We study option pricing and hedging with uncertainty about a Black-Scholes reference model which is dynamically recalibrated to the market price of a liquidly traded vanilla option. For dynamic trading in the underlying asset and this vanilla option, delta-vega hedging is asymptotically optimal in the limit for small u…
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48 results for “Vega”
A new hedging strategy uses deep reinforcement learning to manage gamma and vega risks.
problem Managing gamma and vega risks in derivatives trading with stochastic underlying.
method Deep distributional reinforcement learning (D4PG) combined with quantile regression.
result Optimal hedging strategy depends on objective function, transaction costs, and option maturity.
From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Setscs.LG
We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC) estimators of Restricted Boltzmann Machines (RBMs). We denote our approach Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange for random running times. MCLV uses a stopping set built from the training data and has maximum…
PCA reveals a market factor in S&P500 implied volatilities.
problem Constructing factor models from implied volatility data.
method PCA on implied volatility tensor structure.
result An OI and Vega-weighted index is a significant factor.
We analyze 27 house price indexes of Las Vegas from Jun. 1983 to Mar. 2005, corresponding to 27 different zip codes. These analyses confirm the existence of a real-estate bubble, defined as a price acceleration faster than exponential, which is found however to be confined to a rather limited time interval in the recen…
Study explores geometric implications of timelike conformal Killing vectors.
problem Exploring geometric implications of timelike conformal Killing vectors.
method Investigates geometric consequences of timelike conformal Killing vector fields on globally hyperbolic spacetimes.
result Provides complementary result to Galloway and Vega's main theorem.
PIVOT bridges Black-Scholes price and implied volatility spaces via a differentiable layer.
problem Lack of a differentiable interface between price and implied volatility spaces.
method Develops PIVOT, a differentiable layer that preserves LBR's forward pass and avoids backpropagation through branch logic, addressing singularity issues.
result PIVOT achieves high performance and accuracy, reducing price and implied volatility errors by up to 43.4% and 21.3% respectively.
Optimal hedging strategies for exotic options using vanilla options.
problem Hedging exotic options with illiquid vanilla options.
method Simple approximations and variational techniques in a market model and stochastic volatility model framework.
result Optimal Delta and Vega hedging strategies can be computed easily.
The aim of this paper is to present a dual-term structure model of interest rate derivatives in order to solve the two hardest problems in financial modeling: the exact volatility calibration of the entire swaption matrix, and the calculation of bucket vegas for structured products. The model takes a series of long-ter…
Study cliquet options in a jump-diffusion model with Lévy processes.
problem Pricing cliquet options in a complex financial model with jumps.
method Developed semi-analytic expressions using Lévy process distribution and Fourier transform.
result Inferred semi-analytic expressions for cliquet option prices and derived Greeks.
We consider the problem of learning a general graph using edge-detecting queries, where the number of vertices is given to the learner. The information theoretic lower bound gives for the number of queries, where is the number of edges. In case the number of edges is also given t…
Unified pricing method for FX options with barriers.
problem Calculating the value and sensitivities of FX options with barriers.
method Unified Vanna-Volga pricing technique for single and double barrier FX options.
result Derivation of closed formulas for Delta, Vega, Vanna, and Volga.
Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Managementcs.LG
ANADDH uses deep learning to improve volatility risk management.
problem Traditional Vega hedging strategies are inadequate for rapidly changing markets.
method Combines distributional reinforcement learning with adaptive Nesterov acceleration.
result Significant performance gains over existing hedging techniques.
Iroko enables RL for datacenter CC, outperforming TCP on fat-tree and dumbbell topologies.
problem Stability and over-fitting issues in RL for datacenter networks.
method Developed Iroko emulator to support various network conditions and algorithms.
result Deep RL algorithms outperform TCP on fat-tree and dumbbell topologies.
Limit curve theorems for incomplete metric spaces and the null distance on Lorentzian manifoldsmath.DG
Proves limit curve theorem for incomplete metric spaces, applies to null distance in Lorentzian manifolds.
problem Control of Lorentzian lengths of limit curves in incomplete metric spaces.
method Proves limit curve theorem for incomplete metric spaces and applies to null distance.
result Strong control on Lorentzian lengths of limit curves in Sormani and Vegas' null distance.
Characterizes intrinsic Lorentzian spaces using midpoint properties.
problem Deciding if a metric is length-based in Lorentzian spaces.
method Adapting midpoint criteria from metric geometry to Lorentzian pre-length spaces.
result Spaces with specific midpoint properties are strictly or merely intrinsic.
Study extends null distance concept to Lorentzian length spaces for spacetime analysis.
problem Understanding spacetime convergence and topology in Lorentzian geometry.
method Extend null distance concept to Lorentzian length spaces, study Gromov-Hausdorff convergence.
result First results on compatibility of null distance with synthetic curvature bounds in warped product Lorentzian length spaces.
New uncertainty principle for Schrödinger equations on hyperbolic manifolds.
problem Uncertainty principle for Schrödinger equations on hyperbolic manifolds.
method General strategy of Escauriaza-Kenig-Ponce-Vega, new Carleman estimates, logarithmic convexity, new mollifier and weight function.
result Similar rigidity phenomenon as in Euclidean space persists in hyperbolic geometry.
New null distance bounds confirm Big Bang singularity in cosmological models.
problem Understanding the geometry of spacetime near Big Bang singularities.
method Developed a new null distance metric for temporal functions and applied it to cosmological models.
result Null distance is bounded by a constant multiple of Riemannian distance on level sets with constant gradient norm.
Note: Causality can be encoded without strict time function choice.
problem Global encoding of causality under natural conditions.
method Observation of weakening causality assumptions in existing results.
result Causality can be encoded without strict time function choice.
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.
Computation of option greeks under hybrid stochastic volatility models via Malliavin calculusq-fin.PR
This study introduces computation of option sensitivities (Greeks) using the Malliavin calculus under the assumption that the underlying asset and interest rate both evolve from a stochastic volatility model and a stochastic interest rate model, respectively. Therefore, it integrates the recent developments in the Mall…
Stochastic Spot/Volatility Correlation in Stochastic Volatility Models and Barrier Option Pricingq-fin.PR
Most models for barrier pricing are designed to let a market maker tune the model-implied covariance between moves in the asset spot price and moves in the implied volatility skew. This is often implemented with a local volatility/stochastic volatility mixture model, where the mixture parameter tunes that covariance. T…
The paper constructs Gromov-Hausdorff metrics for Lorentzian spaces and calculates dimensions.
problem Understanding the geometry and dimensions of Lorentzian spaces.
method Construction of Gromov-Hausdorff metrics, calculation of dimensions, and analysis of Lorentzian spaces.
result Dushnik-Miller dimension of Minkowski spaces is countably infinite.
Market makers use a simplified approach for options trading.
problem Optimal control of a high-dimensional portfolio of options.
method Approximating portfolio vega, using a low-dimensional functional equation, and numerical methods.
result The problem of an option market maker is reduced to a tractable, low-dimensional problem.
The distribution of a time integral of geometric Brownian motion is not well understood. To price an Asian option and to obtain measures of its dependence on the parameters of time, strike price, and underlying market price, it is essential to have the distribution of time integral of geometric Brownian motion and it i…
This paper examines the valuation of a generalized American-style option known as a Game-style call option in an infinite time horizon setting. The specifications of this contract allow the writer to terminate the call option at any point in time for a fixed penalty amount paid directly to the holder. Valuation of a pe…
Study Gromov hyperbolic domains in Minkowski space, proving equivalence to boundary properties.
problem Investigate Gromov hyperbolic domains in Minkowski space.
method Explicit comparisons between metrics, dynamical arguments, and quasi-hyperbolic metric.
result Gromov hyperbolicity of convex, future complete domains is equivalent to stable acausality of the boundary.
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…
Neural-SDE models improve option hedging with lower errors and robustness.
problem Improving option hedging strategies using machine learning.
method Derive sensitivity-based and minimum-variance-based hedging strategies using neural-SDE market models.
result Neural-SDE models achieve lower hedging errors and are more robust than traditional models.
We address the information content of European option prices about volatility in terms of the Fisher information matrix. We assume that observed option prices are centred on the theoretical price provided by Heston's model disturbed by additive Gaussian noise. We fit the likelihood function on the components of the VIX…
VegasFlow accelerates complex simulations across various hardware platforms.
problem Complex calculations and simulations requiring high-dimensional integrals.
method Monte Carlo integration techniques using Vegas algorithm and TensorFlow.
result Significantly faster performance on various hardware platforms.
Null distance metric studies spacetime convergence.
problem Investigate convergence in spacetime geometry.
method Introduced null distance metric for Lorentzian manifolds, proving convergence results.
result Null distance metric leads to distinct limiting behavior under non-uniform convergence of warping functions.
Neural networks improve efficiency in integrating multi-dimensional phase spaces in particle physics.
problem Efficiently integrating multi-dimensional phase spaces in particle physics.
method Optimized Neural Network (NN) algorithm for phase space integration.
result NN-based approach achieves unweighting efficiencies of 30-75% in various particle physics examples.
The paper proves uniform Temple charts and applies them to null distance metrics.
problem Proving the existence of uniform Temple charts and their applications to null distance metrics.
method Constructing uniform Temple charts and estimating gradients of optical functions; applying these charts to study spacetime metrics.
result Proves is a rectifiable metric space and applies a Lorentzian isometry theorem.
New distances defined between space-times, proving some definite.
problem Defining distances between space-times.
method Introducing causal-null-compactifiable space-times and using cosmological time and null distance.
result Various definite distances defined, proving convergence of space-times.
We analyze how uncertainty in models affects optimization outcomes using Wasserstein distances.
problem Sensitivity of optimization problems to model uncertainty.
method Non-parametric approach using Wasserstein balls to capture uncertainty, providing explicit corrections for value function and optimizer.
result Explicit formulae for first-order corrections to value function and optimizer.
We provide a mathematical definition of fragility and antifragility as negative or positive sensitivity to a semi-measure of dispersion and volatility (a variant of negative or positive "vega") and examine the link to nonlinear effects. We integrate model error (and biases) into the fragile or antifragile context. Unli…
The paper explores null distance convergence for warped product spacetimes.
problem Defining convergence for sequences of spacetimes as metric spaces.
method Using the null distance to define convergence of spacetimes.
result Optimal convergence theorem for warped product spacetimes.
MVA Transfer Pricingq-fin.PR
This article prices OTC derivatives with either an exogenously determined initial margin profile or endogenously approximated initial margin. In the former case, margin valuation adjustment (MVA) is defined as the liability-side discounted expected margin profile, while in the latter, an extended partial differential e…
Solves VaR-constrained portfolio optimization in markets with stochastic volatility.
problem Optimizing portfolio in markets with stochastic volatility under VaR constraints.
method Dynamic programming approach to Heston's stochastic volatility model.
result Optimal investment strategy linked to unconstrained problem via a vega-neutral derivative.
Quasi-Monte Carlo speeds up option Greeks calculation on GPUs.
problem Efficiently calculating option Greeks for risk management.
method Quasi-Monte Carlo (QMC) combined with GPU acceleration for pathwise sensitivity calculation.
result Increased computational speed and efficiency in estimating option Greeks.
Researchers compute Greeks for rough Volterra SV models using Malliavin calculus.
problem Computing Greeks under rough Volterra stochastic volatility models.
method Malliavin calculus techniques, extending integration by parts to non-square integrable functionals.
result Formulas for computing Greeks (Delta, Gamma, Rho, Vega) under various rough Volterra SV models.
Study sequences of static spacetimes using null distance convergence.
problem How to define convergence for sequences of spacetimes.
method Define null distance metric space structure compatible with Lorentzian structure.
result Prove VADB theorem for sequences of static spacetimes with null distance.
Study of convergence in Lorentzian spacetimes using temporal functions.
problem Non-compactness of spacetime isometries and convergence in semi-Riemannian settings.
method Introduced anchored convergence and used Cauchy temporal functions to define convergence for spacetimes.
result Established local and global regularity of Cauchy temporal functions and their properties.
Enhanced options trading strategies using advanced portfolio optimization.
problem Generating consistent positive returns in high-frequency options trading.
method Advanced portfolio optimization techniques applied to SPY options data.
result Sophisticated strategies incorporating advanced Greeks show potential in high-frequency trading.
Optimal hedging strategies identified for markets with fast-varying volatility.
problem No perfect hedge in markets with fast-varying stochastic volatility.
method Analyzes various delta-type hedging strategies and their performance in a specific asymptotic regime of rapid mean reversion.
result Identifies the `practitioners' delta hedging scheme as optimal in the considered regime of rapid mean reversion.
Hedging Cryptocurrency Optionsq-fin.PR
Study the hedging of cryptocurrency options in a volatile market.
problem Hedging options in a volatile, non-stationary cryptocurrency market.
method Calibrated to SVI-implied volatility surfaces, Monte Carlo price paths generated using SVCJ, GARCH, and historical data. Delta, Delta-Gamma, Delta-Vega, and Minimum Variance strategies applied. Wide range of market models tested.
result Calibration results indicate stochastic volatility, low jump frequency, and infinite activity. Short-dated options less sensitive to volatility or Gamma hedges; longer-dated options benefit from multiple-instrument hedges.