Transition Probability Density Function Research Articles

The SABR Model: Explicit Formulae of the Moments of the Forward Prices/Rates Variable and Series Expansions of the Transition Probability Density and of the Option Prices

The SABR stochastic volatility model with β-volatility β ? (0,1) and an absorbing barrier in zero imposed to the forward prices/rates stochastic process is studied. The presence of (possibly) nonzero correlation between the stochastic differentials that appear on the right hand side of the model equations is considered. A series expansion of the transition probability density function of the model in powers of the correlation coefficient of these stochastic differentials is presented. Explicit formulae for the first three terms of this expansion are derived. These formulae are integrals of known integrands. The zero-th order term of the expansion is a new integral formula containing only elementary functions of the transition probability density function of the SABR model when the correlation coefficient is zero. The expansion is deduced from the final value problem for the backward Kolmogorov equation satisfied by the transition probability density function. Each term of the expansion is defined as the solution of a final value problem for a partial differential equation. The integral formulae that give the solutions of these final value problems are based on the Hankel and on the Kontorovich-Lebedev transforms. From the series expansion of the probability density function we deduce the corresponding expansions of the European call and put option prices. Moreover we deduce closed form formulae for the moments of the forward prices/rates variable. The moment formulae obtained do not involve integrals or series expansions and are expressed using only elementary functions. The option pricing formulae are used to study synthetic and real data. In particular we study a time series (of real data) of futures prices of the EUR/USD currency's exchange rate and of the corresponding option prices. The website: http://www.econ.univpm.it/recchioni/finance/w18 contains material including animations, an interactive application and an app that helps the understanding of the paper. A more general reference to the work of the authors and of their coauthors in mathematical finance is the website: http://www.econ.univpm.it/recchioni/finance.

An analytically tractable multi-asset stochastic volatility model

A multi-asset stochastic volatility model is presented. This is based on the Heston stochastic volatility model. It has the advantage of being sufficiently flexible to interpret the individual asset dynamics as well as the interdependencies among them, while still being analytically tractable. A formula for the joint transition probability density function of the stochastic process implicitly defined by the model is deduced. This formula is expressed as a one dimensional integral of an explicitly known function and allows us to derive elementary formulae for the conditional mean and variance of the asset price log-returns. A calibration procedure is proposed and tested on simulated data.

Pricing credit default swaps with bilateral value adjustments

The paper studies the problem of computing adjustments for bilateral counterparty risk for a standard CDS in a three-factor first-passage time default risk model. Extending the existing literature that gives analytical expression for the transition probability density function (or Green’s function) for two-dimensional Brownian motions absorbed at the boundaries in the positive quadrant, this paper gives a semi-analytical expression for Green’s function for three-dimensional Brownian motions absorbed at first exit time from the positive octant. This is done by separating the problem into a radial and an angular part, of which the latter is universal and depends only on the correlation matrix. These mathematical results are then used to provide semi-analytical expressions for bilateral CVA/DVA of a credit default swap. An example of market data is analysed in detail and it is shown that these value adjustments can be surprisingly large.

H∞Control for Discrete-Time Markov Jump Systems With Uncertain Transition Probabilities

In this technical note, the H∞ control problem for a class of discrete-time Markov jump systems (MJSs) with uncertain transition probabilities (TPs) is investigated. The uncertain information of transition probabilities is quantized by Gaussian transition probability density function (pdf). In light of the proposed descriptions, the MJSs with Gaussian PDF of TPs cover the systems with precisely known and partially known TPs as two special cases. Sufficient conditions for the existence of H∞ controller of the underlying systems are derived in term of linear matrix inequalities. A numerical example is presented to illustrate the effectiveness and potential of the developed theoretical results.

Ornstein–Uhlenbeck process, Cauchy process, and Ornstein–Uhlenbeck–Cauchy process on a circle

By a simple mathematical method, we obtain the transition probability density functions of the Ornstein–Uhlenbeck process, Cauchy process, and Ornstein–Uhlenbeck–Cauchy process on a circle.

The representation of American options prices under stochastic volatility and jump-diffusion dynamics

This paper considers the problem of pricing American options when the dynamics of the underlying are driven by both stochastic volatility following a square-root process as used by Heston [Rev. Financial Stud., 1993, 6, 327–343], and by a Poisson jump process as introduced by Merton [J. Financial Econ., 1976, 3, 125–144]. Probability arguments are invoked to find a representation of the solution in terms of expectations over the joint distribution of the underlying process. A combination of Fourier transform in the log stock price and Laplace transform in the volatility is then applied to find the transition probability density function of the underlying process. It turns out that the price is given by an integral dependent upon the early exercise surface, for which a corresponding integral equation is obtained. The solution generalizes in an intuitive way the structure of the solution to the corresponding European option pricing problem obtained by Scott [Math. Finance, 1997, 7(4), 413–426], but here in the case of a call option and constant interest rates.

Monotonic continuous-time random walks with drift and stochastic reset events

In this paper we consider a stochastic process that may experience random reset events which suddenly bring the system to the starting value and analyze the relevant statistical magnitudes. We focus our attention on monotonic continuous-time random walks with a constant drift: The process increases between the reset events, either by the effect of the random jumps, or by the action of the deterministic drift. As a result of all these combined factors interesting properties emerge, like the existence (for any drift strength) of a stationary transition probability density function, or the faculty of the model to reproduce power-law-like behavior. General formulas for two extreme statistics, the survival probability, and the mean exit time are also derived. To corroborate in an independent way the results of the paper, Monte Carlo methods were used. These numerical estimations are in full agreement with the analytical predictions.

Some Explicit Formulae for the Hull and White Stochastic Volatility Model

An explicit formula for the transition probability density function of the Hull and White stochastic volatility model in presence of nonzero correlation between the stochastic differentials of the Wiener processes on the right hand side of the model equations is presented. This formula gives the transition probability density function as a two dimensional integral of an explicitly known integrand. Previously an explicit formula for this probability density function was known only in the case of zero correlation. In the case of nonzero correlation from the formula for the transition probability density function we deduce formulae (expressed by integrals) for the price of European call and put options and closed form formulae (that do not involve integrals) for the moments of the asset price logarithm. These formulae are based on recent results on the Whittaker functions [1] and generalize similar formulae for the SABR and multiscale SABR models [2]. Using the option pricing formulae derived and the least squares method a calibration problem for the Hull and White model is formulated and solved numerically. The calibration problem uses as data a set of option prices. Experiments with real data are presented. The real data studied are those belonging to a time series of the USA S&P 500 index and of the prices of its European call and put options. The quality of the model and of the calibration procedure is established comparing the forecast option prices obtained using the calibrated model with the option prices actually observed in the financial market. The website: http://www.econ.univpm.it/recchioni/finance/w17 contains some auxiliary material including animations and interactive applications that helps the understanding of this paper. More general references to the work of the authors and of their coauthors in mathematical finance are available in the website: http://www.econ.univpm.it/recchioni/finance.

An Empirical Study of Option Prices under the Hybrid Brownian Motion Model

In this paper, we mainly discuss an empirical study of option prices under the hybrid Brownian motion model developed by [1]. In a specific case of parameters, we have a simple transition probability density function that has a fattailed feature as time passes. We show some empirical evidences that the feature of the model reflects the real market price movements in Japanese stock market. Furthermore, we make a performance comparison between the hybrid model and the BS model using Nikkei 225 call options. In general our results show that the hybrid model is slightly better than the BS model.

Some Explicitly Solvable SABR and Multiscale SABR Models: Option Pricing and Calibration

A multiscale SABR model that describes the dynamics of forward prices/rates is presented. New closed form formulae for the transition probability density functions of the normal and lognormal SABR and multiscale SABR models and for the prices of the corresponding European call and put options are deduced. The technique used to obtain these formulae is rather general and can be used to study other stochastic volatility models. A calibration problem for these models is formulated and solved. Numerical experiments with real data are presented.

On intentional introduction of stiffness nonlinearities for energy harvesting under white Gaussian excitations

A significant body of the open literature on vibratory energy harvesting is currently focused on the concept of purposeful inclusion of stiffness nonlinearities for broadband transduction. When compared to their linear resonant counterparts, nonlinear energy harvesters have a wider steady-state frequency bandwidth, leading to the idea that they can be utilized to improve performance especially in random and non-stationary vibratory environments. To further investigate this common belief, this paper studies the response of vibratory energy harvesters to white Gaussian excitations. Both mono- and bi-stable piezoelectric Duffing-type harvesters are considered. The Fokker–Plank–Kolmogorov equation governing the evolution of the system’s transition probability density function is formulated and used to generate the moment differential equations governing the response statistics. The moment equations are then closed using a fourth-order cumulant-neglect closure scheme and the relevant steady-state response statistics are obtained. It is demonstrated that the energy harvester’s time constant ratio, i.e., the ratio between the nominal period of the mechanical subsystem and the time constant of the harvesting circuit, plays a critical role in characterizing the performance of nonlinear harvesters in a random environment. When the time constant ratio is large, stiffness-type nonlinearities have very little influence on the voltage response. In such a case, no matter how the potential function of the harvester is altered, it does not affect the average output power of the device. When the time constant ratio is small, the influence of the nonlinearity on the voltage output becomes more prevalent. In this case, a Duffing-type mono-stable harvester can never outperform its linear counterpart. A bi-stable harvester, on the other hand, can outperform a linear harvester only when it is designed with the proper potential energy function based on the known noise intensity of the excitation. Such conclusions hold for harvesters with nonlinearities appearing in the restoring force.

Efficient path integral solution of Fokker–Planck equation: response, bifurcation and periodicity of nonlinear systems

Response of nonlinear systems subjected to harmonic, parametric, and random excitation is of importance in the field of structural dynamics. The transitional probability density function (PDF) of the random response of nonlinear systems under white or coloured noise excitation (delta-correlated) is governed by both the forward Fokker–Planck (FP) and the backward Kolmogorov equations. This article presents a new approach for efficient numerical implementation of the path integral (PI) method in the solution of the FP equation for some nonlinear systems subjected to white noise, parametric and combined harmonic and white noise excitations. The modified PI method is based on a non-Gaussian transition PDF and the Gauss–Legendre integration scheme. The steady state PDF, jump phenomenon, noise induced state changes of PDF are studied by the modified PI method. Effects of white noise intensity, amplitude and frequency of harmonic excitation and the level of nonlinearity on stochastic jump and bifurcation behaviours of a hardening Duffing oscillator are also investigated.

On the therapy effect for a stochastic growth Gompertz-type model

We consider a diffusion model based on a generalized Gompertz deterministic growth in which carrying capacity depends on the initial size of the population. The drift of the resulting process is then modified by introducing a time-dependent function, called “therapy”, in order to model the effect of an exogenous factor. The transition probability density function and the related moments for the proposed process are obtained. A study of the influence of the therapy on several characteristics of the model is performed. The first-passage-time problem through time-dependent boundaries is also analyzed. Finally, an application to real data concerning a rabbit population subject to particular therapies is presented.

Detection, modelling and estimation of non-linear trends by using a non-homogeneous Vasicek stochastic diffusion. Application to CO2 emissions in Morocco

This paper proposes a new stochastic model, based on a Vasicek non-homogeneous diffusion process, in which the non-linear trend coefficient (drift) depends on deterministic functions that describe the dynamic evolution of certain exogenous variables. After studying its probabilistic characteristics, and in particular the transition probability density and trend function, the associated stochastic inference based on discrete sampling in time is established using maximum likelihood methodology. This model is applied to detect, estimate and model the non-linear trend present in data corresponding to CO2 emissions in Morocco. Energy and financial variables that affect the behaviour of this trend are also detected, and substantial improvement provided by this non-homogeneous model with respect to its corresponding homogeneous version, is confirmed.

Numerical solutions of Fokker–Planck equation of nonlinear systems subjected to random and harmonic excitations

A new approach for an efficient numerical implementation of the path integral (PI) method based on non-Gaussian transition probability density function (PDF) and the Gauss–Legendre integration scheme is developed. This modified PI method is used to solve the Fokker–Planck (FP) equation and to study the nature of the stochastic and chaotic response of the nonlinear systems. The steady state PDF, periodicity, jump phenomenon, noise induced changes in joint PDF of the states are studied by the modified PI method. A computationally efficient higher order, finite difference (FD) technique is derived for the solution of higher-dimensional FP equation. A two degree of freedom nonlinear system having Coulomb damping with a variable friction coefficient subjected to Gaussian white noise excitation is considered as an example which can represent a bladed disk assembly of turbo-machinery blades. Effects of normal force and viscous damping on the mean square response are investigated.

First Passage Time Moments of Jump-Diffusions with Markovian Switching

Using an integral equation associated with generalized backward Kolmogorov's equation for the transition probability density function, recurrence relations are derived for the moments of the time of first exit of jump-diffusions with Markovian switching. The results are used to find the expectation of first passage time of some financial models.

A new stochastic approach to multi-compartment pharmacokinetic models: probability of traveling route and distribution of residence time in linear and nonlinear systems

Drug kinetics in human has been studied from both deterministic and stochastic perspectives. However, little research has been done to systematically determine the probability for a drug molecule to follow a specific traveling route. Recently a method was developed to estimate this probability and the probability density function of residence time in linear systems. In this paper, we provide a rigorous proof of the main results of the previous paper and extend the method to nonlinear multi-compartment systems. A novel concept of compartment expansion is introduced to facilitate the development of our method. This formulation resolves computational difficulties associated with nonlinear systems, allowing for direct estimation of the probability intensity coefficients, and subsequently the transition probability and probability density function of the residence time. With such expansion of the methodology, it becomes both practical and feasible to apply it in the real-world drug development where drug disposition patterns are often nonlinear. The method can be used to estimate drug exposure at any site of interest, thus may help us to gain better understanding about the impact of drug exposure on efficacy and safety.

On the densities of certain bounded diffusion processes

A comprehensive outline is presented for obtaining the Laplace transforms of the transition probability density functions and of the first-passage-time densities for one-dimensional time-homogeneous diffusion processes in the presence of absorbing and/or reflecting boundaries. In particular, the Laplace transform of the transition probability density function in the presence of pairs of reflecting boundaries are explicitly obtained. Symmetric diffusion processes are then specifically considered and explicit closed-form relations are then obtained for the hyperbolic diffusion process in the presence of absorbing and/or reflecting boundaries. The special cases of the Brownian motion and of the Hongler process are finally analyzed.

Stochastic ship roll motion via path integral method

The response of ship roll oscillation under random ice impulsive loads modeled by Poisson arrival process is very important in studying the safety of ships navigation in cold regions. Under both external and parametric random excitations the evolution of the probability density function of roll motion is evaluated using the path integral (PI) approach. The PI method relies on the Chapman-Kolmogorov equation, which governs the response transition probability density functions at two close intervals of time. Once the response probability density function at an early close time is specified, its value at later close time can be evaluated. The PI method is first demonstrated via simple dynamical models and then applied for ship roll dynamics under random impulsive white noise excitation.

Stochastic ship roll motion via path integral method

The response of ship roll oscillation under random ice impulsive loads modeled by Poisson arrival process is very important in studying the safety of ships navigation in cold regions. Under both external and parametric random excitations the evolution of the probability density function of roll motion is evaluated using the path integral (PI) approach. The PI method relies on the Chapman-Kolmogorov equation, which governs the response transition probability density functions at two close intervals of time. Once the response probability density function at an early close time is specified, its value at later close time can be evaluated. The PI method is first demonstrated via simple dynamical models and then applied for ship roll dynamics under random impulsive white noise excitation.