Option Market Data Research Articles

Stable local volatility function calibration using spline kernel

We propose an optimization formulation using the l 1 norm to ensure accuracy and stability in calibrating a local volatility function for option pricing. Using a regularization parameter, the proposed objective function balances calibration accuracy with model complexity. Motivated by the support vector machine learning, the unknown local volatility function is represented by a spline kernel function and the model complexity is controlled by minimizing the 1-norm of the kernel coefficient vector. In the context of support vector regression for function estimation based on a finite set of observations, this corresponds to minimizing the number of support vectors for predictability. We illustrate the ability of the proposed approach to reconstruct the local volatility function in a synthetic market. In addition, based on S&P 500 market index option data, we demonstrate that the calibrated local volatility surface is simple and resembles the observed implied volatility surface in shape. Stability is illustrated by calibrating local volatility functions using market option data from different dates.

Pricing inflation products with stochastic volatility and stochastic interest rates

We consider a Heston type inflation model in combination with a Hull–White model for nominal and real interest rates, in which all the correlations can be non-zero. Due to the presence of the Heston dynamics our derived inflation model is able to capture the implied volatility skew/smile, which is present in the inflation option market data. We derive an efficient approximate semi-closed pricing formula for two types of inflation dependent options: index and year-on-year inflation options. The derived pricing formulas allow for an efficient calibration of the inflation model. We also illustrate our approach using a real-life pension fund example, where the Heston Hull–White model is used to determine the value of conditional future indexations.

Performance Analysis of Economic VaR Estimation using Risk Neutral Probability Distributions

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

Implied liquidity: towards stochastic liquidity modelling and liquidity trading

In this paper the authors introduce the new concept of implied liquidity based on the recent developed two-way price theory (conic finance). Implied liquidity isolates and quantifies liquidity risk in financial markets. It is shown on real market option data on the major US indices how liquidity dried up in the troubled year end of 2008. These investigations open the door to stochastic liquidity modelling, liquidity derivatives and liquidity trading.

Implied Liquidity - Towards Stochastic Liquidity Modeling and Liquidity Trading

In this paper the authors introduce the new concept of implied liquidity on the basis of the recent developed two-way price theory (conic finance). Implied liquidity isolates and quantifies in a fundamental way liquidity risk in financial markets. It is shown on real market option data on the major US indices how liquidity dried up in the troubled year end of 2008. These investigations open the door to stochastic liquidity modeling, liquidity derivatives and liquidity trading.

Implied Volatility Surface Estimation Using Transductive Gaussian Fields Regression

Implied volatility estimation is one of the fundamental tasks for asset pricing and risk management. In this paper, we investigate the applicability of semi-supervised regression techniques to estimate an implied volatility surface from the real market option data. Specifically, we employ a transductive Gaussian field regression method since it is able to predict a distribution of the implied volatilities for unlabelled data using only partially labeled data. We've conducted simulation on S&P 500 index data before and after the global financial crisis with discussions of the observed empirical properties of the method.

Modeling Implied Volatility Surfaces Using Two-dimensional Cubic Spline with Estimated Grid Points

In this paper, we introduce the implied volatility from Black-Scholes model and suggest a model for constructing implied volatility surfaces by using the two-dimensional cubic (bi-cubic) spline. In order to utilize a spline method, we acquire grid (knot) points. To this end, we first extract implied volatility curves weighted by trading contracts from market option data and calculate grid points from the extracted curves. At this time, we consider several conditions to avoid arbitrage opportunity. Then, we establish an implied volatility surface, making use of the two-dimensional cubic spline method with previously estimated grid points. The method is shown to satisfy several properties of the implied volatility surface (smile, skew, and flattening) as well as avoid the arbitrage opportunity caused by simple match with market data. To show the merits of our proposed method, we conduct simulations on market data of S&P500 index European options with reasonable and acceptable results.

A stochastic approximation algorithm for option pricing model calibration with a switchable market

This paper is concerned with option pricing under a regime-switching model. The switching process takes two different modes, and the underlying stock price evolves in accordance with the two modes dictated by a continuous-time, finite-state Markov chain. At any given instance, the price follows either a geometric Brownian motion model or a mean-reversion model, depending on its market mode. Stochastic approximation/optimization algorithms are developed for model calibration. Convergence of the algorithm is proved; rate of convergence is also provided. Option market data are used to predict the future market mode.

Asymptotic option pricing under pure-jump Lévy processes via nonlinear regression

When the underlying asset price process follows a Lévy process, the market becomes incomplete, in which the option pricing can be a complicated problem. This paper proposes a method of asymptotic option pricing when the underlying asset price process follows a pure-jump Lévy process. We express the option price as the expected value of the discounted payoff and expand it at the Black–Scholes price assuming that the price process converges weakly to the Black–Scholes model. The price can be approximated by a formula with 4 parameters, which can easily be estimated using option prices observed in the market. The proposed price explains the market option data better than the Black–Scholes price in real data application with KOSPI 200.

Calibrating parametric exponential Lévy models to option market data by incorporating statistical moments priors

We investigate a parametric method for calibrating European option pricing using the state-of-art exponential Lévy models. We propose a derivative-free calibration method constrained by four observable statistical moments (mean, variance, skewness and kurtosis) from underlying time series to conquer the ill-posed inverse problem and to incorporate priors on observable statistical moments. We present a numerical implementation scheme for calibrating the exponential Lévy models and show that it can resolve the instability of the inverse problems empirically and can produce good calibration results. In particular, we apply our approach to real market data sets of S&P 500 call options with significantly better performance.

General equilibrium pricing of options with habit formation and event risks

This paper proposes a general equilibrium model that explains the pricing of the S&P 500 index options. The central ingredients are a peso component in the consumption growth rate and the time-varying risk aversion induced by habit formation which amplifies consumption shocks. The amplifying effect generates the excess volatility and a large jump-risk premium which combine to produce a pronounced volatility smirk for index options. The time-varying volatility and jump-risk premiums explain the observed state-dependent smirk patterns. Besides volatility smirks, the model has a variety of other implications which are broadly consistent with the aggregate stock and option market data.

Predicting a distribution of implied volatilities for option pricing

In this paper, we propose a method that predicts a distribution of the implied volatility functions and that provides confidence intervals for the option prices from it. The proposed method, based on a Bayesian approach, employs a Bayesian kernel machine, so-called Gaussian process regression. To verify the performance of the proposed method, we conducted simulations on some model-generated option prices data and real option market data. The simulation results show that the proposed method performs well with practically meaningful option ranges as well as overcomes the problem of containing negative prices in their predicted confidence intervals by the previous works.

Nonlinear Regression for an Asymptotic Option Price

This paper approaches the problem of option pricing in an incomplete market, where the underlying asset price process follows a compound Poisson model. We assume that the price process follows a compound Poisson model under an equivalent martingale measure and it converges weakly to the Black-Scholes model. First, we express the option price as the expectation of the discounted payoff and expand it at the Black-Scholes price to obtain a pricing formula with three unknown parameters. Then we estimate those parameters using the market option data. This method can use the option data on the same stock with different expiration dates and different strike prices.

The Effectiveness of Foreign Exchange Intervention in Emerging Market Countries: Evidence from the Czech Koruna

We survey the literature on the efficacy of foreign exchange market intervention in emerging market countries, emphasising the differences with the literature on industrial countries. We then use official statistics on central bank intervention by the Czech National Bank in conjunction with options market data, to study the impact of intervention during 2001-02. We find that central bank intervention had some (weakly) statistically significant impact on the spot rate and the risk reversal but that this impact was small. We do not find evidence that intervention had an influence on short-term exchange rate volatility. We also find that, in our sample period, Czech authorities appeared to intervene mainly in response to an acceleration of the speed of koruna appreciation.

Implied non-recombining trees and calibration for the volatility smile

In this paper we capture the implied distribution from option market data using a non-recombining (binary) tree, allowing the local volatility to be a function of the underlying asset and of time. The problem under consideration is a non-convex optimization problem with linear constraints. We elaborate on the initial guess for the volatility term structure and use nonlinear constrained optimization to minimize the least squares error function on market prices. The proposed model can accommodate European options with single maturities and, with minor modifications, options with multiple maturities. It can provide a market-consistent tree for option replication with transaction costs (often this requires a non-recombining tree) and can help pricing of exotic and Over The Counter (OTC) options. We test our model using options data for the FTSE 100 index obtained from LIFFE. The results strongly support our modelling approach.

Recovering Probabilistic Information from Options Prices and the Underlying

This paper examines a variety of methods for extracting implied probability distributions from option prices and the underlying. The paper first explores non-parametric procedures for reconstructing densities directly from options market data. I then consider local volatility functions, both through implied volatility trees and volatility interpolation. I then turn to alternative specifications of the stochastic process for the underlying. I estimate a mixture of log normals model, apply it to exchange rate data, and illustrate how to conduct forecast comparisons. I finally turn to the estimation of jump risk by extracting bipower variation.

The effectiveness of foreign exchange intervention in emerging market countries: Evidence from the Czech koruna

We survey the literature on the efficacy of foreign exchange market intervention in emerging market countries, emphasising the differences with the literature on industrial countries. We then use official statistics on central bank intervention by the Czech National Bank in conjunction with options market data to study the impact of intervention during 2001–2002. We find that central bank intervention had some (weakly) statistically significant impact on the spot rate and the risk reversal but that this impact was small. We do not find evidence that intervention had an influence on short-term exchange rate volatility. We also find that, in our sample period, Czech authorities appeared to intervene mainly in response to an acceleration of the speed of koruna appreciation.

Implied Binomial Trees and Calibration for the Volatility Smile

In this paper we capture the implied distribution from option market data using non-recombining binomial trees allowing the local volatility to be a function of the underlying asset and of time. We elaborate on the initial guess for the volatility term structure, and use non-linear constrained optimization to minimize the least square error function on market prices. The proposed model can accommodate European options with single maturities and, with minor modifications, options with multiple maturities. It can provide a market-consistent tree for option replication with transaction costs (often this requires a non-recombining tree) and can help pricing of exotic and Over The Counter (OTC) options. We test our model using options data of the FTSE-100 index obtained from LIFFE. The results strongly support our modelling approach

Foreign exchange market intervention and expectations: The yen/dollar exchange rate

Using press reports and official statistics on central bank intervention in conjunction with options market data, we find that central bank intervention had no statistically significant systematic impact on the mean or higher moments of the exchange rate during 1993–2000. We also find that Japanese authorities appeared to intervene mainly in response to deviations of the exchange rate from some implicit target levels and to a rise in market uncertainty. U.S. monetary authorities intervened only in cooperation with the Japanese authorities.

On the risk‐adjusted pricing‐methodology‐based valuationof vanilla options and explanation of the volatility smile

We analyse a model for pricing derivative securities in the presence of both transaction costs as well as the risk from a volatile portfolio. The model is based on the Black‐Scholes parabolic PDE in which transaction costs are described following the Hoggard, Whalley, and Wilmott approach. The risk from a volatile portfolio is described by the variance of the synthesized portfolio. Transaction costs as well as the volatile portfolio risk depend on the time lag between two consecutive transactions. Minimizing their sum yields the optimal length of the hedge interval. In this model, prices of vanilla options can be computed from a solution to a fully nonlinear parabolic equation in which a diffusion coefficient representing volatility nonlinearly depends on the solution itself giving rise to explaining the volatility smile analytically. We derive a robust numerical scheme for solving the governing equation and perform extensive numerical testing of the model and compare the results to real option market data. Implied risk and volatility are introduced and computed for large option datasets. We discuss how they can be used in qualitative and quantitative analysis of option market data.