A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
This study reviews techniques to estimate volatility and price Variance Swaps.
Calibrates historical and implied correlations in energy markets.
Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-Itô-OI model, we assume that the option-implied…
This paper evaluates different methods to estimate S&P 500 volatility.
Analyzes how rough volatility affects stock pricing and risk premium.
We investigate whether it is possible to formulate option pricing and hedging models without using probability. We present a model that is consistent with two notions of volatility: a historical volatility consistent with statistical analysis, and an implied volatility consistent with options priced with the model. The…
It has been recently shown that spot volatilities can be very well modeled by rough stochastic volatility type dynamics. In such models, the log-volatility follows a fractional Brownian motion with Hurst parameter smaller than 1/2. This result has been established using high frequency volatility estimations from histor…
New distribution resolves excess volatility puzzle in finance.
We undertake a systematic comparison between implied volatility, as represented by VIX (new methodology) and VXO (old methodology), and realized volatility. We compare visually and statistically distributions of realized and implied variance (volatility squared) and study the distribution of their ratio. We find that t…
The Local Volatility model is a well-known extension of the Black-Scholes constant volatility model whereby the volatility is dependent on both time and the underlying asset. This model can be calibrated to provide a perfect fit to a wide range of implied volatility surfaces. The model is easy to calibrate and still ve…
Paper tackles rough volatility estimation from high-frequency data.
Novel method for nowcasting implied volatility using neural operators.
We revisit the problem of pricing options with historical volatility estimators. We do this in the context of a generalized GARCH model with multiple time scales and asymmetry. It is argued that the reason for the observed volatility risk premium is tail risk aversion. We parametrize such risk aversion in terms of thre…
Implied volatilities form a well-known structure of smile or surface which accommodates the Bachelier model and observed market prices of interest rate options. For the swaptions that we study, three parameters are taken into account for indexing the implied volatilities and form a "volatility cube": strike (or moneyne…
A new method simulates implied volatility surfaces for multiple assets.
Paper uses AI to predict stock market volatility with neural networks and genetic algorithms.
We show that the moments of the distribution of historic stock returns are in excellent agreement with the Heston model and not with the multiplicative model, which predicts power-law tails of volatility and stock returns. We also show that the mean realized variance of returns is a linear function of the number of day…
We investigate the historical volatility of the 100 most capitalized stocks traded in US equity markets. An empirical probability density function (pdf) of volatility is obtained and compared with the theoretical predictions of a lognormal model and of the Hull and White model. The lognormal model well describes the pd…
Generative diffusion models forecast implied vol surfaces without arbitrage issues.
Paper improves volatility forecasting for new issues and spin-offs.
In this chapter, we consider volatility swap, variance swap and VIX future pricing under different stochastic volatility models and jump diffusion models which are commonly used in financial market. We use convexity correction approximation technique and Laplace transform method to evaluate volatility strikes and estim…
Survey of continuous volatility models, focusing on fractional and rough methods.
Paper proposes a new GPR-HS framework for accurate VCV estimation in global equity indices.
The study forecasts portfolio volatility using cointegrated asset dynamics.
We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock pr…
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…
Paper introduces CSIE for estimating stock market volatility.
Proposes NDIG model to capture bitcoin volatility and option pricing.
Study uses machine learning to predict stock prices, finds Kalman filter works well for low-volatility stocks.
Rough volatility models are very appealing because of their remarkable fit of both historical and implied volatilities. However, due to the non-Markovian and non-semimartingale nature of the volatility process, there is no simple way to simulate efficiently such models, which makes risk management of derivatives an int…
Generates consistent IV surfaces using VAEs and SDE models.
Classical (Itô diffusions) stochastic volatility models are not able to capture the steepness of small-maturity implied volatility smiles. Jumps, in particular exponential Lévy and affine models, which exhibit small-maturity exploding smiles, have historically been proposed to remedy this (see \cite{Tank} for an overvi…
Rough volatility models are known to reproduce the behavior of historical volatility data while at the same time fitting the volatility surface remarkably well, with very few parameters. However, managing the risks of derivatives under rough volatility can be intricate since the dynamics involve fractional Brownian mot…
A new method to estimate local volatility from high-frequency data.
The intrinsic entropy model accurately estimates stock market volatility.
New model prices crypto options by clustering market regimes and using implied volatility.
This study compares three volatility metrics for Bitcoin, highlighting high expected volatility.
The paper studies estimation of parameters of diffusion market models from historical data. The standard definition of implied volatility for these models presents its value as an implicit function of several parameters, including the risk-free interest rate. In reality, the risk free interest rate is unknown and need …
A pairs trading model with time-varying volatility using stochastic control.
The paper models Gasoil options using Brent benchmarks, improving volatility estimation.
PCA reveals a market factor in S&P500 implied volatilities.
Proposes deep hedging for index options using implied volatility surface.
The paper evaluates criteria for selecting cryptocurrencies based on historical data.
It has been recently shown that rough volatility models, where the volatility is driven by a fractional Brownian motion with small Hurst parameter, provide very relevant dynamics in order to reproduce the behavior of both historical and implied volatilities. However, due to the non-Markovian nature of the fractional Br…
Paper approximates rough stochastic local volatility models for efficient computation.
Analyzes multi-day stock returns, showing linear volatility and mean dependence.
We study the risk premium impact in the Perturbative Black Scholes model. The Perturbative Black Scholes model, developed by Scotti, is a subjective volatility model based on the classical Black Scholes one, where the volatility used by the trader is an estimation of the market one and contains measurement errors. In t…
New models improve stock and wind speed forecasting.