We investigate the position of the Buchen-Kelly density in a family of entropy maximising densities which all match European call option prices for a given maturity observed in the market. Using the Legendre transform which links the entropy function and the cumulant generating function, we show that it is both the uni…
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48 results for “Digital call pricing”
Option prices with call pricesq-fin.PR
There exist several methods how more general options can be priced with call prices. In this article, we extend these results to cover a wider class of options and market models. In particular, we introduce a new pricing formula which can be used to price more general options if prices for call options and digital opti…
Study small-time CLTs for stochastic Volterra equations with various kernels.
problem Understanding the behavior of stochastic Volterra equations with different kernels.
method Proved convergence of finite-dimensional distributions, functional CLT, and limit theorems for smooth transformations.
result Derived asymptotic pricing formulae for digital calls in rough volatility models.
Study reveals jumps in crypto markets predict future prices.
problem Understanding jumps in high frequency digital asset markets.
method High frequency crypto data analysis, econometric modeling.
result Intra-day jumps significantly influence end of day returns.
"Digitwashing": The Gap between Words and Deeds in Digital Transformation and Stock Price Crash Riskq-fin.ST
Digitwashing gap boosts stock crash risk, study finds.
problem The gap between companies' digital promises and actual performance increases stock crash risk.
method Empirical analysis of Shanghai and Shenzhen A-share companies from 2010 to 2021, robustness tests conducted.
result GDT significantly increases stock price crash risk, confirmed by robust tests.
In this paper we develop an algorithm to calculate the prices and Greeks of barrier options in a hyper-exponential additive model with piecewise constant parameters. We obtain an explicit semi-analytical expression for the first-passage probability. The solution rests on a randomization and an explicit matrix Wiener-Ho…
We propose a new NFT price index to track the digital art market.
problem Lack of a comprehensive NFT price index.
method Developed a new methodology to create a NFT Price Index.
result Demonstrated the dynamics and performances of NFT markets.
We propose an efficient lattice procedure which permits to obtain European and American option prices under the Black and Scholes model for digital options with barrier features. Numerical results show the accuracy of the proposed method.
Paper develops security model and pricing for stable digital currency in quantum blockchain network.
problem Securing and pricing stable digital currency in a quantum blockchain network.
method Developed a block-based quantum channel networking technology and a FinTech platform model with dynamic pricing.
result Established a generalized IoB security model using quantum channel networking and QKD.
We determine the price of digital double barrier options with an arbitrary number of barrier periods in the Black-Scholes model. This means that the barriers are active during some time intervals, but are switched off in between. As an application, we calculate the value of a structure floor for structured notes whose …
Network-based strategy for optimal cryptocurrency portfolios identified.
problem Challenges in predicting cryptocurrency prices in a volatile market.
method Network methods to identify decorrelated cryptocurrencies, Markowitz Portfolio Theory.
result Network-based portfolios outperform benchmarks with high expected returns.
We introduce a novel stochastic volatility model where the squared volatility of the asset return follows a Jacobi process. It contains the Heston model as a limit case. We show that the joint density of any finite sequence of log returns admits a Gram-Charlier A expansion with closed-form coefficients. We derive close…
Trading bubbles form when traders adapt to price mismatches.
problem Self-sustained price bubbles driven by adaptive trading behavior.
method Multi-agent model illustrating price bubble formation and statistical properties.
result Price bubbles can be driven by adaptive investment strategies.
This paper deals with pricing of European and American options, when the underlying asset price follows Heston model, via the interior penalty discontinuous Galerkin finite element method (dGFEM). The advantages of dGFEM space discretization with Rannacher smoothing as time integrator with nonsmooth initial and boundar…
Double no-touch options, contracts which pay out a fixed amount provided an underlying asset remains within a given interval, are commonly traded, particularly in FX markets. In this work, we establish model-free bounds on the price of these options based on the prices of more liquidly traded options (call and digital …
In Neri and Schneider (2012) we presented a method to recover the Maximum Entropy Density (MED) inferred from prices of call and digital options on a set of n strikes. To find the MED we need to numerically invert a one-dimensional function for n values and a Newton-Raphson method is suggested. In this note we revisit …
Study shows how algorithmic prediction affects US housing market, reducing racial wealth disparities.
problem Impact of algorithmic prediction on housing market and racial wealth disparities.
method Natural experiment using digitization of housing records to study entry, allocation, and prices.
result Digitization leads to increased sale prices for minority-owned homes, reducing racial wealth disparities.
The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economyphysics.soc-ph
What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we foc…
New method uses SVD entropy to price artworks.
problem Lack of fine measurements in traditional art pricing models.
method SVD entropy of painting images for content measurement.
result SVD entropy positively affects sales price at 1% significance level.
Do Prediction Markets Match Option Prices? Bitcoin Threshold Evidence from Binance and Polymarketq-fin.TR
Prediction markets and crypto options show persistent pricing gaps.
problem Comparing prediction markets and crypto options for identical payoffs.
method Comparing Polymarket Yes prices with Binance call option prices.
result Mean pricing gap of 5.6 percentage points across 214 hourly observations.
Study shows monetary policy impacts digital assets like BTC and ETH.
problem Impact of monetary policy on digital assets and DeFi.
method Event study regressions and high-frequency price analysis.
result Monetary policy surprises negatively affect BTC and ETH but not other digital assets.
We consider model-free pricing of digital options, which pay out if the underlying asset has crossed both upper and lower barriers. We make only weak assumptions about the underlying process (typically continuity), but assume that the initial prices of call options with the same maturity and all strikes are known. Unde…
The article models financial asset returns using Gaussian mixtures and EVT-based copulas to price equity options.
problem Modeling financial asset returns and pricing equity options considering extreme values.
method Modeling marginal distributions with Gaussian mixtures and joint dependence structure with EVT-based copulas.
result The approach accurately prices various equity options on Atos and Dassault Systems actions.
New method reduces errors in pricing and sensitivities for discontinuous payoffs.
problem Errors in pricing and sensitivities for discontinuous payoffs in digital and barrier options.
method Alternative methods for estimating sensitivities, including likelihood ratio and hybrid methods.
result New methods substantially reduce test errors in prices and sensitivities.
This paper makes the first attempt to introduce the tools from computer graphics into the art pricing research. We argue that the creation of a painting calls for a combination of conceptual effort and painting effort from the artist. However, as the important price determinants, both efforts are long missing in the tr…
DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
Paper uses deep learning to price and hedge options in incomplete markets.
problem Incomplete markets lack unique no-arbitrage solutions for pricing and hedging European options.
method Constrained deep learning approach with a single neural network representing option prices and hedging strategies.
result Constrained networks produce superior P&L distributions compared to unconstrained networks.
We obtain the maximum entropy distribution for an asset from call and digital option prices. A rigorous mathematical proof of its existence and exponential form is given, which can also be applied to legitimise a formal derivation by Buchen and Kelly. We give a simple and robust algorithm for our method and compare our…
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.
A shallow Bi-LSTM model forecasts Bitcoin prices using engineered features.
problem Lack of high-quality historical Bitcoin data for deep learning models.
method Proposed a shallow Bi-LSTM model with feature engineering on Bitcoin data.
result A shallow deep neural network outperforms other models in Bitcoin price forecasting.
We study the problem of finding probability densities that match given European call option prices. To allow prior information about such a density to be taken into account, we generalise the algorithm presented in Neri and Schneider (2011) to find the maximum entropy density of an asset price to the relative entropy c…
Study predicts NFT bubbles using LPPL model.
problem Tackles bubble prediction of NFTs.
method Applied logarithmic periodic power law (LPPL) model to NFT price data.
result NFTs, Decentraland, and ArtBlocks are in bubbles, while Ethereum Name Service is in a negative bubble.
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.
Enhances MOT with causality constraints for better option pricing.
problem Limited applicability of traditional martingale optimal transport (MOT) for option pricing.
method Integrates causality constraints into MOT and proposes McCormick relaxations for computational tractability.
result Empirically, McCormick MOT yields significant price reductions for basket and digital options compared to classic MOT.
We give an answer to the question given by T.Y.Kong in his article "Can 3-D Digital Topology be Based on Axiomatically Defined Digital Spaces?" In this article he asks the question, if so called "good pairs" of neighborhood relations can be found on the set Z^n such that the existence of digital manifolds of dimension …
We describe a project, called the "Discretization in Geometry and Dynamics Gallery", or DGD Gallery for short, whose goal is to store geometric data and to make it publicly available. The DGD Gallery offers an online web service for the storage, sharing, and publication of digital research data.
Adaptive Multilevel Splitting improves rare event pricing for financial derivatives.
problem Efficient pricing of binary options in rare event regimes with discontinuous payoffs.
method Adaptive Multilevel Splitting (AMS) reformulates rare-event problem as conditional events.
result AMS achieves up to 200-fold improvements over standard Monte Carlo, preserving unbiasedness.
Generative models create personalized patient health simulations.
problem Creating accurate digital twins for personalized medicine.
method Neural network architecture for conditional generative models of clinical trajectories.
result Same architecture generates accurate twins across 13 indications.
CRBM generates digital twins for MS patients, aiding in disease progression analysis.
problem Characterizing and analyzing disease progression in MS patients.
method Unsupervised machine learning with Conditional Restricted Boltzmann Machines (CRBMs).
result Generated digital twins are statistically indistinguishable from actual subjects.
This research predicts Bitcoin prices using wavelet and deep stacking approach.
problem Predicting price fluctuations of Bitcoin due to its price volatility.
method Wavelet for noise removal, deep learning models (neural networks and transformers), feature selection.
result The model achieved high accuracy in predicting Bitcoin prices at different time intervals.
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.
Study examines trading costs on Uniswap, finding adversarial slippage is significant for large trades and certain assets.
problem Analyzing costs and slippage in decentralized exchanges (DEXs).
method Empirical evaluation of Uniswap's USDC-ETH and PEPE-ETH pools, calculating slippage and reordering slippage.
result Adversarial slippage is significant for large trades and certain assets like PEPE.
Bitcoin volatility can be predicted from price and alternative data.
problem Predicting Bitcoin volatility from market data.
method Modeling Bitcoin volatility using price, volatility momentum, and alternative data like sentiment and engagement.
result Bitcoin volatility can be predicted with a lag of several hours.
Efficient evaluation of double-barrier options and joint cpdf of a Lévy process and its two extremaq-fin.CP
Fast method developed for pricing barrier options and joint Lévy process distributions.
problem Accurate pricing of barrier options and joint distributions in Lévy models.
method Dual space calculations, Wiener-Hopf factorization, sinh-deformations, Gaver-Wynn Rho acceleration.
result Achieves precision of in seconds and in fractions of a second.
We discuss a common suspicion about reported financial data, in 10 industrial sectors of the 6 so called "main developing countries" over the time interval [2000-2014]. These data are examined through Benford's law first significant digit and through distribution distances tests. It is shown that several visually anoma…
New model prices crypto options by clustering market regimes and using implied volatility.
problem Inaccurate option pricing for volatile crypto markets.
method Time-regime clustering with Implied Stochastic Volatility Model (ISVM).
result MR-ISVM overcomes complexity and adapts to market dynamics.
The paper analyzes gold, oil, and bitcoin futures volatility and basis.
problem Understanding the volatility and basis of gold, oil, and bitcoin futures.
method Contract-by-contract analysis of spot and futures prices, trading volume, and open interest data.
result Trading volume positively affects volatility in all three assets, while open interest has a possible negative effect.
Enhanced multi-fidelity models improve digital twin accuracy and uncertainty quantification.
problem Lack of detailed application-specific data and inaccurate sensor data hinder surrogate model learning for digital twins.
method Proposes a multi-fidelity surrogate model framework integrating PCFE and GP, and deep-HPCFE with auto-regression schemes.
result Demonstrates improved accuracy and uncertainty quantification in digital twin systems.