Deriving option prices from operational-time Markov lattices
problem Option pricing
method Operational-time Markov lattice
result Derives option-pricing equations from an operational-time Markov lattice
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48 results for “pricing operator”
Risk hedging can reduce operational costs by adjusting prices and production levels in response to asset price movements.
problem How risk hedging impacts operational decisions in response to asset price movements.
method Developed and solved a risk-management model integrating risk hedging into a price-setting newsvendor problem.
result Hedging generally reduces optimal price and VPQ, but may increase VPQ under certain conditions.
The paper analyzes sensitivities of cash flows using PDEs and Hansen-Scheinkman decomposition.
problem Large-time sensitivities of cash flows in quantitative finance.
method PDE representation of pricing operator with Hansen-Scheinkman decomposition.
result Detailed convergence rates of sensitivities are provided.
ICON-OCnet solves optimal execution problems with neural networks and few examples.
problem Optimal order execution in markets with unknown price impact.
method Transformer-based neural network architecture (ICON-OCnet) that learns price impact from few examples and applies it to optimal execution strategies.
result ICON-OCnet accurately infers price impact models and retrieves optimal execution strategies for various propagator kernels.
Quantum assets are priced using a new theorem, extending classical asset pricing.
problem Quantum properties in financial markets and assets.
method Developed a new definition of arbitrage for quantum assets and proved a quantum version of the first fundamental theorem of asset pricing.
result There exists a risk-free density operator under which all quantum assets are martingales if no arbitrage exists.
New pricing framework allocates costs of operating reserves and transmission.
problem Allocating costs of operating reserves and transmission efficiently.
method Causation-based framework using contingency-constrained scheduling models.
result More comprehensive and efficient cost-reflective market operations.
Quantum theory reinterprets financial pricing by focusing on observable price transitions.
problem Traditional financial models rely on latent variables; this paper proposes a new observable approach.
method Shift operators, spectral calculus, and Lindblad semigroups are used to define observable frequency operators and convolution generators.
result The framework leads to a nonlocal pricing equation that converges to classical Black-Scholes-Merton under small mesh limits.
We develop a theory of bid and ask price dynamics where the two prices form due to interaction of buy and sell orders. In this model the two prices are represented by eigenvalues of a 2x2 price operator corresponding to "bid" and "ask" eigenstates. Matrix elements of price operator fluctuate in time which results in ph…
We study a novel pricing operator for complete, local martingale models. The new pricing operator guarantees put-call parity to hold for model prices and the value of a forward contract to match the buy-and-hold strategy, even if the underlying follows strict local martingale dynamics. More precisely, we discuss a chan…
In an -framework, we present a few extension theorems for linear operators. We focus the attention on majorant preserving and sandwich preserving types of extensions. These results are then applied to the study of price systems derived by a reasonable restriction of the class of equivalent martingale measures…
Derivative-Informed Operator Learning for Finance: On-the-Fly Greeks, Surfaces, Hedging, and Controlq-fin.MF
Derivative-informed models improve financial surrogates for accurate hedging and risk management.
problem Developing fast surrogate models for financial derivatives and risk quantities.
method Derivative-informed operator learning framework combining neural operators, random features, and tangent sensitivity equations.
result The framework reduces hedging and risk errors by 40-76% compared to standard surrogates.
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.
We study the forward price dynamics in commodity markets realized as a process with values in a Hilbert space of absolutely continuous functions defined by Filipović. The forward dynamics are defined as the mild solution of a certain stochastic partial differential equation driven by an infinite dimensional Lévy proces…
The price of a given stock is exactly known only at the time of sale when the stock is between the traders. If we know the price (owner) then we have no information on the owner (price). A more general description including cases when we have partial information on both price and ownership is obtained by using the quan…
Study shows increased VRE penetration reduces electricity prices and volatility.
problem Impact of increased variable renewable energy on electricity prices and volatility.
method Hourly, real-time data from six ISOs, quantile and skew t-distribution regressions.
result Increased VRE penetration is associated with decreased system electricity price and volatility in most ISOs.
In this article, we investigate the behavior of long-term options. In many cases, option prices follow an exponential decay (or growth) rate for further maturity dates. We determine under what conditions option prices are characterized by this property. To see this, we use the martingale extraction method through which…
We prove existence, uniqueness, and regularity of viscosity solutions to the stationary and evolution obstacle problems defined by a class of nonlocal operators that are not stable-like and may have supercritical drift. We give sufficient conditions on the coefficients of the operator to obtain Hölder and Lipschitz con…
A methodology is developed to identify, as units of study, each decrease in the value of a stock from a given maximum price level. A critical level in the amount of price declines is found to separate a segment operating under a random walk from a segment operating under a power law. This level is interpreted as a poin…
In this paper we propose a quadratic programming model that can be used for calculating the term structure of electricity prices while explicitly modeling startup costs of power plants. In contrast to other approaches presented in the literature, we incorporate the startup costs in a mathematically rigorous manner with…
Recent theoretical results establish that time-consistent valuations (i.e. pricing operators) can be created by backward iteration of one-period valuations. In this paper we investigate the continuous-time limits of well-known actuarial premium principles when such backward iteration procedures are applied. We show tha…
This study optimizes energy storage scheduling under price uncertainty, balancing risk and reward.
problem Optimizing energy storage operation under price uncertainty and risk.
method Two-stage stochastic risk-constrained approach using conditional value-at-risk.
result Increasing risk aversion leads to substantial benefits in terms of risk reduction and expected reward.
We present an arbitrage free theoretical framework for modeling bid and ask prices of dividend paying securities in a discrete time setup using theory of dynamic acceptability indices. In the first part of the paper we develop the theory of dynamic subscale invariant performance measures, on a general probability space…
Approximates derivative pricing under fractional stochastic volatility.
problem Derivative pricing under fractional stochastic volatility model.
method Approximate expression derived from deterministic functions and fractional Ornstein-Uhlenbeck process.
result Numerical simulations show the feasibility and effect of long-range dependencies on derivative prices.
This work forecasts electricity prices using Bayesian regime detection and conditional neural processes.
problem Forecasting electricity prices with optimal operational outcomes.
method Bayesian regime detection with conditional neural processes, integrating multi-criteria decision support.
result R-NP model outperformed other models in comprehensive operational utility assessments.
DeepSVM learns SVMs without PDE solving, achieving high pricing accuracy.
problem Computational bottleneck in real-time calibration of stochastic volatility models.
method Physics-informed Deep Operator Network (PI-DeepONet) that enforces terminal payoffs and no-arbitrage conditions.
result DeepSVM achieves high pricing accuracy across various market dynamics.
FINN learns option pricing and hedging using financial theory.
problem Learning accurate option prices and sensitivities from financial theory.
method Self-supervised replication objective based on dynamic hedging.
result FINN accurately recovers classical Black--Scholes prices and performs robustly in stochastic volatility environments.
Financial derivatives pricing aims to find the fair value of a financial contract on an underlying asset. Here we consider option pricing in the partial differential equations framework. The contemporary models lead to one-dimensional or multidimensional parabolic problems of the convection-diffusion type and generaliz…
Deep neural operators learn complex probabilistic models efficiently.
problem Learning complex probabilistic models with global Lipschitz conditions.
method Deep neural-operator framework under global Lipschitz conditions.
result Explicit network-size bounds for universal approximation of probabilistic models.
This paper develops a spectral theory of Markovian asset pricing models where the underlying economic uncertainty follows a continuous-time Markov process X with a general state space (Borel right process (BRP)) and the stochastic discount factor (SDF) is a positive semimartingale multiplicative functional of X. A key …
Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCAq-fin.ST
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
problem Challenges in predicting stock prices of aerospace companies due to market uncertainty and complexity.
method Combination of Principal Component Analysis (PCA) and Recurrent Neural Networks (RNN).
result PCA improves both accuracy and efficiency of stock price prediction.
Quantum effects improve stock option pricing model.
problem Persistent discrepancies between classical Black-Scholes model and actual stock prices.
method Introduced an additional pseudo-Wiener process to represent non-classical information.
result The norm of a complex quantity compensates for price discrepancies, providing market evidence for non-classical processes.
Study on energy storage's impact on electricity prices and profitability.
problem Analyzing the profitability of energy storage in electricity markets.
method Characterized optimal operating strategy for storage systems, determined equilibrium price in a market with storage, renewables, and conventional producers, and characterized price process using stochastic differential equations.
result Increased average revenues and interquantile ranges for storage assets in energy transition scenarios.
New risk theory for 'Pay-for-Performance' models.
problem How to price and hedge operational and financial risks in new business models.
method Developed a new risk theory and calculation method for 'Pay-for-Performance' models.
result Presented a model for determining risk premiums including both financial and operational risks.
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.
We study the formation of derivative prices in equilibrium between risk-neutral agents with heterogeneous beliefs about the dynamics of the underlying. Under the condition that the derivative cannot be shorted, we prove the existence of a unique equilibrium price and show that it incorporates the speculative value of p…
Model for dynamic pricing across multiple RE groups to maximize revenue.
problem Maximizing revenue from multiple RE pricing groups.
method Mathematical model incorporating multiple pricing groups, revenue goals, and time value of money.
result Algorithm for constructing a pricing policy for multiple RE groups.
Fast probabilistic option price predictions using modular Bayesian inference.
problem Accurate probabilistic predictions of future option prices.
method Modular approximate Bayesian inference framework that combines multiple data sources.
result Accurate probabilistic option-price predictions in realistic scenarios.
In this paper we study dynamic pricing mechanisms of financial derivatives. A typical model of such pricing mechanism is the so-called g--expectation defined by solutions of a backward stochastic differential equation with g as its generating function. Black-Scholes pricing model is a special linear case of this pricin…
We analyze a controlled price formation experiment in the laboratory that shows evidence for bubbles. We calibrate two models that demonstrate with high statistical significance that these laboratory bubbles have a tendency to grow faster than exponential due to positive feedback. We show that the positive feedback ope…
A new adaptive pricing framework for perpetual protocols using liquidity curves and on-chain oraclesq-fin.TR
Adaptive pricing framework for perpetual contracts using liquidity curves and oracles.
problem Ensuring stable and predictable pricing for perpetual contracts.
method Uses liquidity curves and on-chain oracles with parabolic and sigmoid functions to quote prices and fees.
result Ensures pricing stability and predictability through adaptive pricing framework.
Pricing Derivatives under Self-Exciting Dynamics: A Finite-Difference and Transform Approachq-fin.MF
A new method for pricing derivatives using self-exciting dynamics and finite-difference transforms.
problem Pricing derivatives with accumulated marks using a self-exciting marked point process.
method Derive discounted pricing equation as a PIDE, transform to one-dimensional PIDEs, use Laplace/Fourier transform, approximate jump term, solve using finite difference scheme.
result Efficiently price derivatives with accumulated marks using a novel finite-difference and transform approach.
OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecastingq-fin.CP
New method learns interaction-aware orderbook representation for better intraday electricity price forecasting.
problem Challenges in probabilistic intraday electricity price forecasting due to dynamic orderbook microstructure.
method OrderFusion: an end-to-end and parameter-efficient probabilistic forecasting model that learns interaction-aware representation of buy-sell dynamics.
result Consistent improvements over conventional baselines in probabilistic forecasting of CID price indices.
The Price twist creates three 4-manifolds from a 4-sphere.
problem Understanding the properties of a non-simply connected 4-manifold created from a 4-sphere.
method Cutting and pasting operation on a -knot in a 4-manifold.
result The non-simply connected 4-manifold is studied for Kinoshita type -knots.
In the present paper we present a finite element approach for option pricing in the framework of a well-known stochastic volatility model with jumps, the Bates model. In this model the asset log-returns are assumed to follow a jump-diffusion model where the jump component consists of a Levy process of compound Poisson …
Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first de…
Model predicts BESS interactions and price impacts in energy markets.
problem Understanding BESS interactions and price formation in energy markets.
method Stochastic game-theoretic model with linear-quadratic differential game.
result Equilibrium controls and prices derived for BESSs in both heterogeneous and homogeneous settings.
Derives operational-time variance kernel for reaction boundaries in financial markets.
problem Separating components in volatility models to better understand market dynamics.
method Derives a variance kernel for a latent-order-book reaction boundary, separating structural boundary cumulant, clock projection, and pricing-measure choice.
result Operational variance has a closed asymptotic form for long-memory forcing, with effective signed-forcing intensity and resilience.
Enhances option pricing for American-style options using JDOI method.
problem Pricing American-style options efficiently under stochastic volatility.
method Extends DOI variance reduction technique to Lévy dynamics, combining with LSMC.
result Strong variance reduction in option pricing compared to standard LSMC.