Market Prices Of Options Research Articles

A Bayesian estimation of exponential Lévy models for implied volatility smile

In this paper, a Bayesian estimation method for calibrating the parameter set of exponential Lévy models with the prior information and predicting distributions of option prices, is proposed. As real option prices are noisy, it is appropriate for option pricing models to provide confidence intervals for option prices. An algorithm for calibrating the parameter sets of exponential Lévy models is presented, and the performance of the proposed method is verified by comparing model-generated option prices with real market option prices such as KOSPI 200 Index option prices. Simulation results show that the proposed method calibrates the parameter sets effectively, overcoming the ill-posed inverse problem of model calibration and enabling the construction of reasonable predicted distributions of option prices.

Correcting the Bias in the Practitioner Black-Scholes Method

We address a number of technical problems with the popular Practitioner Black-Scholes (PBS) method for valuing options. The method amounts to a two-stage procedure in which fitted values of implied volatilities (IV) from a linear regression are plugged into the Black-Scholes formula to obtain predicted option prices. Firstly we ensure that the prediction from stage one is positive by using log-linear regression. Secondly, we correct the bias that results from the transformation applied to the fitted values (i.e., the Black-Scholes formula) being a highly non-linear function of implied volatility. We apply the smearing technique in order to correct this bias. An alternative means of implementing the PBS approach is to use the market option price as the dependent variable and estimate the parameters of the IV equation by the method of non-linear least squares (NLLS). A problem we identify with this method is one of model incoherency: the IV equation that is estimated does not correspond to the set of option prices used to estimate it. We use the Monte Carlo method to verify that (1) standard PBS gives biased option values, both in-sample and out-of-sample; (2) using standard (log-linear) PBS with smearing almost completely eliminates the bias; (3) NLLS gives biased option values, but the bias is less severe than with standard PBS. We are led to conclude that, of the range of possible approaches to implementing PBS, log-linear PBS with smearing is preferred on the basis that it is the only approach that results in valuations with negligible bias.

Conic Option Pricing

Lower and upper prudent valuations in two price economies obtained on sufficiently distorting physical probabilities for returns to horizons matching option maturities straddle the market prices of options. Market prices are then modeled as geometric averages of the extremal valuations. Upper valuation weights sensitive to moneyness are calibrated to market data for 203 underliers over seven years. Hedges are designed to minimize capital defined as the upper lower valuation spread. The hedging criterion is immune to cost of hedge issues, works on both aspects of super and sub replication and provides a dispersion measure with parametric control over hedge aggresiveness.

Model-Free Stochastic Collocation for an Arbitrage-Free Implied Volatility, Part II

This paper explores the stochastic collocation technique, applied on a monotonic spline, as an arbitrage-free and model-free interpolation of implied volatilities. We explore various spline formulations, including B-spline representations. We explain how to calibrate the different representations against market option prices, detail how to smooth out the market quotes, and choose a proper initial guess. The technique is then applied to concrete market options and the stability of the different approaches is analyzed. Finally, we consider a challenging example where convex spline interpolations lead to oscillations in the implied volatility and compare the spline collocation results with those obtained through arbitrage-free interpolation technique of Andreasen and Huge.

Ensuring More Is Better: On the Simultaneous Application of Stock and Options Data to Estimate the GARCH Options Pricing Model

The most common approach in fitting option pricing models to market data is first to make an assumption about the underlying asset’s returns process and then develop an option pricing model for that process that is tested against market option prices. The returns process is estimated from historical data, option values are computed, and then compared against a cross-section of prices from the options market. Unfortunately, this often does not work well, and plainly it is inefficient in its use of the data. However, efforts to combine returns data from the asset market and prices from the options market into a single estimation have also not had much success. In this article, Chang, Cheng, and Fuh propose a new procedure to combine data from both markets in the estimation, in which options are assumed to be subject to random pricing noise relative to model values. The additional slack gives the estimator better ability to match prices in both markets. The article contrasts the performance of the full model approach with an approach that only uses stock prices or options prices to fit an option pricing model based on an underlying GARCH process. The value of the combined approach is demonstrated both theoretically as an asymptotic result in the model and also in a Monte Carlo simulation.

The Second Partial Derivative of Option Price with Respect to the Strike: A Historical Reminiscence

An option’s market price reflects the risk-neutral probability that it will end up in the money. Research has been increasing in recent years that shows how, given a set of market prices for options covering a range of strikes, an estimate of the entire risk-neutral probability distribution can be obtained. The technique is based on the fact that the second partial derivative of the option pricing function with respect to the strike price is the risk-neutral density (discounted from option expiration). This idea is generally attributed to Breeden and Litzenberger’s 1978 paper. In this article, Zimmermann shows that the connection between the second partial derivative of the option price with respect to the exercise price and risk-neutral probabilities has a much longer history, including a little-known 1974 note by Fischer Black, and going all the way back to Bachelier in 1900.

Model Independent Multi-Asset Volatility Smile with Empirical Confirmation

This paper presents a new formalism to price European options in all asset classes that fits the market data remarkably well. We use a model-independent representation of European Option prices as path integrals over all of the underlying asset price from inception to maturity. The no arbitrage condition of the path integral representation generates a consistency condition upon the risk neutral probability density function to maturity. When solving for the risk neutral density function to maturity that satisfies the consistency condition, the European volatility smile is obtained. As an illustration of the formalism, we show that when the underlying asset price changes at constant volatility (standard deviation), the option price satisfies the Black-Scholes-Merton model without the pre-assumption that the underlying asset price satisfies a Brownian motion. More generally, without any pre-assumption on the underlying asset’s stochastic process, the risk neutral density function for a given expiration date is determined by the option prices of three strikes with the same expiration. We offer a parameterization of the volatility smile with a closed-form expression using pre-calculated tables. Comprehensive empirical evidence is provided, confirming that the new model accurately matches the market option prices in equities, commodities, currencies and interest rates. In addition, a formalism to obtain the conditional probability transfer density function from the vanilla options term structure is suggested.

A Universal Model for Pricing All Options

This paper presents a new option pricing approach for all underlying assets that precisely fits the market data. We obtain the probability density function of the underlying asset without any external parameter. The density function for a given expiration date is uniquely determined by the prices of three options with different strikes but the same expiration. Our approach allows for the calculation of path dependent options as we are able to calculate the contingent density function of the underlying asset between subsequent expiries as a function of the underlying asset price. The new model accurately matches market option prices in all asset classes (currencies, interest rates, equities and commodities) including exotic options.

Product Market Competition and Option Prices

Most firms face some form of competition in product markets. The degree of competition a firm faces feeds back into its cash flows and affects the values of the securities it issues. Through its effects on stock prices, product market competition affects the prices of options on equity and naturally leads to an inverse relationship between equity returns and volatility, generating a negative volatility skew in option prices. Using a large sample of U.S. equity options, we provide empirical support for this finding and demonstrate the importance of accounting for product market competition when explaining the cross-sectional variation in option skew.

Regime-switching stochastic volatility model: estimation and calibration to VIX options

ABSTRACTWe develop and implement a method for maximum likelihood estimation of a regime-switching stochastic volatility model. Our model uses a continuous time stochastic process for the stock dynamics with the instantaneous variance driven by a Cox–Ingersoll–Ross process and each parameter modulated by a hidden Markov chain. We propose an extension of the EM algorithm through the Baum–Welch implementation to estimate our model and filter the hidden state of the Markov chain while using the VIX index to invert the latent volatility state. Using Monte Carlo simulations, we test the convergence of our algorithm and compare it with an approximate likelihood procedure where the volatility state is replaced by the VIX index. We found that our method is more accurate than the approximate procedure. Then, we apply Fourier methods to derive a semi-analytical expression of S&P500 and VIX option prices, which we calibrate to market data. We show that the model is sufficiently rich to encapsulate important features of the joint dynamics of the stock and the volatility and to consistently fit option market prices.

Variance Premium and Implied Volatility in a Low-Liquidity Option Market

We propose an implied volatility index for Brazil (called IVol-BR), based on daily market prices of options over IBOVESPA - an option market with relatively low liquidity and low number of option strikes. Our methodology combines usual international methodology used in high-liquidity markets with adjustments that take into account the low liquidity in Brazilian option market. We do a number of empirical tests to validate the IVol-BR. First, we show that the IVol-BR has significant predictive power over future volatility of equity returns not contained in traditional volatility forecasting variables. Second, we decompose the squared IVol-BR into (i) the expected variance of stock returns and (ii) the equity variance premium. This decomposition is of interest since the equity variance premium directly relates to the representative investor risk-aversion. Finally, we show empirically that higher risk-aversion is accompanied with higher expected returns, confirming the theory that high risk-aversion should be compensated by higher returns.

A Sentiment-Based Model for the Bitcoin: Theory, Estimation and Option Pricing

In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website this http URL.

Recovery of time-dependent volatility in option pricing model☆☆The work described in this paper was partially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region (Project No. CityU 101112) and grants from the NNSF of China (Nos. 11261029, 11461039), and NSF grants DMS 10-08902 and 15-14886 and by Emylou Keith and Betty Dutcher Distinguished

In this paper we investigate an inverse problem of determining the time-dependent volatility from observed market prices of options with different strikes. Due to the non linearity and sparsity of observations, an analytical solution to the problem is generally not available. Numerical approximation is also difficult to obtain using most of the existing numerical algorithms. Based on our recent theoretical results, we apply the linearisation technique to convert the problem into an inverse source problem from which recovery of the unknown volatility function can be achieved. Two kinds of strategies, namely, the integral equation method and the Landweber iterations, are adopted to obtain the stable numerical solution to the inverse problem. Both theoretical analysis and numerical examples confirm that the proposed approaches are effective.

Risk Neutral versus Real-World Distribution on Publicly Listed Bank Corporations

In this study, we examine different quantitative methods to recover the risk neutral distribution function associated to the prices of option on bank shares. This is useful for a wide range of applications, such as determining the implicit State guarantee that systemic financial institutions benefit from the State, or looking if the market prices correctly the fat tails of financial returns. We assess the performance of these techniques in various ways, including comparing market option prices and historical Values-at-Risk to option prices and Value-at-Risk implied by the estimated risk neutral distribution. We find that, contrary to what is expected for a market composed of risk averse investors, the latter is much smaller than the one obtained from real data. We discuss our results with respect to the theory of risk neutral valuation and investor risk preference.

Optimization of covered call strategies

We present a risk-return optimization framework to select strike prices and quantities of call options to sell in a covered call strategy. Covered calls of a general form are considered where call options with different strike prices can be sold simultaneously. Tractable formulations are developed using variance, semivariance, VaR, and CVaR as risk measures. Sample expected return and sample risk are formulated by simulating the price of the underlying asset. We use option market price data to perform the optimization and analyze the structure of optimal covered call portfolios using the S&P 500 as the underlying. The optimal solution is shown to be directly linked to the options’ call risk premiums. We find that from a risk-return perspective it is often optimal to simultaneously sell call options of different strike prices for all risk measures considered.

What Goes into Risk Neutral Volatility? Empirical Estimates of Risk and Subjective Risk Preferences

Under Black-Scholes (BS) assumptions, empirical volatility and risk neutral volatility are given by a single parameter, which captures all aspects of risk. Inverting the model to extract implied volatility from an option's market price gives the market's forecast of future empirical volatility. But real world returns are not lognormal, volatility is stochastic, and arbitrage is limited, so option prices embed both the market's estimate of the empirical returns distribution and also investors' risk attitudes, including possibly distinct preferences over different volatility-related aspects of the returns process, such as tail risk. All of these influences are reflected in the risk neutral density (RND), which can be extracted from option prices without requiring restrictive assumptions from a pricing model.We compute daily RNDs for the SP others reflect risk attitudes, such as the level of investor confidence and the size of recent volatility forecast errors.

Estimating discrete dividends by no-arbitrage

We develop and showcase a simple no-arbitrage methodology for the valuation of discrete dividend payments, based exclusively on market prices of options via the put-call parity. Our approach integrates all available option market data and simultaneously calibrates the market-implied discount curve, thus ensuring consistency across spot and derivative markets. We illustrate our method using stocks of European blue-chip companies.

The Implied Convexity of VIX Futures

An important component of theoretical CBOE Volatility Index (VIX) futures prices is a term correcting for the negative convexity of the square root function by subtracting from the forward-starting variance swap rate an estimate of the future volatility of VIX futures prices. In the same fashion that an index option’s traditional implied volatility can be viewed as an aggregate market consensus of future realized volatility, this convexity value can be viewed as an aggregate market consensus of future volatility of volatility. This article examines the predictive properties and features of this convexity adjustment needed to value VIX futures prices by extracting it from the relationship between observed VIX futures prices and the corresponding spot option market prices used to compute the forward-starting variance swap rate. The authors find that implied convexity levels can indeed be used to forecast the future volatility of VIX futures prices, even though implied convexity consistently underestimates future realized VIX futures variance. They also show that implied convexity can at times violate strict theoretical conditions by being negative, although we are able to rule out arbitrage opportunities. Finally, they examine the properties of this implied convexity adjustment, both as a time series and with respect to various market volatility factors with which they find positive and statistically significant relations.

Calibration of stochastic volatility models: A Tikhonov regularization approach

We aim to calibrate stochastic volatility models from option prices. We develop a Tikhonov regularization approach with an efficient numerical algorithm to recover the risk neutral drift term of the volatility (or variance) process. In contrast to most existing literature, we do not assume that the drift term has any special structure. As such, our algorithm applies to calibration of general stochastic volatility models. An extensive numerical analysis is presented to demonstrate the efficiency of our approach. Interestingly, our empirical study reveals that the risk neutral variance processes recovered from market prices of options on S&P 500 index and EUR/USD exchange rate are indeed linearly mean-reverting.

Neutrosophic Information Fusion Applied to Financial Market

In this paper we basically make two propositions ~ firstly Q non-linear feedback process that is primarily fuelled by mass cognitive dissonance could generate systematic deviations between the theoretical and market prices of long-term options, and secondly such deviations are best reconciled in terms of neutrosophic rather than rule-based reasoning, especially in the context of the users of automated trading systems designed to generate trading signals based on analysis of information from conflicting sources.