scholarly journals An inverse problem of determining the implied volatility in option pricing

2008 ◽  
Vol 340 (1) ◽  
pp. 16-31 ◽  
Author(s):  
Zui-Cha Deng ◽  
Jian-Ning Yu ◽  
Liu Yang
2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Shou-Lei Wang ◽  
Yu-Fei Yang ◽  
Yu-Hua Zeng

The estimation of implied volatility is a typical PDE inverse problem. In this paper, we propose theTV-L1model for identifying the implied volatility. The optimal volatility function is found by minimizing the cost functional measuring the discrepancy. The gradient is computed via the adjoint method which provides us with an exact value of the gradient needed for the minimization procedure. We use the limited memory quasi-Newton algorithm (L-BFGS) to find the optimal and numerical examples shows the effectiveness of the presented method.


2016 ◽  
Vol 19 (02) ◽  
pp. 1650014 ◽  
Author(s):  
INDRANIL SENGUPTA

In this paper, a class of generalized Barndorff-Nielsen and Shephard (BN–S) models is investigated from the viewpoint of derivative asset analysis. Incompleteness of this type of markets is studied in terms of equivalent martingale measures (EMM). Variance process is studied in details for the case of Inverse-Gaussian distribution. Various structure preserving subclasses of EMMs are derived. The model is then effectively used for pricing European style options and fitting implied volatility smiles.


2002 ◽  
Vol 05 (06) ◽  
pp. 599-618 ◽  
Author(s):  
YUJI YAMADA ◽  
JAMES A. PRIMBS

In this paper, we propose a numerical option pricing method based on an arbitrarily given stock distribution. We first formulate a European call option pricing problem as an optimal hedging problem by using a lattice based incomplete market model. A dynamic programming technique is then applied to solve the mean square optimal hedging problem for the discrete time multi-period case by assigning suitable probabilities on the lattice, where the underlying stock price distribution is derived directly from empirical stock price data which may possess "heavy tails". We show that these probabilities are obtained from a network flow optimization which can be solved efficiently by quadratic programming. A computational complexity analysis demonstrates that the number of iterations for dynamic programming and the number of parameters in the network flow optimization are both of square order with respect to the number of periods. Numerical experiments illustrate that our methodology generates the implied volatility smile.


2018 ◽  
Vol 10 (6) ◽  
pp. 108
Author(s):  
Yao Elikem Ayekple ◽  
Charles Kofi Tetteh ◽  
Prince Kwaku Fefemwole

Using market covered European call option prices, the Independence Metropolis-Hastings Sampler algorithm for estimating Implied volatility in option pricing was proposed. This algorithm has an acceptance criteria which facilitate accurate approximation of this volatility from an independent path in the Black Scholes Model, from a set of finite data observation from the stock market. Assuming the underlying asset indeed follow the geometric brownian motion, inverted version of the Black Scholes model was used to approximate this Implied Volatility which was not directly seen in the real market: for which the BS model assumes the volatility to be a constant. Moreover, it is demonstrated that, the Implied Volatility from the options market tends to overstate or understate the actual expectation of the market. In addition, a 3-month market Covered European call option data, from 30 different stock companies was acquired from Optionistic.Com, which was used to estimate the Implied volatility. This accurately approximate the actual expectation of the market with low standard errors ranging between 0.0035 to 0.0275.


2019 ◽  
Vol 18 (1) ◽  
pp. 134-156 ◽  
Author(s):  
François Aubert ◽  
Jeff J. Wang ◽  
Gary Grudnitski

Purpose The purpose of this paper is to introduce analyst estimates and option pricing-based variables in modeling material accounting misstatements. Design/methodology/approach The paper uses a logistic regression model to analyze a comprehensive sample of AAER and non-AAER firms listed in the USA. Findings By applying a cross-sectional, sequence of time-series logistic regression models, the authors find better identifiers of ex ante risk of fraud than prediction models based on an inspection of abnormal accruals. These identifiers include the managed earnings (ME) component of a firm and the change in a firm’s option contracts’ implied volatility (IV) prior to an earnings announcement. Practical implications The empirical findings contribute to an understanding of earnings manipulation (fraud) and should be of value to auditors and regulatory bodies interested in identifying financial statement fraud, particularly the Securities and Exchange Commission, which has been improving its accounting quality model (AQM or Robocop) fraud detection tool for many years. The results contribute substantially to enhancing the current accounting literature by introducing two non-accrual-based measures that significantly enhance the predictive power of an accrual-based accounting misstatement prediction model. Originality/value This paper radically departs from relying on the assumption that the clearest and easiest pathway to detect fraud reporting ex ante is through an examination of accruals. Instead, the authors use a richer source of information about the possibility of a firm’s misstatement of its financial accounting numbers, namely, analyst estimates of ex post earnings and the IV from exchange-traded option contracts.


Author(s):  
Przemyslaw S. Stilger ◽  
Ngoc Quynh Anh Nguyen ◽  
Tri Minh Nguyen

This paper examines the empirical performance of four stochastic volatility option pricing models: Heston, Heston[Formula: see text], Bates and Heston–Hull–White. To compare these models, we use individual stock options data from January 1996 to August 2014. The comparison is made with respect to pricing and hedging performance, implied volatility surface and risk-neutral return distribution characteristics, as well as performance across industries and time. We find that the Heston model outperforms the other models in terms of in-sample pricing, whereas Heston[Formula: see text] model outperforms the other models in terms of out-of-sample hedging. This suggests that taking jumps or stochastic interest rates into account does not improve the model performance after accounting for stochastic volatility. We also find that the model performance deteriorates during the crises as well as when the implied volatility surface is steep in the maturity or strike dimension.


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