The complete Gaussian kernel in the multi-factor Heston model: Option pricing and implied volatility applications

Recchioni, Maria Cristina; Iori, Giulia; Tedeschi, Gabriele; Ouellette, M.

doi:10.1016/j.ejor.2020.11.050

Cited by 15 publications

(3 citation statements)

References 56 publications

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“…The coefficient of determination describes how much an independent variable contributes to its dependent variables. Obtain the coefficient of the decision can be calculated using Equations ( 5) [24,25]:…”

Section: Data Processingmentioning

confidence: 99%

Comparing Gaussian and Epanechnikov Kernel of Nonparametric Regression in Forecasting Issi (Indonesia Sharia Stock Index)

Farida

Purwanti

Ulinnuha

2022

BAREKENG: J. Il. Mat. & Ter.

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ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.

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Section: Data Processingmentioning

confidence: 99%

Comparing Gaussian and Epanechnikov Kernel of Nonparametric Regression in Forecasting Issi (Indonesia Sharia Stock Index)

Farida

Purwanti

Ulinnuha

2022

BAREKENG: J. Il. Mat. & Ter.

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“…For VIX futures and Exchange-Traded products these features are studied in [10]. The complexity of volatility markets is also exemplified by the difficulty to jointly model the behavior of the volatility smiles of vanilla options written on the underlying and its volatility index, see for instance [6,64,68]. This longstanding puzzle is known as the S&P 500 (SPX)/VIX calibration puzzle.…”

Section: Introductionmentioning

confidence: 99%

The Rough Hawkes Heston Stochastic Volatility Model

2022

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“…The volatility of financial assets plays an extremely important role in investment decision (Alaali, 2020), option pricing (Recchioni et al, 2020), and stock market movement (Balli et al, 2015). Therefore, using appropriate models to forecast volatility possesses an important theoretical significance and practical value.…”

Section: Introductionmentioning

confidence: 99%

Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models

Song

Tang

Wang

et al. 2022

Journal of Forecasting

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Empirical experiments have shown that macroeconomic variables can affect the volatility of stock market. However, the frequencies of macroeconomic variables are low and different from the stock market volatility, and few literature considers the low‐frequency macroeconomic variables as input indicators for deep learning models. In this paper, we forecast the stock market volatility incorporating low‐frequency macroeconomic variables based on a hybrid model integrating the deep learning method with generalized autoregressive conditional heteroskedasticity and mixed data sampling (GARCH‐MIDAS) model to process the mixing frequency data. This paper firstly takes macroeconomic variables as exogenous variables then uses the GARCH‐MIDAS model to deal with the problem of different frequencies between the macroeconomic variables and stock market volatility and to forecast the short‐term volatility and finally takes the predicted short‐term volatility as the input indicator into machine learning and deep learning models to forecast the realized volatility of stock market. It is found that adding macroeconomic variables can significantly improve the forecasting ability in the comparison of the forecasting effects of the same model before and after adding the macroeconomic variables. Additionally, in the comparison of the forecasting effects among different models, it is also found that the forecasting effect of the deep learning model is the best, the machine learning model is worse, and the traditional econometric model is the worst.

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The complete Gaussian kernel in the multi-factor Heston model: Option pricing and implied volatility applications

Cited by 15 publications

References 56 publications

Comparing Gaussian and Epanechnikov Kernel of Nonparametric Regression in Forecasting Issi (Indonesia Sharia Stock Index)

Comparing Gaussian and Epanechnikov Kernel of Nonparametric Regression in Forecasting Issi (Indonesia Sharia Stock Index)

The Rough Hawkes Heston Stochastic Volatility Model

Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models

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