The Economic Value of Equity Implied Volatility Forecasting with Machine Learning

2020 ◽  
Author(s):  
Paul Borochin ◽  
Yanhui Zhao
Keyword(s):  
Machine Learning ◽  
Implied Volatility ◽  
Economic Value ◽  
Volatility Forecasting

Implied volatility parameterization based on a machine learning polynomial approach

2018 ◽  
Vol 22 (5) ◽  
pp. 1127-1141
Author(s):  
Mostafa Pouralizadeh Jobejarkol ◽  
Abdolrahim Badamchizadeh ◽  
Manuel Morales
Keyword(s):  
Machine Learning ◽  
Implied Volatility

scholarly journals Sound Deposit Insurance Pricing Using a Machine Learning Approach

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 45 ◽  
Author(s):  
Hirbod Assa ◽  
Mostafa Pouralizadeh ◽  
Abdolrahim Badamchizadeh
Keyword(s):  
Machine Learning ◽  
Moral Hazard ◽  
Deposit Insurance ◽  
Implied Volatility ◽  
Calibration Method ◽  
Quadratic Model ◽  
Learning Approach ◽  
Conceptual Issue ◽  
Hazard Risk ◽  
Machine Learning Approach

While the main conceptual issue related to deposit insurances is the moral hazard risk, the main technical issue is inaccurate calibration of the implied volatility. This issue can raise the risk of generating an arbitrage. In this paper, first, we discuss that by imposing the no-moral-hazard risk, the removal of arbitrage is equivalent to removing the static arbitrage. Then, we propose a simple quadratic model to parameterize implied volatility and remove the static arbitrage. The process of removing the static risk is as follows: Using a machine learning approach with a regularized cost function, we update the parameters in such a way that butterfly arbitrage is ruled out and also implementing a calibration method, we make some conditions on the parameters of each time slice to rule out calendar spread arbitrage. Therefore, eliminating the effects of both butterfly and calendar spread arbitrage make the implied volatility surface free of static arbitrage.

scholarly journals ON THE RELATIONSHIP OF IMPLIED, REALIZED AND HISTORICAL VOLATILITY: EVIDENCE FROM NSE EQUITY INDEX OPTIONS

2014 ◽  
Vol 15 (5) ◽  
pp. 915-934 ◽  
Author(s):  
Puja Padhi ◽  
Imlak Shaikh
Keyword(s):  
Instrumental Variable ◽  
Implied Volatility ◽  
Return Volatility ◽  
Volatility Forecasting ◽  
Variable Technique ◽  
Index Options ◽  
Equity Index ◽  
Relationship Of ◽  
Index Option ◽  
The Relationship

This study examines the information content of implied volatility, using the options of the underlying S&P CNX Nifty index. In this study, implied, historical and realized volatilities are calculated using non-overlapping monthly at-the-money samples. The study covers the period from introduction of options on the derivative segment of NSE, June 2001 to May 2011. The results reveal that call and put implied volatility of S&P CNX Nifty index option does contain information about future realized return volatility. This study accounts for the problem of error-in-variable and controls for it by using the instrumental variable technique. In the 2SLS estimation, the Hausman H-statistic shows that call implied volatility is measured with error. Hence, 2SLS coefficients are more consistent than the OLS estimates. Results of this study might prove to be helpful to the volatility traders in volatility forecasting and option pricing.

scholarly journals A Machine Learning Method for the Detection of Brown Core in the Chinese Pear Variety Huangguan Using a MOS-Based E-Nose

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4499 ◽  
Author(s):  
Hao Wei ◽  
Yu Gu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Economic Value ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Learning Machine

The brown core is an internal disorder that significantly affects the palatability and economic value of Chinese pears. In this study, a framework that includes a back-propagation neural network (BPNN) and extreme learning machine (ELM) (BP-ELMNN) was proposed for the detection of brown core in the Chinese pear variety Huangguan. The odor data of pear were collected using a metal oxide semiconductor (MOS) electronic nose (E-nose). Principal component analysis was used to analyze the complexity of the odor emitted by pears with brown cores. The performances of several machine learning algorithms, i.e., radial basis function neural network (RBFNN), BPNN, and ELM, were compared with that of the BP-ELMNN. The experimental results showed that the proposed framework provided the best results for the test samples, with an accuracy of 0.9683, a macro-precision of 0.9688, a macro-recall of 0.9683, and a macro-F1 score of 0.9685. The results demonstrate that the use of machine learning algorithms for the analysis of E-nose data is a feasible and non-destructive method to detect brown core in pears.

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