Proposes atomic swaptions for trustless cryptocurrency derivatives.
problem Lack of trustless derivatives for cryptocurrency exchanges.
method Extends atomic swap protocol to include derivatives without oracles.
result Atomic swaptions enable trustless exchange of derivative assets.
Proposes a new method for completing swap cycles in decentralized exchanges.
problem Completing swap cycles in decentralized exchanges efficiently and without slippage.
method Introduces an asset matrix formulation to verify and complete CoW cycles using graph traversal and imbalance correction.
result Demonstrates efficient discovery and insertion of synthetic orders for atomic cycle closure.
Debt swaps improve financial networks by optimizing clearing payments and stability.
problem Improving financial network stability and efficiency through debt swaps.
method Analyzing computational complexity of debt swaps, focusing on semi-positive swaps and v-improving swaps.
result Polynomial length of sequences of semi-positive v-improving swaps for ranking-based clearing, but NP-hard for arbitrary v-improving swaps.
Paper generalizes pricing and hedging of volatility swaps in stochastic models.
problem Pricing and hedging of volatility swaps in stochastic volatility models.
method Generalizes zero vanna approximation to seasoned swaps, derives hedges using vanilla options and variance swaps.
result Pricing and hedging of volatility swaps are made practical and robust.
Swapping debt contracts can mitigate risk in financial networks.
problem Mitigating risk in financial networks through debt swaps.
method Analysis of debt swapping operations in financial networks under various conditions.
result Positive debt swaps can exist in worst-case shock models to minimize losses.
We derive an arbitrage free relationship between recovery swap rates, digital default swap spreads and conventional CDS spreads, and argue that the fair forward recovery rate used in recovery swaps must contain a convexity premium over the expected recovery value.
The paper prices swaps on generalized variance measures for multiple assets.
problem Hedging risk in financial markets with multi-asset swaps.
method Pricing generalized variance swaps using Barndorff-Nielsen and Shephard model.
result Results have implications for commodity sector risk management.
This study reviews techniques to estimate volatility and price Variance Swaps.
problem Estimating historical volatility and pricing Variance Swaps.
method Review of existing techniques.
result Discussion of various methods to estimate volatility and price Variance Swaps.
F. Labourie [arXiv:1212.5015] characterized the Hitchin components for PSL(n,R) for any n>1 by using the swapping algebra, where the swapping algebra should be understood as a ring equipped with a Poisson bracket. We introduce the rank n swapping algebra, which is the quotient of the swap…
In this paper, we model financial markets with semi-Markov volatilities and price covarinace and correlation swaps for this markets. Numerical evaluations of vari- nace, volatility, covarinace and correlations swaps with semi-Markov volatility are presented as well. The novelty of the paper lies in pricing of volatilit…
An uncollateralized swap hedged back-to-back by a CCP swap is used to introduce FVA. The open IR01 of FVA, however, is a sure sign of risk not being fully hedged, a theoretical no-arbitrage pricing concern, and a bait to lure market risk capital, a practical business concern. By dynamically trading the CCP swap, with t…
Exact relationships found between ATM slope, volatility swap, and zero vanna.
problem Understanding relationships between implied volatilities and swaps.
method Analyzes exact relationships between ATM slope, volatility swap, and zero vanna.
result Exact relationships between ATM slope, volatility swap, and zero vanna.
Paper derives formulas for volatility swap strike and zero vanna implied volatility.
problem Relationship between volatility swap strike and zero vanna implied volatility.
method Applied Malliavin calculus to derive exact formulas.
result Zero vanna implied volatility is a better approximation for volatility swap strike.
Unified theory linking atom-centered and message-passing models for molecular properties.
problem Combining atom-centered and message-passing models for accurate molecular property prediction.
method Generalizing ACDC framework to include multi-centered information, providing a complete linear basis for regression.
result Unified understanding of atom-centered and message-passing models, providing a coherent foundation.
A note on setting swap parameters for traders.
problem Determining optimal slippage parameters and trade size for wealth swapping.
method Theoretical solution and framework for optimal slippage parameters and trade size.
result Offers a method to solve optimal slippage parameters and trade size for wealth swapping.
The paper proposes pricing methods for multi-asset generalized variance swaps.
problem Hedging risk in financial markets with complex asset structures.
method Proposes pricing methods for two new measures of generalized variance (maximum eigen-value and trace of covariance matrix) under Markov-modulated volatilities.
result Demonstrates pricing results for three stocks, highlighting the usefulness of these swaps in commodity risk management.
Paper solves no-swap regret minimization for combinatorial bandits with polylogarithmic dependence on N.
problem Design efficient no-swap regret algorithms for combinatorial bandits with exponentially large action space.
method Introduces a no-swap-regret learning algorithm with polylogarithmic dependence on N and demonstrates efficient implementation.
result Achieves no-swap regret with polylogarithmic dependence on N, resolving an open problem.
Derives measure changes for pricing midcurve swaptions.
problem Pricing midcurve swaptions in a forward swap annuity measure.
method Derives measure change formulae and constructs linear and exponential terminal swap rate models.
result Captures midcurve swaption correlation skew.
Interpool solves interoperability issues by minting, exchanging, and burning tokens within a single liquidity pool.
problem Lack of proper interoperability in blockchain use cases.
method Interpool operates as a standalone liquidity pool that mints, exchanges, and burns tokens, optimizing the order of transactions in the mempool.
result Interpool transforms front-running issues into a solution that ensures ultimate liquidity through a burning procedure, enabling trustless design.
We consider the high-dimensional sparse linear regression problem of accurately estimating a sparse vector using a small number of linear measurements that are contaminated by noise. It is well known that the standard cadre of computationally tractable sparse regression algorithms---such as the Lasso, Orthogonal Matchi…
The paper extends the market price of risk for electricity swap contracts, incorporating jump risk.
problem Pricing electricity swap contracts with consideration of jump risk.
method Introducing a Merton type model with jumps and transferring to the physical measure, comparing arithmetic and geometric averaging.
result A decomposition of swap's market price of risk into classical and market price of risk components.
Paper introduces a new pricing method for electricity swaps and options.
problem Pricing electricity swaps and options in markets with varying delivery periods.
method Introduces a weighted geometric averaging of futures prices over delivery periods.
result Arbitrage-free pricing framework for derivatives in electricity markets.
Improved bounds for multicalibration and omniprediction in online and distributional settings.
problem Achieving efficient multicalibration and omniprediction in fairness and loss minimization.
method Proposed an efficient algorithm achieving improved rates for multicalibration and omniprediction.
result Achieved O(T31) ℓ2-swap multicalibration error for convex Lipschitz functions. Researchers use manifold learning to analyze 4D-STEM data of graphene, revealing atomic structure details.
problem Challenges in processing and interpreting large 4D-STEM datasets, especially for light materials.
method Data-driven manifold learning approaches for visualization and exploration of 4D-STEM datasets.
result Extracted patterns relate to individual atom sites and sublattice structures, effectively discriminating single dopant anomalies.
Machine learning predicts atomization energies accurately from low-fidelity calculations.
problem Predicting accurate atomization energies of organic molecules efficiently.
method Machine learning models trained on low-fidelity B3LYP energies to predict high-fidelity G4MP2 energies.
result Predicted G4MP2 atomization energies within 0.012 eV for molecules with 10-14 heavy atoms.
A variance swap is a derivative with a path-dependent payoff which allows investors to take positions on the future variability of an asset. In the idealised setting of a continuously monitored variance swap written on an asset with continuous paths it is well known that the variance swap payoff can be replicated exact…
Paper examines pricing and hedging for cross-currency swaps referencing backward-looking rates.
problem Pricing and hedging cross-currency swaps with backward-looking rates.
method Uses interest rate and currency futures for hedging, analyzes arbitrage-free multi-curve setting.
result Explicit pricing and hedging results for CCBS with backward-looking rates.
The SABR model is shortly presented and the volatility swap explained. The fair value for a volatility swap is then computed using the usual theory in financial mathematics. An analytical solution using confluent hypergeometric functions is found. The solution is then verified using Rama Cont's functional calculus.
Lower bound found for volatility swap in SABR model.
problem Finding a lower bound for volatility swap in SABR model.
method Short time to maturity limit analysis of conditionally lognormal SABR model.
result Zero vanna implied volatility is a lower bound for volatility swap strike.
Injective map found between Poisson algebras.
problem Mapping between Poisson algebras on Grassmannian and swapping algebra.
method Injective Poisson homomorphism from Grassmannian to swapping algebra.
result Found an injective Poisson homomorphism.
We study the fair strike of a discrete variance swap for a general time-homogeneous stochastic volatility model. In the special cases of Heston, Hull-White and Schobel-Zhu stochastic volatility models we give simple explicit expressions (improving Broadie and Jain (2008a) in the case of the Heston model). We give condi…
New features for quantum calculations learn N-center Hamiltonian matrix elements.
problem Quantum calculations need features for N-center Hamiltonians, not just atom-centered ones.
method Developed fully equivariant N-center features for machine learning.
result Learned matrix elements of N-center Hamiltonians efficiently.
This paper investigates the pricing and hedging of variance swaps under a 3/2 volatility model. Explicit pricing and hedging formulas of variance swaps are obtained under the benchmark approach, which only requires the existence of the numéraire portfolio. The growth optimal portfolio is the numéraire portfolio and u…
Neural network learns atomic coordinates from Patterson maps in a simplified case.
problem Training a neural network to infer atomic coordinates from Patterson maps.
method Synthetic data training, centering output maps, removing centrosymmetric inversion, and adding empty space.
result The network can generalize to infer atom positions from Patterson maps not in the training set.
Study compares atom representations in graph neural networks for molecular properties.
problem Incorrect attribution of results in molecular property prediction due to varying atom features.
method Evaluated multiple atom representations on free energy, solubility, and metabolic stability predictions.
result Different atom representations can lead to varying predictive performance in graph neural networks.
Study finds discrepancies in open interest reporting for Bitcoin perpetual swaps.
problem Misquoted open interest in perpetual swaps leads to liquidity and solvency concerns.
method Analyzed tick-by-tick data from seven exchanges to identify discrepancies.
result Open interest reported by exchanges varies widely, some implausible.
We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models a…
There are many studies on development of models for analyzing some derivatives such as credit default swaps .
In this paper, we consider the problem of pricing discretely-sampled variance swaps based on a hybrid model of stochastic volatility and stochastic interest rate with regime-switching. Our modelling framework extends the Heston stochastic volatility model by including the CIR stochastic interest rate and model paramete…
We prove that the introduction of the class of geometrically atomic bundle maps by Harvey and Lawson in their theory of singular connections is not necessary because an arbitrary map satisfies the conditions of geometric atomicity.
Paper explores volatility swaps in rough volatility models.
problem Understanding volatility swaps in rough volatility models.
method Examines the relationship between forward start volatility swaps and implied volatilities in rough volatility models.
result The leading term approximation error in the correlated case does not depend on the time to forward start date.
We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix. By combining these functions with the Gaussian approximation potential approach, we present LC-GAP, a new system for generating atomic potentials through machine learning (ML). Tests on the QM7, QM7b and GDB9 biom…
ASLA learns atomic structures using neural networks and reinforcement learning.
problem Designing materials and drugs with desired properties.
method Atomistic structure learning algorithm (ASLA) using a convolutional neural network and reinforcement learning.
result ASLA can predict optimal structural arrangements of atoms for various target properties.
Italian banks use swaps to hedge against rising interest rates, offsetting losses on debt securities.
problem Interest rate risk on Italian banks' debt securities.
method Analysis of granular regulatory data on euro interest rate swap trades.
result Swaps can offset losses on debt securities, reducing interest rate exposure.
PAM models generate dependent random distributions across groups with overlapping clusters.
problem Generating dependent random distributions across multiple groups.
method Atom skipping in an infinite mixture model.
result Interpretable posterior inference of cluster exclusivity and sharing.
We derive a general multivariate theory for realised characteristics of `model-free discretisation-invariant swaps', so-called because the standard no-arbitrage assumption of martingale forward prices is sufficient to derive fair-value swap rates for such characteristics which have no jump or discretisation errors. Thi…
Non-atomic arbitrage exploits price differences on Ethereum and other blockchains, accounting for over 10% of Ethereum's block value.
problem Price differences on decentralized exchanges and centralized exchanges lead to MEV.
method Analyzed non-atomic arbitrage on Ethereum's largest DEXes, identifying its prevalence and impact.
result More than 10% of Ethereum's block value is attributed to non-atomic arbitrage, involving over $132 billion.
Graph neural network predicts protonation energies of oxygen atoms in bio-oil molecules.
problem Predicting protonation energies of oxygen atoms in bio-oil molecules for chemical upgrading.
method Site-specific graph neural network approach using iterative local nonlinear embedding.
result Effective prediction of protonation energies of individual oxygen atoms in bio-oil molecules.