Closed-form Characteristic Function Research Articles

Stability Tests and Analysis of a Low-Pressure Natural Circulation Loop with Flashing Instability

Natural circulation is employed in new designs of light water reactors to enhance passive safety by maintaining flow and heat removal without pumps. Under low-pressure and low-flow-rate conditions, natural circulation is susceptible to two-phase instabilities leading to undesirable flow oscillations and operational difficulties. Flashing instability is one of the most widely reported low-pressure natural circulation instabilities, related to saturated vaporization triggered by a hydrostatic pressure drop in an adiabatic riser above a heated section. While existing studies have reported flashing instability experiments, modeling, and simulations including successes in matching numerical results and experimental data, solid yet clear analytical explanations for many of its qualitative features are still rare. To enhance the physical understanding beyond stability boundary prediction, the current work develops, validates, and analyzes a linear stability model of flashing instability. This model adopts a one-dimensional Drift-Flux Model simplified by physical assumptions and approximations, and it includes optional component models to match an actual facility for validation. Stability tests are performed on a 5-m-tall natural circulation loop, providing comprehensive benchmark data covering stability boundaries, one-dimensional transient signals, and periodic mean waveforms from local measurements. Validation confirms acceptable predictions of steady states, stability boundaries, and oscillation periods. The tractable model formulation leads to a closed-form characteristic function facilitating analytical manipulations and physical interpretations, based on which dominant pressure drop responses to inlet flow rate are extracted. The major instability mechanism is identified as a strong response of the two-phase driving force to the inlet flow rate that is delayed by enthalpy transportation through a long single-phase distance and can become an overwhelmingly destabilizing positive feedback under low-frequency perturbations. Experimentally reported qualitative features, including stability changes, timescale relations, and oscillation patterns, are analytically predicted and physically explained with clarity. In general, this study enriches experimental resources of flashing instability with a comprehensive dataset and provides a simple yet realistic analytical basis for physically understanding flashing instability beyond predicting stability boundaries.

Deposit and Withdrawal Dynamics: A Data-Based Mutually-Exciting Stochastic Model

This paper proposes a novel self- and mutual-exciting stochastic model to capture two essential features underlying a general type of discrete-time event data motivated by the practice: the dependence on the past event arrivals and associated sizes (i.e., self-exciting) and the behavioral interdependence between multiple activities (i.e., mutual-exciting). Despite the technical challenges in capturing these two features under a general discrete-time framework, we are able to fully characterize the probability distribution for the proposed model (i.e., the closed-form characteristic functions), theoretically quantify customer performance measures, and establish efficient maximum likelihood estimation. To illustrate the applicability of our model and validate its performance, we calibrate it with a customer deposit and withdrawal data set from one leading online money market fund. We analytically quantify the churn probability and expected activity level of customers as an illustration of performance measures and then compare our model with classic time-series and machine-learning models and show that our model can achieve high prediction accuracy. The theoretical tractability and predictive accuracy of the proposed framework enable us to build optimization models to improve firm performance, and we illustrate one application through a personalized interest-rate optimization problem. On a broader note, our model framework is generally applicable to characterize any discrete-time event data with self and mutual excitation in nature and to inform optimal policies for decision-makers.

A multivariate 4/2 stochastic covariance model: properties and applications to portfolio decisions

This paper introduces a multivariate 4/2 stochastic covariance process generalizing the one-dimensional counterparts presented in [Grasselli, M., The 4/2 stochastic volatility model: A unified approach for the Heston and the 3/2 model. Math. Finance, 2017, 27(4), 1013–1034]. Our construction permits stochastic correlation not only among stocks but also among volatilities, also known as co-volatility movements, both driven by more convenient 4/2 stochastic structures. The parametrization is flexible enough to separate these types of correlation, permitting their individual study. Conditions for proper changes of measure and closed-form characteristic functions under risk-neutral and historical measures are provided, allowing for applications of the model to risk management and derivative pricing. We apply the model to an expected utility theory problem in incomplete markets. Our analysis leads to closed-form solutions for the optimal allocation and value function. Conditions are provided for well-defined solutions together with a verification theorem. Our numerical analysis highlights and separates the impact of key statistics on equity portfolio decisions, in particular, volatility, correlation, and co-volatility movements, with the latter being the least important in an incomplete market.

Pricing holder-extendable call options with mean-reverting stochastic volatility

Options with extendable features have many applications in finance and these provide the motivation for this study. The pricing of extendable options when the underlying asset follows a geometric Brownian motion with constant volatility has appeared in the literature. In this paper, we consider holder-extendable call options when the underlying asset follows a mean-reverting stochastic volatility. The option price is expressed in integral forms which have known closed-form characteristic functions. We price these options using a fast Fourier transform, a finite difference method and Monte Carlo simulation, and we determine the efficiency and accuracy of the Fourier method in pricing holder-extendable call options for Heston parameters calibrated from the subprime crisis. We show that the fast Fourier transform reduces the computational time required to produce a range of holder-extendable call option prices by at least an order of magnitude. Numerical results also demonstrate that when the Heston correlation is negative, the Black–Scholes model under-prices in-the-money and over-prices out-of-the-money holder-extendable call options compared with the Heston model, which is analogous to the behaviour for vanilla calls.
 
 doi:10.1017/S1446181119000142

PRICING HOLDER-EXTENDABLE CALL OPTIONS WITH MEAN-REVERTING STOCHASTIC VOLATILITY

Options with extendable features have many applications in finance and these provide the motivation for this study. The pricing of extendable options when the underlying asset follows a geometric Brownian motion with constant volatility has appeared in the literature. In this paper, we consider holder-extendable call options when the underlying asset follows a mean-reverting stochastic volatility. The option price is expressed in integral forms which have known closed-form characteristic functions. We price these options using a fast Fourier transform, a finite difference method and Monte Carlo simulation, and we determine the efficiency and accuracy of the Fourier method in pricing holder-extendable call options for Heston parameters calibrated from the subprime crisis. We show that the fast Fourier transform reduces the computational time required to produce a range of holder-extendable call option prices by at least an order of magnitude. Numerical results also demonstrate that when the Heston correlation is negative, the Black–Scholes model under-prices in-the-money and over-prices out-of-the-money holder-extendable call options compared with the Heston model, which is analogous to the behaviour for vanilla calls.

Non-Affine Stochastic Volatility With Seasonal Trends

We propose a new stochastic volatility model for pricing options on assets that exhibit seasonal trends in volatility. Such assets are prevalent among commodities, with futures on grains and energy being an example. The model is based on the 3/2 stochastic volatility model, but includes a cyclical long-run volatility component. The model yields a closed-form characteristic function which can be used to rapidly calibrate the model to benchmark options. We test the model on market data and show that the model proposed here allows for significantly better empirical fit to option prices on corn and wheat futures than the unmodified 3/2 model.

On the Distribution Function of the Generalized Beckmann Random Variable and Its Applications in Communications

The Beckmann distribution has a wide range of applications in radio-frequency communications, free-space optical (FSO) communications, and underwater wireless optical communications (UWOC). However, the cumulative distribution function (cdf) of the Beckmann random variable (RV) does not have a closed-form expression, which makes it challenging to derive analytical solutions for the outage probability of systems involving Beckmann RVs. In this paper, we study the generalized Beckmann distribution, which includes the Beckmann, Rayleigh, Rician, Nakagami- $m$ , Hoyt, $\kappa $ - $\mu $ , $\eta $ - $\mu $ , single-sided Gaussian, and the Beaulieu-Xie distributions as special cases. Three approaches are proposed to estimate the cdf of the generalized Beckmann distribution, including closed-form upper and lower cdf bounds, single-fold integration based on the closed-form characteristic function, and a left-tail cdf approximation. We compare the three approaches in terms of the ranges of applications and the computation time complexity. Based on the new cdf estimation techniques, one can efficiently evaluate the outage probabilities of pointing-error-limited FSO systems, UWOC systems, and maximum-ratio combining over arbitrarily correlated generalized Beckmann channels.

VALUATION OF VULNERABLE OPTIONS UNDER THE DOUBLE EXPONENTIAL JUMP MODEL WITH STOCHASTIC VOLATILITY

In this paper, we extend the framework of Klein [15] [Journal of Banking & Finance 20: 1211–1229] to a general model under the double exponential jump model with stochastic volatility on the underlying asset and the assets of the counterparty. Firstly, we derive the closed-form characteristic functions for this dynamic. Using the Fourier-cosine expansion technique, we get numerical solutions for vulnerable European put options based on the characteristic functions. The inverse fast Fourier transform method provides a fast numerical algorithm for the twice-exercisable vulnerable Bermuda put options. By virtue of the modified Geske and Johnson method, we obtain an approximate pricing formula of vulnerable American put options. Numerical simulations are made for investigating the impact of stochastic volatility on vulnerable options.

A HYBRID ASYMPTOTIC EXPANSION SCHEME: AN APPLICATION TO LONG-TERM CURRENCY OPTIONS

This paper develops a general approximation scheme, henceforth called a hybrid asymptotic expansion scheme for valuation of multi-factor European path-independent derivatives. Specifically, we apply it to pricing long-term currency options under a market model of interest rates and a general diffusion stochastic volatility model with jumps of spot exchange rates. Our scheme is very effective for a type of models in which there exist correlations among all the factors whose dynamics are not necessarily affine nor even Markovian so long as the randomness is generated by Brownian motions. It can also handle models that include jump components under an assumption of their independence of the other random variables when the characteristic functions for the jump parts can be analytically obtained. An asymptotic expansion approach provides a closed-form approximation formula for their values, which can be calculated in a moment and thus can be used for calibration or for an explicit approximation of Greeks of options. Moreover, this scheme develops Fourier transform method with an asymptotic expansion as well as with closed-form characteristic functions obtainable in parts of a model, extending the method proposed by Takehara and Takahashi (2008) to be applicable to a general class of models. It also introduces a characteristic-function-based Monte Carlo simulation method with the asymptotic expansion as a control variable in order to make full use of analytical approximations by the asymptotic expansion and of the closed-form characteristic functions. Finally, a series of numerical examples shows the effectiveness of our scheme.

Option Pricing with Piecewise-Constant Parameters, Discrete Jumps and Regime-Switching

In this paper, I address systematically how to enhance the most existing option models with piecewise-constant parameters, and how to derive the corresponding closed-form characteristic function under the risk-neutral measure. As long as the characteristic function with piecewise-constant parameters is analytical known, the pricing formula for a European call is then given by inverse transform of the derived characteristic function. The method developed here is based on iterated expectations by applying the tower rule, and is applicable for all existing option models with closed-form characteristic function. Particularly, the most popular option pricing models are extended with time-dependent parameters, including stochastic volatility models, jump-diffusion models and Levy pure jump models. Additionally, to demonstrate the potential power of iterated expectation, I develop a discrete jump model that allows for jumps at discrete time points. A concrete use of this discrete jump model is a discrete dividend model. Furthermore, a regime-switched option pricing model can also be easily dealt with where the underlying asset follows different types of stochastic process in the course of time. For instance, an asset can be governed first by stochastic volatility dynamics, and then by Levy pure jump dynamics. Finally, two stochastic volatility models of Heston (1993) as well as of Schobel and Zhu (1999) are examined in details. The time-dependent variants of these two models are calibrated to FX option markets, then compared to the conventional stochastic volatility models in terms of market data fitting and product pricing.

A multivariate Lévy process model with linear correlation

In this paper, we develop a multivariate risk-neutral Lévy process model and discuss its applicability in the context of the volatility smile of multiple assets. Our formulation is based upon a linear combination of independent univariate Lévy processes and can easily be calibrated to a set of one-dimensional marginal distributions and a given linear correlation matrix. We derive conditions for our formulation and the associated calibration procedure to be well-defined and provide some examples associated with particular Lévy processes permitting a closed-form characteristic function. Numerical results of the option premiums on three currencies are presented to illustrate the effectiveness of our formulation with different linear correlation structures.

A Hybrid Asymptotic Expansion Scheme: An Application to Long-Term Currency Options

This paper develops a general approximation scheme, henceforth called a hybrid asymptotic expansion scheme for the valuation of multi-factor European path-independent derivatives. Specifically, we apply it to pricing long-term currency options under a market model of interest rates and a general diffusion stochastic volatility model with jumps of spot exchange rates.Our scheme is very effective for a type of models in which there exist correlations among all the factors whose dynamics are not necessarily affine nor even Markovian so long as the randomness is generated by Brownian motions. It can also handle models that include jump components under an assumption of their independence of the other random variables when the characteristic functions for the jump parts can be analytically obtained.An asymptotic expansion approach provides a closed-form approximation formula for their values, which can be calculated in a moment and thus can be used for calibration or for an explicit approximation of Greeks of options. Moreover, this scheme develops Fourier transform method with an asymptotic expansion as well as with closed-form characteristic functions obtainable in parts of a model, extending the method proposed by Takehara and Takahashi [2008] to be applicable to a general class of models.It also introduces a characteristic-function-based Monte Carlo simulation method with the asymptotic expansion as a control variable in order to make full use of analytical approximations by the asymptotic expansion and of the closed-form characteristic functions. Finally, a series of numerical examples shows the effectiveness of our scheme.

Why the Rotation Count Algorithm Works

The characteristic functions of many affine jump-diffusion models, such as Heston’s stochastic volatility model and all of its extensions, involve multivalued functions such as the complex logarithm. If we restrict the logarithm to its principal branch, as is done in most software packages, the characteristic function can become discontinuous, leading to completely wrong option prices if options are priced by Fourier inversion. In this paper we prove under non-restrictive conditions on the parameters that the rotation count algorithm of Kahl and Jackel chooses the correct branch of the complex logarithm. Under the same restrictions we prove that in an alternative formulation of the characteristic function the principal branch is the correct one. Seen as this formulation is easier to implement and numerically more stable than Heston’s formulation, it should be the preferred one. The remainder of this paper shows how complex discontinuities can be avoided in the Schobel-Zhu model and the exact simulation algorithm of the Heston model, recently proposed by Broadie and Kaya. Finally, we show that Matytsin’s SVJJ model has a closed-form characteristic function, though the complex discontinuities that arise there due to the branch switching of the exponential integral cannot be avoided under all circumstances.

Accurate closed-form approximations to Ricean sum distributions and densities

The statistical distribution of a sum of Ricean random variables occurs extensively in wireless communications. A closed-form expression does not exist for the sum distribution and, furthermore, the Ricean random variable does not have a closed-form characteristic function. For these reasons, it is somewhat difficult to numerically calculate the sum distribution. Highly accurate, closed-form approximations for the sum distributions and densities are presented. These approximations are valid for a wide range of argument values, Rice factors and number of summands.

Bootstrap‐assisted Goodness‐of‐fit Tests in the Frequency Domain

The validity of a stationary time series model may be measured by the goodness of fit of the spectral distribution function. Anderson (Technical Report 27, 1991; Technical Report 309, 1995; Stanford University) has worked out the closed‐form characteristic functions for the Cramer–von Mises criterion for general linear processes, under the condition that all values of the parameters are specified. The asymptotic approach is not easily implemented and usually requires a case by case analysis. In this paper we propose a bootstrap goodness‐of‐fit test in the frequency domain. By properly resampling the residuals, we can consistently estimate the p values for many weakly dependent semiparametric models with unspecified parameter values. This is the content of the main theorem that we try to explain. A group of simulations is conducted, showing consistent significance level and good power. The special tests are applied to the lynx data and reveal structure unexplained by the AR(1) model fitted by Tong (J. R. Stat. Soc. A 140 (1977), 432–36). A possible generalization with application to financial data analysis is also discussed.

Theoretical and experimental analysis of clipping-induced impulsive noise in AM-VSB subcarrier multiplexed lightwave systems

A new theoretical and experimental analysis of clipping-induced impulsive noise in directly modulated AM-VSB/digital hybrid lightwave systems is presented. The theoretical model is based on asymptotic clipping and shot noise theories, and the probability density of combined Gaussian and impulsive noise is obtained by performing a numerical inverse Fourier transform on the closed-form characteristic function. Similar analyzes in previously published work have resulted in closed-form expressions for the BER in hybrid AM-VSB/QAM systems, but these models, which do not use the numerical inverse Fourier transform, have either required the use of an experimentally measured clipping rate using a spectrum analyzer or have had limited BER prediction accuracy. Application of our theory to experimental results for the BER in a hybrid AM-VSB/64 QAM system demonstrates that the performance of the model if better than the models of previously published works. Theoretical analysis of a similar hybrid system employing the N-VSB HDTV digital modulation format rather than QAM indicates that an improvement in BER is achieved if 8-VSB at double the symbol rate is used instead of 64 QAM. We also present the first experimental characterization of the amplitude and time distribution of (1) low-frequency bandpass and (2) down-converted impulsive noise and we show that the time interval between clipping events has a high probability of being 167 /spl mu/s, 1.5 /spl mu/s, or 4 ms in an NTSC system with unmodulated carriers. These results have potential impact on forward error correcting codes used to ameliorate the degrading effects of clipping.