Characterizes smiles in delta satisfying specific conditions.
problem Characterizing no butterfly arbitrage smiles in delta.
method Using parametrization of the smile in delta, we characterize the set of smiles.
result Obtained a parametrization of the set via one real number and three positive functions.
All SMILES VAE learns molecule latent representations from SMILES strings.
problem Non-unique SMILES strings and high computational cost of graph convolutions hinder VAEs for molecular property optimization.
method Stacked recurrent neural networks encode multiple SMILES strings, pooling hidden representations, and attentional pooling builds a final latent representation.
result All SMILES VAE significantly surpasses state-of-the-art in molecular property optimization tasks.
GEN generates millions of valid SMILES with high novelty and property conservation.
problem Generating high-quality, de novo molecules in a known chemical space.
method GEN uses bidirectional RNNs with concatenated sub-models to learn and generate SMILES, with online examination to ensure quality.
result GEN can generate SMILES with 95-98% validity, 85-90% novelty, and 95-99% property conservation.
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…
Text classification on drug SMILES strings yields competitive drug type classification results.
problem Classifying drug types using conventional text classification methods.
method Treated drug SMILES as sentences and applied basic NLP methods for classification.
result Competitive drug type classification results achieved.
Study examines implied volatility smiles around jumps in high-frequency S&P500 index data.
problem Understanding implied volatility smiles around market jumps.
method High-frequency analysis of SPX S&P500 index option data using principal components.
result Volatility smiles exhibit abnormal properties around jumps, independent of maturity and option type.
Extends saddle-point method for large-time volatility smiles.
problem Analyzing large-time volatility smiles in financial models.
method Saddle-point approach to derive large-time model-implied volatility smiles.
result Provides theoretical foundation and wide class of arbitrage-free parametrizations.
Volatility smiles emerge from imperfect hedging in financial markets.
problem Imperfect hedging in financial markets leads to volatility smiles.
method Examined option prices as fair game agreements based on expected payoffs and risk.
result Resulting prices lead to the volatility smile.
Modified Vanna-Volga method constructs Normal volatility smiles.
problem No method existed for constructing Normal volatility smiles.
method Modified Vanna-Volga method applied to Normal volatilities.
result The Vanna-Volga method can easily fit both convex and concave smiles.
Extends SABR model for pricing RFR caplets.
problem Pricing backward RFR caplets in a post-Libor market.
method Closed-form effective SABR parameters for backward RFR caplets.
result Closed-form solution for backward RFR caplets.
Sharp bounds for VIX futures derived from S&P 500 smiles.
problem Deriving precise bounds for VIX futures prices.
method Model-free sub/superreplication of VIX using S&P 500 and its options.
result Improved bounds for VIX futures prices using functionally generated portfolios.
SMILES Transformer learns molecular fingerprints for drug discovery.
problem Poor performance of rule-based molecular fingerprints in shallow prediction models or small datasets.
method Unsupervised pre-training of a sequence-to-sequence language model on a corpus of SMILES.
result SMILES Transformer outperformed existing methods in small-data settings.
We derive a new, exact and transparent expansion for option smiles, which lends itself both to analytical approximation and, perhaps more importantly, to congenial numerical treatments. We show that the skew and the curvature of the smile can be computed as exotic options, for which the Hedged Monte Carlo method is par…
In the Black-Scholes context we consider the probability distribution function (PDF) of financial returns implied by volatility smile and we study the relation between the decay of its tails and the fitting parameters of the smile. We show that, considering a scaling law derived from data, it is possible to get a new f…
A new model shows joint calibration of SPX and VIX smiles is possible.
problem Jointly fitting SPX and VIX smiles is challenging.
method Combining rough volatility and price-feedback effect in the quadratic rough Heston model.
result The quadratic rough Heston model can calibrate SPX and VIX smiles simultaneously.
Deep learning for smile detection on embedded systems.
problem Real-time smile detection in low-resource environments.
method Comparison of neural network architectures on NVidia Jetson platform with asynchronous multithreading.
result Low complexity architectures can achieve similar performance to larger networks with less computation.
DCNN improves volatility smile and skewness calibration without arbitrage constraints.
problem Calibrating volatility smile and skewness surfaces with no arbitrage constraints.
method Derivative-Constrained Neural Network (DCNN) incorporating derivatives in the loss function.
result DCNN generates a smooth surface that satisfies no-arbitrage conditions.
Improved Heston model produces steeper smile for short maturities.
problem Implied volatility surface does not produce a steep enough smile for short maturities.
method Introduced Stationary Heston model with invariant measure and used Product Recursive Quantization for numerical solution.
result Stationary Heston model produces a steeper smile for short maturities.
Our derivation of the distribution function for future returns is based on the risk neutral approach which gives a functional dependence for the European call (put) option price, C(K), given the strike price, K, and the distribution function of the returns. We derive this distribution function using for C(K) a Black-Sc…
We develop a new method to price SOFR futures contracts considering convexity, skew, and smile.
problem Analyzing and pricing SOFR futures contracts with convexity, skew, and smile adjustments.
method A perturbative formalism based on a time-ordered exponential series to solve the backward-Kolmogorov diffusion PDE.
result An analytic pricing formula for SOFR futures contracts that incorporates convexity, skew, and smile adjustments.
The rBergomi model is improved with a regime switching change of measure to match market VIX smiles.
problem The rBergomi model produces flat VIX smiles, not matching market observations.
method A regime switching stochastic change of measure is applied to the rBergomi model, using an inhomogeneous fractional Ornstein-Uhlenbeck equation and an efficient Monte Carlo method.
result The model produces upward sloping VIX smiles, aligning with market observations.
In this paper we investigate the asymptotics of forward-start options and the forward implied volatility smile in the Heston model as the maturity approaches zero. We prove that the forward smile for out-of-the-money options explodes and compute a closed-form high-order expansion detailing the rate of the explosion. Fu…
CNF model improves molecular prediction accuracy.
problem Improving molecular prediction accuracy with limited data.
method Combining CNN and SMILES multiplicity.
result CNF model outperforms traditional descriptors on small datasets.
We prove here a general closed-form expansion formula for forward-start options and the forward implied volatility smile in a large class of models, including the Heston stochastic volatility and time-changed exponential Lévy models. This expansion applies to both small and large maturities and is based solely on the p…
Derives an option-pricing formula for fractional markets with skew and smile.
problem Developing a pricing formula for financial options with skew and smile.
method Employed the Lévy-Khintchine theorem and fractional Gaussian noise to generalize the Black-Scholes-Merton formula.
result An exponentially convergent option-pricing formula for fractional markets.
We simplify SVI volatility smile constraints for three sub-SVIs without numerical methods.
problem No arbitrage constraints for SVI volatility smiles.
method Explicit domain derivation for sub-SVIs without numerical procedures.
result Explicit no arbitrage domains for Symmetric SVI, Vanishing Upward/Downward SVI, and SSVI.
Paper addresses xVA models for market-implied skew and smile.
problem Capturing market-implied skew and smile in xVA calculations.
method Developed a state-dependent SDE combining Hull-White models with RAnD technique.
result Demonstrated significant effect of skew and smile on xVA calculations.
Smile-GANs clusters brain MRI scans to reveal disease subtypes and progression.
problem Understanding disease heterogeneity in brain MRI scans.
method Generative Adversarial Networks (GANs) for semi-supervised clustering.
result Discovered four subtypes of Alzheimer's and prodromal phases, with two progressive pathways.
This study examines how earnings announcements affect option volatility and pricing.
problem The impact of earnings announcements on option volatility and pricing.
method Analysis of extremely short-term options data to study bimodality and concavity in IV curves.
result Investors pay a premium to hedge against extreme volatility during earnings announcements in the presence of concave IV smiles.
Study examines implied volatility behavior in Bachelier model.
problem Characterizing implied volatility in Bachelier model for large strikes.
method Exploiting regular variation theory, derived explicit expressions for Bachelier implied volatility.
result Established a rigorous connection between characteristic function analyticity and volatility smile asymptotic slope.
We review and illustrate how the volatility smile translates into a probability distribution, the market-implied probability distribution representing believes priced in. The effects of changes in the smile are examined. Special attention is given to the effects of slope, which might appear at first counter-intuitive. …
Relativistic extension of Brownian motion explains volatility smiles.
problem Understanding volatility smiles in financial markets.
method Relativistic extension of Brownian motion to model financial processes.
result The model predicts volatility smiles due to relativistic effects.
SMILES2Vec learns chemical properties from SMILES strings without feature engineering.
problem Predicting chemical properties from SMILES strings without manual feature engineering.
method Deep RNN (SMILES2Vec) learns features from SMILES strings, optimized using Bayesian optimization.
result Optimized SMILES2Vec outperforms MLP neural networks and achieves 88% accuracy in predicting solubility.
We consider the fractional Heston model originally proposed by Comte, Coutin and Renault. Inspired by recent ground-breaking work on rough volatility, which showed that models with volatility driven by fractional Brownian motion with short memory allows for better calibration of the volatility surface and more robust e…
We introduce a new class of local volatility models. Within this framework, we obtain expressions for both (i) the price of any European option and (ii) the induced implied volatility smile. As an illustration of our framework, we perform specific pricing and implied volatility computations for a CEV-like example. Nume…
Study develops methods for pricing and hedging volatility options in rough volatility models.
problem Pricing and hedging volatility options in rough volatility models, especially for VIX products.
method Developed efficient Monte Carlo methods and asymptotic approximations for Gaussian Volterra process models, and introduced modulated Volterra processes for VIX smile.
result Modulated Volterra processes successfully capture the VIX smile, improving model fit for VIX products.
In this work, we aim to gain a better understanding of the volatility smile observed in options markets through microsimulation (MS). We adopt two types of active traders in our MS model: speculators and arbitrageurs, and call and put options on one underlying asset. Speculators make decisions based on their expectatio…
We study in details the skew of stock option smiles, which is induced by the so-called leverage effect on the underlying -- i.e. the correlation between past returns and future square returns. This naturally explains the anomalous dependence of the skew as a function of maturity of the option. The market cap dependence…
ChemBoost predicts protein-ligand binding affinity using SMILES syntax.
problem Predicting high affinity drug-target interactions from sequence similarity alone.
method ChemBoost uses SMILES syntax to represent ligands as documents and proteins as sequences or ligand-centric features. It learns chemical word embeddings and predicts affinities using eXtreme Gradient Boosting.
result ChemBoost outperforms state-of-the-art systems in predicting protein-ligand affinities.
Deep learning model integrates SMILES and molecular descriptors for EGFR inhibitor prediction.
problem Improving drug discovery by integrating structural and property data.
method Attention-based deep learning architecture trained on SMILES and molecular descriptors.
result Max MCC 0.58 and AUC 90% on EGFR inhibitors dataset, outperforming reference model.
A new volatility model calibrates SPX & VIX smiles with 6 parameters.
problem Joint calibration of SPX and VIX smiles with a simple model.
method Quintic Ornstein-Uhlenbeck volatility model with polynomial volatility process.
result Remarkable joint fits of SPX-VIX smiles with only 6 parameters.
We revisit the ``Smile Dynamics'' problem, which consists in relating the implied leverage (i.e. the correlation of the at-the-money volatility with the returns of the underlying) and the skew of the option smile. The ratio between these two quantities, called ``Skew-Stickiness Ratio'' (SSR) by Bergomi (Smile Dynamics …
SMILE improves explainability of machine learning models.
problem Difficulty in understanding and trusting the conclusions of black-box machine learning models.
method Statistical Model-agnostic Interpretability with Local Explanations (SMILE).
result SMILE makes machine learning models more interpretable.
Study on implied volatility for multi-factor rough volatility models.
problem Understanding implied volatility in multi-factor rough volatility models.
method Large deviations principle and numerical methods to compute rate function.
result Identification of models generating non-linear smiles.
Heteroencoders improve chemical latent space diversity and molecular generation.
problem Improving chemical latent space properties and diversity in autoencoders.
method Employing SMILES enumeration for encoder or decoder, training RNNs with LSTM, and using QSAR models.
result Heteroencoders yield more diverse latent spaces and better molecular generation.
Improved model for SOFR, SONIA, and ESTR caplets pricing.
problem Accurate pricing of options on backward-looking rates.
method Extended Turfus and Romero-Bermúdez model to include smile and skew.
result Simple effective variance formulae for caplet pricing.
Study proposes a method to construct copulas using corrected Hermite polynomial expansion for estimating foreign exchange volatility.
problem Estimating cross foreign exchange volatility with complex correlation structures.
method Applying corrections to the finite sum of multivariate Hermite polynomial expansions to construct copulas.
result The proposed copula method accurately reproduces the volatility smile of cross currency pairs.
The Multi Variate Mixture Dynamics model is a tractable, dynamical, arbitrage-free multivariate model characterized by transparency on the dependence structure, since closed form formulae for terminal correlations, average correlations and copula function are available. It also allows for complete decorrelation between…