed traders.
We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggregate probability distribution over these events. Researchers have proposed many aggregation methods, although on the question of which is be…
The target of this paper is to establish the bid-ask pricing frame work for the American contingent claims against risky assets with G-asset price systems (see \cite{Chen2013b}) on the financial market under Knight uncertainty. First, we prove G-Dooby-Meyer decomposition for G-supermartingale. Furthermore, we consider …
Paper analyzes arbitrage in uncertain markets, providing quantitative asset pricing.
problem Dealing with model uncertainty in markets that allow small arbitrage.
method Quantitative analysis of arbitrage, focusing on asset price processes close to martingales.
result Quantitative version of the Fundamental Theorem of Asset Pricing and Super-Replication Theorem.
Grid-scale batteries' bid patterns in price uncertainty markets
problem Interpreting bids from grid-scale batteries in wholesale electricity markets under price uncertainty
method Developing an asset-level model of a price-taking battery
result Empirical results deliver insights into withholding behavior, uncertainty effects, and risk management reshaping bid curves
Uniform AMMs control loss in prediction markets.
problem Controlling loss in prediction markets.
method Loss-versus-rebalancing (LVR) framework and uniform AMMs.
result Uniform AMMs achieve proportional LVR to pool value.
Uniform AMMs control loss in prediction markets.
problem Controlling loss in prediction markets.
method Loss-versus-rebalancing (LVR) framework and uniform AMMs.
result Uniform AMMs achieve proportional LVR to pool value.
In this paper, we propose an equilibrium pricing model in a dynamic multi-period stochastic framework with uncertain income streams. In an incomplete market, there exist two traded risky assets (e.g. stock/commodity and weather derivative) and a non-traded underlying (e.g. temperature). The risk preferences are of expo…
A method for accurate pricing of multidimensional derivatives under uncertain volatility.
problem High-dimensional stochastic control problem in uncertain volatility model.
method Backward actor-critic stochastic policy gradient scheme combining DP, PPO, and neural networks.
result Accurate and efficient pricing of multidimensional derivatives compared to benchmarks.
Revisiting the Black-Scholes equationcond-mat.stat-mech
In common finance literature, Black-Scholes partial differential equation of option pricing is usually derived with no-arbitrage principle. Considering an asset market, Merton applied the Hamilton-Jacobi-Bellman techniques of his continuous-time consumption-portfolio problem, deriving general equilibrium relationships …
Economic Complexity Limits Accuracy of Price Probability Predictions by Gaussian Distributionsq-fin.GN
Price and return predictions are limited by economic complexity, not just volatility.
problem Limited accuracy of price and return probability forecasts by Gaussian distributions.
method Analyzes economic reasons behind limitations in predicting price and return statistical moments.
result Predictions of price and return probabilities by Gaussian distributions are inaccurate due to economic complexity.
We propose a probabilistic framework for pricing derivatives, which acknowledges that information and beliefs are subjective. Market prices can be translated into implied probabilities. In particular, futures imply returns for these implied probability distributions. We argue that volatility is not risk, but uncertaint…
Market inefficiencies arise from density-dependent returns in a noisy environment.
problem Market inefficiencies and excess volatility.
method Developed a market model using ecological concepts.
result Market dynamics are density-dependent, leading to inefficiencies.
Study finds social media investor emotions predict stock prices.
problem Validation of social media sentiment models in predicting market behavior.
method Employed EmTract, an emotion model, to test social media sentiment against lab experiments.
result Firm-specific investor emotions forecast daily asset price movements.
In this paper, we study the asymptotic behavior of Asian option prices in the worst case scenario under an uncertain volatility model. We give a procedure to approximate the Asian option prices with a small volatility interval. By imposing additional conditions on the boundary condition and cutting the obtained Black-S…
Paper provides a method to price electricity storage contracts using COS technique.
problem Valuation of electricity storage contracts considering physical and operational constraints.
method Uses Fourier-based COS method to price contracts based on stochastic polynomial process.
result The COS method accurately and efficiently prices electricity storage contracts.
TSFMs outperform traditional models in electricity price forecasting.
problem Accurate electricity price forecasting for effective decision-making.
method Benchmarking several TSFMs against traditional models using real-world data.
result MSTL model consistently outperforms TSFMs across countries and metrics.
Proposes a new uncertain volatility model with worst-case scenario analysis.
problem Modeling and pricing options under uncertain volatility.
method Connection between G-HJB equations and 2BSDEs for option pricing.
result Derives a limit model for worst-case price scenario.
Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.
problem Fair pricing in dynamic, uncertain demand scenarios.
method Contextual bandit algorithm with dynamic pricing and demand learning.
result Achieves optimal regret bound with fairness constraints.
Investor optimizes worst-case portfolio in uncertain markets.
problem Optimizing investment in markets with potential crashes.
method Enhanced martingale approach via BSDEs and PDEs.
result Characterized indifference optimal strategies for various models.
This paper predicts stock prices during unusual events like the pandemic.
problem Lack of models to predict stock price changes during catastrophic events.
method ARIMA, LSTM, sentiment analysis models trained on historical data.
result Achieved 98% prediction accuracy for stock prices during anomalous circumstances.
Study optimizes trading strategies in markets with transaction costs and uncertain models.
problem Optimizing trading strategies in markets with transaction costs and model uncertainty.
method Maximizing worst-case expected utility over a class of models on a filtered probability space.
result Existence of optimal trading strategies for general càdlàg price processes and incomplete filtrations.
We study a Markov-Functional (MF) interest-rate model with Uncertain Volatility Displaced Diffusion (UVDD) digital mapping, which is consistent with the volatility-smile phenomenon observed in the option market. We first check the impact of pricing Bermudan swaptions by the model. Next, we also investigate the future s…
Study on learning strategies in matching markets with uncertain preferences.
problem Decision-making in scarcity of shared resources with unknown agent preferences.
method Representation of preferences in a reproducing kernel Hilbert space, learning algorithm for uncertainty.
result Optimal strategies derived to maximize agents' expected payoffs, with stability and fairness properties.
We study an optimal execution problem with uncertain market impact to derive a more realistic market model. We construct a discrete-time model as a value function for optimal execution. Market impact is formulated as the product of a deterministic part increasing with execution volume and a positive stochastic noise pa…
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
problem Real-time market prediction requires accurate predictions before a deadline.
method Filters feasible models, dispatches workers, trades accuracy for deadline risk.
result Optimized pool achieves 0.991 timely accuracy and 0.994 raw accuracy.
Model uncertainty is a type of inevitable financial risk. Mistakes on the choice of pricing model may cause great financial losses. In this paper we investigate financial markets with mean-volatility uncertainty. Models for stock markets and option markets with uncertain prior distribution are established by Peng's G-s…
Forecasting Uncertain Events with Small Groupscond-mat.stat-mech
We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncert…
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…
The paper examines Indian market bubbles using financial ratios.
problem Detecting bubbles in emerging markets like India is challenging.
method Utilizes financial ratios and the Philips et al 2015 right-tailed unit test.
result Identifies various bubble episodes in the Indian market.
A new high-frequency market making strategy using Deep Hawkes process.
problem Optimizing high-frequency trading in volatile markets.
method Developed a Deep Hawkes process to model order arrivals and their effects on the limit order book.
result The new strategy outperforms traditional methods in market making.
In recent years, a market for mortality derivatives began developing as a way to handle systematic mortality risk, which is inherent in life insurance and annuity contracts. Systematic mortality risk is due to the uncertain development of future mortality intensities, or {\it hazard rates}. In this paper, we develop a …
Paper optimizes financial trading strategies under uncertain market conditions.
problem Guaranteeing robust positive expected profits in financial systems.
method Transformed semi-infinite constraints into structured policies and proposed a novel graphical approach.
result Demonstrated superior risk-adjusted returns and downside risk compared to conventional strategies.
Proposes a novel evolutionary model for stock price prediction.
problem Challenges in financial markets, such as adaptability and interpretability.
method Trader-Company method, which aggregates suggestions from multiple weak learners (Traders) to predict stock returns.
result Shows the effectiveness of the method through experiments on real market data.