Efficient Monte Carlo for Discrete Variance Contracts

This paper uses an extensive set of market data of forward swap rates and swaptions covering 3 July 2002 to 21 May 2009 to identify a two-dimensional stochastic volatility process for the level of rates. The process is identified step by step by increasing the requirement of the model and introducing appropriate adjustments. The first part of the paper investigates the smile dynamics of forward swap rates at their setting dates. Comparing the SABR (with different ) and Heston stochastic volatility models informs us what different specifications of the driving SDEs have to offer in terms of reflecting the dynamics of the smile across dates. The outcome of the analysis is that a normal SABR model () satisfactorily passes all tests and seems to provide a good match to the market. In contrast, we find that the Heston model does not. The next step is to seek a model of the forward swap rates (in their own swaption measure) based on only two Brownian motions that enables a specification with common parameters. It turns out that this can be done by extending the SABR model with a time-dependent volatility function and a mean-reverting volatility process. The performance of the extended (SABR with mean-reversion) model is analysed over several historical dates and is shown to be a stable and flexible choice that allows for good calibration across expiries and strikes. Finally, a time-homogeneous candidate stochastic volatility process that can be used as a driver for all swap rates is identified. This candidate process may in future work be used as a building block for a separable stochastic volatility LIBOR market model or a stochastic volatility Markov-functional model.

Reconsidering the continuous time limit of the GARCH(1, 1) process

This paper studies the portfolio optimization of mean-variance utility with state-dependent risk aversion, where the stock asset is driven by a stochastic process. The sub-game perfect Nash equilibrium strategies and the extended Hamilton-Jacobi-Bellman equations have been used to derive the system of non-linear partial differential equations. From the economic point of view, we demonstrate the numerical evaluation of the suggested solution for a special case where the risk aversion rate is proportional to the wealth value. Our results show that the asset driven by the stochastic volatility process is more general and reasonable than the process with a constant volatility.

This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by means of a linear transformation of a Jacobi process. Such models are both analytically tractable and allow for a direct economic interpretation. In particular, we propose two new stochastic volatility models which allow for a stochastic leverage effect: the generalised Heston model and the generalised Barndorff-Nielsen & Shephard model. We investigate the impact of a stochastic leverage effect in the risk neutral world by focusing on implied volatilities generated by option prices derived from our new models. Furthermore, we give a detailed account on statistical properties of the new models.

In considering volatility as a stochastic, the aim of this paper is to estimate the four parameters related to a particular stochastic process named P1 and based on a Wiener–Levy process. We present the methodology to estimate its four parameters. We calibrate this theoretical model P1 to the CAC 40 index real data. In the same time, we test the normality of the random variables related to the two Wiener–Levy processes. The calibration is performed using the implemented aforesaid algorithm. We compare the stochastic process P1 with another process named P2 and to the Heston [Closed form solution for options with stochastic volatility with application to bonds and currency options, Rev. Financ. Stud. 6(2) (1993), pp. 327–343] process named H0 and to two other improved Heston processes named H1 and H2. For the empirical study, the same algorithm is used to calibrate the five processes. The calibration is based on a database including the CAC 40 index daily ‘closing fixing’ values for the time period from 3rd January 2005 to 22nd January 2007. The data are divided into 18 classes relative to 18 different contracts of European calls on the CAC 40 index. As a result, we find that, the normality test of the CAC 40 index is rejected which is in accordance with the previous original works dealing with this problem. For the five volatility processes, the normality test is verified almost for the same contracts. We also find that according to the used data, the process P1 and its equivalent H1 are the best for calibration.

This paper presents an overview of information-based asset pricing. In this approach, an asset is defined by its cash-flow structure. The market is assumed to have access to "partial" information about future cash flows. Each cash flow is determined by a collection of independent market factors called X-factors. The market filtration is generated by a set of information processes, each of which carries information about one of the X-factors, and eventually reveals the X-factor. Each information process has two terms, one of which contains a "signal" about the associated X-factor, and the other of which represents "market noise". The price of an asset is given by the expectation of the discounted cash flows in the risk-neutral measure, conditional on the information provided by the market. When the market noise is modelled by a Brownian bridge one is able to construct explicit formulae for asset prices, as well as semi-analytic expressions for the prices and greeks of options and derivatives. In particular, option price data can be used to determine the information flow-rate parameters implicit in the definitions of the information processes. One consequence of the modelling framework is a specific scheme of stochastic volatility and correlation processes. Instead of imposing a volatility and correlation model upon the dynamics of a set of assets, one is able to deduce the dynamics of the volatilities and correlations of the asset price movements from more primitive assumptions involving the associated cash flows. The paper concludes with an examination of situations involving asymmetric information. We present a simple model for informed traders and show how this can be used as a basis for so-called statistical arbitrage. Finally, we consider the problem of price formation in a heterogeneous market with multiple agents.

In this paper we show that in the lognormal discrete-time stochastic volatility model with predictable conditional expected returns, the conditional expected value of the discounted payo of a European call option is infinite. Our empirical illustration shows that the characteristics of the predictive distributions of the discounted payos, obtained using Monte Carlo methods, do not indicate directly that the expected discounted payos are infinite.

International audience

ABSTRACTWe consider a borderline case: The central limit theorem for a strictly stationary time series with infinite variance but a Gaussian limit. In the i.i.d. case, a well‐known sufficient condition for this central limit theorem is regular variation of the marginal distribution with tail index . In the dependent case, we assume the stronger condition of sequential regular variation of the time series with tail index . We assume that a sample of size from this time series can be split into blocks of size such that as and that the block sums are asymptotically independent. Then we apply classical central limit theory for row‐wise i.i.d. triangular arrays. The necessary and sufficient conditions for such independent block sums will be verified by using large deviation results for the time series. We derive the central limit theorem for ‐dependent sequences, linear processes, stochastic volatility processes and solutions to affine stochastic recurrence equations whose marginal distributions have infinite variance and are regularly varying with tail index .

An Efficient Markov-Chain Monte Carlo Method for the State Space Models with Stochastic Volatility

Time Deformation and Term Structure of Interest Rates

We develop a flexible business cycle indicator that accounts for potential time variation in macroeconomic variables. The coincident economic indicator is based on a multivariate trend cycle decomposition model and is constructed from a moderate set of US macroeconomic time series. In particular, we consider an unobserved components time series model with a common cycle that is shared across different time series but adjusted for phase shift and amplitude. The extracted cycle can be interpreted as a model‐based bandpass filter and is designed to emphasize the business cycle frequencies that are of interest to applied researchers and policymakers. Stochastic volatility processes and mixture distributions for the irregular components and the common cycle disturbances enable us to account for the heteroskedasticity present in the data. Forecasting results are presented for a set of different specifications. Point forecasts from the preferred model indicate a future recession with the uncertainty over the business cycle growing quickly as the forecast horizon increases. Copyright © 2010 John Wiley & Sons, Ltd.

A maximum likelihood approach for the analysis of stochastic volatility models is developed. The method uses a recursive numerical integration procedure that directly calculates the marginal likelihood. Only conventional integration techniques are used, making this approach both flexible and simple. Experimentation shows that the method matches the performance of the best estimation tools currently in use. New stochastic volatility models are introduced and estimated. The model that best fits recent stock-index data is characterized by a highly non-Gaussian stochastic volatility innovation distribution. This model dominates a model that includes an autoregressive conditional heteroscedastic effect in the stochastic volatility process and a model that includes a stochastic volatility effect in the conditional mean.

If a financial time series is presented backward in time, is it possible to see differences? For example, a simple constant volatility random walk is invariant under time reversal. Because financial data are dominated by randomness, the differences, if they exist, are small. Three estimators are presented which are sensitive to the direction of time. When applied to empirical data, they show unambiguously that financial time series are not invariant. The same tests applied to theoretical processes become a powerful selection criterion. The multiscale ARCH structure is able to reproduce the observed asymmetries, whereas the stochastic volatility processes cannot (regardless of the parameter values).

This chapter contains sections titled: Independent Price/Volatility Shocks Dependent Price/Volatility Shocks Stochastic Volatility with Jumps in Price Further Advances Exercises Empirical Project 7