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Online adaptive machine learning based algorithm for implied volatility surface modeling
16 references·0 citing·49 second hop
Online adaptive machine learning based algorithm for implied volatility surface modeling (2018)Online adaptive machine learning based al…Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedging and Fitting (2018)Machine Learning for Quantita…A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing (2016)A new hybrid parametric and m…Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options (2014)Parametric models and non-par…Forecasting the volatility of crude oil futures using intraday data (2014)Forecasting the volatility of…Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble With Time Window in Corporate Financial Risk Prediction (2013)Concept Drift-Oriented Adapti…Using neural network for forecasting TXO price under different volatility models (2011)Using neural network for fore…A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method (2011)A novel bankruptcy prediction…Forecasting volatility with support vector machine‐based GARCH model (2009)Forecasting volatility with s…A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects (2008)A discrete-time model for dai…Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity (2006)Forecast approach using neura…A semiparametric factor model for implied volatility surface dynamics (2006)A semiparametric factor model…A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data (2005)A Realized Variance for the W…Forecasting Volatility in Financial Markets: A Review (2003)Forecasting Volatility in Fin…Forecasting Volatility in Financial Markets: A Review (2003)Forecasting Volatility in Fin…Implied Volatility Functions: Empirical Tests (1998)Implied Volatility Functions:…Using neural networks to forecast the S&P 100 implied volatility (1996)Using neural networks to fore…Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)A comparison of time series and machine learning models for inflation forecasting: empirical evidence from the USA (2016)Volatility forecasting via SVR–GARCH with mixture of Gaussian kernels (2016)Stacking hybrid GARCH models for forecasting Bitcoin volatility (2021)The Pricing of Options and Corporate Liabilities (1973)The Pricing of Options on Assets with Stochastic Volatilities (1987)A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options (1993)The Implied Market Price of Weather Risk (2011)Predictable Dynamics in Higher-Order Risk-Neutral Moments: Evidence from the S&P 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References

  • 2003Forecasting Volatility in Financial Markets: A Review1,373 cited
  • 1998Implied Volatility Functions: Empirical Tests1,248 cited
  • 2003Forecasting Volatility in Financial Markets: A Review800 cited
  • 2005A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data306 cited
  • 2014Forecasting the volatility of crude oil futures using intraday data263 cited
  • 2008A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects203 cited
  • 2011A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method183 cited
  • 2018Machine Learning for Quantitative Finance: Fast Derivative Pricing, Hedging and Fitting158 cited
  • 2006A semiparametric factor model for implied volatility surface dynamics111 cited
  • 2009Forecasting volatility with support vector machine‐based GARCH model87 cited
  • 2014Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options84 cited
  • 1996Using neural networks to forecast the S&P 100 implied volatility70 cited
  • 2006Forecast approach using neural network adaptation to support vector regression grey model and generalized auto-regressive conditional heteroscedasticity70 cited
  • 2013Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble With Time Window in Corporate Financial Risk Prediction

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63 cited
  • 2016A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing43 cited
  • 2011Using neural network for forecasting TXO price under different volatility models41 cited
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