Recovery of volatility coefficient by linearization

Abstract

We study the problem of reconstruction of the asset price dependent local volatility from market prices of options with different strikes. For a general diffusion process we apply the linearization technique and we conclude that the option price can be obtained as the sum of the Black-Scholes formula and of an explicit functional which is linear in perturbation of volatility. We obtain an integral equation for this functional and we show that under some natural conditions it can be inverted for volatility. We demonstrate the stability of the linearized problem, and we propose a numerical algorithm which is accurate for volatility functions with different properties.     

Model Uncertainty and Option Markets with Heterogeneous Beliefs

This paper provides option pricing and volume implications for an economy with heterogeneous agents who face model uncertainty and have different beliefs on expected returns. Market incompleteness makes options nonredundant, while heterogeneity creates a link between differences in beliefs and option volumes. We solve for both option prices and volumes and test the joint empirical implications using S&P500 index option data. Specifically, we use survey data to build an Index of Dispersion in Beliefs and find that a model that takes information heterogeneity into account can explain the dynamics of option volume and the smile better than can reduced‐form models with stochastic volatility.     

Determinants of S&;P 500 index option returns

We analyze common factors that affect returns on S&P 500 index options and find that 93% of the variation in option returns can be explained by three factors, which respectively account for 87%, 4%, and 2% of the variation in option returns. Furthermore, we test diffusion option pricing models by using mean–variance spanning properties implied in the models. The spanning tests reject one-factor diffusion models, as well as the hypothesis that the underlying asset and an equally weighted option index span options. Our results fail to reject that the underlying asset and an at-the-money option can span out-of-the-money options, but does reject that they span in-the-money options.      

Option Volume and Stock Prices: Evidence on Where Informed Traders Trade

This paper investigates the informational role of transactions volume in options markets. We develop an asymmetric information model in which informed traders may trade in option or equity markets. We show conditions under which informed traders trade options, and we investigate the implications of this for the linkage between markets. Our model predicts an important informational role for the volume of particular types of option trades. We empirically test our model's hypotheses with intraday option data. Our main empirical result is that negative and positive option volumes contain information about future stock prices.     

Fast approximations of bond option prices under CKLS models

A new computational method for approximating prices of zero-coupon bonds and bond option prices under general Chan–Karolyi–Longstaff–Schwartz models is proposed. The pricing partial differential equations are discretized using second-order finite difference approximations and an exponential time integration scheme combined with best rational approximations based on the Carathéodory–Fejér procedure is employed for solving the resulting semi-discrete equations. The algorithm has a linear computational complexity and provides accurate bond and European bond option prices. We give several numerical results which illustrate the computational efficiency of the algorithm and uniform second-order convergence rates for the computed bond and bond option prices.     

Asian options and meromorphic Lévy processes

One method to compute the price of an arithmetic Asian option in a Lévy driven model is based on an exponential functional of the underlying Lévy process: If we know the distribution of the exponential functional, we can calculate the price of the Asian option via the inverse Laplace transform. In this paper, we consider pricing Asian options in a model driven by a general meromorphic Lévy process. We prove that the exponential functional is equal in distribution to an infinite product of independent beta random variables, and its Mellin transform can be expressed as an infinite product of gamma functions. We show that these results lead to an efficient algorithm for computing the price of the Asian option via the inverse Mellin–Laplace transform, and we compare this method with some other techniques.     

Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models

We present a neural network-based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models—including second-generation stochastic volatility models and the rough volatility family—and a range of derivative contracts. Neural networks in this work are used in an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. The form in which information from available data is extracted and used influences network performance: The grid-based algorithm used for calibration is inspired by representing the implied volatility and option prices as a collection of pixels. We highlight how this perspective opens new horizons for quantitative modelling. The calibration bottleneck posed by a slow pricing of derivative contracts is lifted, and stochastic volatility models (classical and rough) can be handled in great generality as the framework also allows taking the forward variance curve as an input. We demonstrate the calibration performance both on simulated and historical data, on different derivative contracts and on a number of example models of increasing complexity, and also showcase some of the potentials of this approach towards model recognition. The algorithm and examples are provided in the Github repository GitHub: NN-StochVol-Calibrations.     

Pricing multiasset equity options: How relevant is the dependence function?

In this paper we test how different choices for the dependence function can affect the prices of a set of multiasset equity options. We conduct the analysis for various 5-dimensional baskets of UK shares, and a wide range of payoffs for the multiasset options, consistent with the instruments traded on the market. We also test the relevance of the dependence specification over both volatile and quiet market scenarios. Interestingly, we find that, in most circumstances, the choice of a dependence structure richer than the standard linear correlation does not seem to affect option prices substantially. However, the dependence function becomes more relevant in particularly volatile market conditions.     

An empirical test of the variance gamma option pricing model

In this paper, we test the three-parameter symmetric variance gamma (SVG) option pricing model and the four-parameter asymmetric variance gamma (AVG) option pricing model empirically. Prices of the Hang Seng Index call options, which are of European style, are used as the data for the empirical test. Since the variance gamma option pricing model is developed for the pricing of European options, the empirical test gives a more conclusive answer than previous papers, which used American option data to the applicability of the VG models. The present study uses a large number of intraday option data, which span over a period of 3 years. Synchronous option and futures data are used throughout the study. Pairwise comparisons between the accuracy of model prices are carried out using both parametric and nonparametric methods.The conclusion is that the VG option pricing model performs marginally better than the Black–Scholes (BS) model. Under the historical approach, the VG models can moderately iron out some of the systematic biases inherent in the BS model. However, under the implied approach, the VG models continue to exhibit predictable biases and its overall performance in pricing and hedging is still far less than desirable.     

Maximum entropy distributions inferred from option portfolios on an asset

We obtain the maximum entropy distribution for an asset from call and digital option prices. A rigorous mathematical proof of its existence and exponential form is given, which can also be applied to legitimise a formal derivation by Buchen and Kelly (J. Financ. Quant. Anal. 31:143–159, 1996). We give a simple and robust algorithm for our method and compare our results to theirs. We present numerical results which show that our approach implies very realistic volatility surfaces even when calibrating only to at-the-money options. Finally, we apply our approach to options on the S&P 500 index.     

Microstructural biases in empirical tests of option pricing models

This paper examines how noise in observed option prices arising from discrete prices and other microstructural frictions affects empirical tests of option pricing models and risk-neutral density estimation. The discrete tick size alone introduces enough noise to make model comparisons difficult, especially for lower-priced stocks. We demonstrate that microstructural noise can lead to incorrect inferences in the univariate diffusion test of Bakshi et al. (Rev Financ Stud 13:549–584, 2000), the transition density diffusion test of Aït-Sahalia (J Financ 57:2075–2112, 2002), and the speed-of-convergence test of Carr and Wu (J Financ 58:2581–2610, 2003). We also show that microstructural noise induces a bias into the implied risk-neutral moment estimators of Bakshi et al. (Rev Financ Stud 16:101–143, 2003). Even in active, liquid option markets, observation error is likely to reduce significantly the power of tests, and in some cases represents an important source of bias.     

Option pricing under short-lived arbitrage: theory and tests

Models in financial economics derived from no-arbitrage assumptions have found great favour among theoreticians and practitioners. We develop a model of option prices where arbitrage is short lived. The arbitrage process is Ornstein–Uhlenbeck with zero mean and rapid adjustment of deviations. We find that arbitrage correlated with the underlying can have sizeable impact on option prices. We use data from five large capitalization firms to test implications of the model. Consistent with the existence of arbitrage, we find that idiosyncratic factors significantly effect arbitrage model parameters.     

ADAPTIVE MESH MODELING AND BARRIER OPTION PRICING UNDER A JUMP‐DIFFUSION PROCESS

The computational burden of numerical barrier option pricing is significant, even prohibitive, for some parameterizations—especially for more realistic models of underlying asset behavior, such as jump diffusions. We extend a binomial jump diffusion pricing algorithm into a trinomial setting and demonstrate how an adaptive mesh may fit into the model. Our result is a barrier option pricing method that employs fewer computational resources, reducing run times substantially. We demonstrate that this extension allows the pricing of options that were previously computationally infeasible and examine the parameterizations in which use of the adaptive mesh is most beneficial.     

Automated trading with boosting and expert weighting

We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning algorithm, an online learning utility, and a risk management overlay. Alternating decision tree (ADT), which is implemented with Logitboost, was chosen as the underlying algorithm. One of the strengths of our approach is that the algorithm is able to select the best combination of rules derived from well-known technical analysis indicators and is also able to select the best parameters of the technical indicators. Additionally, the online learning layer combines the output of several ADTs and suggests a short or long position. Finally, the risk management layer can validate the trading signal when it exceeds a specified non-zero threshold and limit the application of our trading strategy when it is not profitable. We test the expert weighting algorithm with data of 100 randomly selected companies of the S&P 500 index during the period 2003–2005. We find that this algorithm generates abnormal returns during the test period. Our experiments show that the boosting approach is able to improve the predictive capacity when indicators are combined and aggregated as a single predictor. Even more, the combination of indicators of different stocks demonstrated to be adequate in order to reduce the use of computational resources, and still maintain an adequate predictive capacity.     

Model Specification and Risk Premia: Evidence from Futures Options

This paper examines model specification issues and estimates diffusive and jump risk premia using S&P futures option prices from 1987 to 2003. We first develop a time series test to detect the presence of jumps in volatility, and find strong evidence in support of their presence. Next, using the cross section of option prices, we find strong evidence for jumps in prices and modest evidence for jumps in volatility based on model fit. The evidence points toward economically and statistically significant jump risk premia, which are important for understanding option returns.     

On the Robustness of Least-Squares Monte Carlo (LSM) for Pricing American Derivatives

This paper analyses the robustness of Least-Squares Monte Carlo, a technique proposed by Longstaff and Schwartz (2001) for pricing American options. This method is based on least-squares regressions in which the explanatory variables are certain polynomial functions. We analyze the impact of different basis functions on option prices. Numerical results for American put options show that this approach is quite robust to the choice of basis functions. For more complex derivatives, this choice can slightly affect option prices. This revised version was published online in June 2006 with corrections to the Cover Date.     

Information and option pricings

How can one relate stock fluctuations and information-based human activities? We present a model of an incomplete market by adjoining the Black-Scholes exponential Brownian motion model for stock fluctuations with a hidden Markov process, which represents the state of information in the investors' community. The drift and volatility parameters take different values depending on the state of this hidden Markov process. Standard option pricing procedure under this model becomes problematic. Yet, with an additional economic assumption, we provide an explicit closed-form formula for the arbitrage-free price of the European call option. Our model can be discretized via a Skorohod embedding technique. We conclude with an example of a simulation of IBM stock, which shows that, not surprisingly, information does affect the market.     

AN EMPIRICAL TEST OF THE COMMODITY OPTION PRICING MODEL USING GINNIE MAE CALL OPTIONS

This paper tests the ability of Black's commodity option pricing model to provide prices for over-the-counter Ginnie Mae call options, which are not significantly different from actual market prices. The test is applied to a unique data set on option prices and Ginnie Mae forward contracts, furnished by a brokerage house specializing in trading government-backed securities. The model generates prices close to those actually available when trading is reasonably active.     

Informed traders and their market preference: Empirical evidence from prices and volumes of options and stock

The causal structure of price and volume in options and stock markets is examined to investigate whether a preferred market for informed trading exists. The possible trade-off of leverage on one hand and liquidity and transactions costs on the other, and the effect of different market mechanisms, are discussed in this context.We test for cointegration and use the vector error correction (VEC) approach if we find it. Otherwise, causality tests are via the conventional vector autoregression (VAR). We find that volume leads price in both markets, but that option volume leads stock volume, and stock price leads option price. These results differ from previous studies. Potential explanations are discussed.     

Estimation and pricing under long-memory stochastic volatility

We treat the problem of option pricing under a stochastic volatility model that exhibits long-range dependence. We model the price process as a Geometric Brownian Motion with volatility evolving as a fractional Ornstein–Uhlenbeck process. We assume that the model has long-memory, thus the memory parameter H in the volatility is greater than 0.5. Although the price process evolves in continuous time, the reality is that observations can only be collected in discrete time. Using historical stock price information we adapt an interacting particle stochastic filtering algorithm to estimate the stochastic volatility empirical distribution. In order to deal with the pricing problem we construct a multinomial recombining tree using sampled values of the volatility from the stochastic volatility empirical measure. Moreover, we describe how to estimate the parameters of our model, including the long-memory parameter of the fractional Brownian motion that drives the volatility process using an implied method. Finally, we compute option prices on the S&P 500 index and we compare our estimated prices with the market option prices.