Quantitative Finance

In this paper, we consider a dynamic asset pricing model in an approximate fractional economy to address empirical regularities related to both investor protection and past information. Our newly developed model features not only in terms with a controlling shareholder who diverts a fraction of the output, but also good (or bad) memory in his budget dynamics which can be well-calibrated by a pathwise way from the historical data. We find that poorer investor protection leads to higher stock holdings of controlling holders, lower gross stock returns, lower interest rates, and lower modified stock volatilities if the ownership concentration is sufficiently high. More importantly, by establishing an approximation scheme for good (bad) memory of investors on the historical market information, we conclude that good (bad) memory would increase (decrease) aforementioned dynamics and reveal that good (bad) memory strengthens (weakens) investor protection for minority shareholder when the ownership concentration is sufficiently high, while good (bad) memory inversely weakens (strengthens) investor protection for minority shareholder when the ownership concentration is sufficiently low. Our model's implications are consistent with a number of interesting facts documented in the recent literature.

This paper is a continuation of Akahori-Barsotti-Imamura (2017) and where the authors i) showed that a payment at a random time, which we call timing risk, is decomposed into an integral of static positions of knock-in type barrier options, ii) proposed an iteration of static hedge of a timing risk by regarding the hedging error by a static hedge strategy of Bowie-Carr type with respect to a barrier option as a timing risk, and iii) showed that the error converges to zero by infinitely many times of iteration under a condition on the integrability of a relevant function. Even though many diffusion models including generic 1-dimensional ones satisfy the required condition, a construction of the iterated static hedge that is applicable to any uniformly elliptic diffusions is postponed to the present paper because of its mathematical difficulty. We solve the problem in this paper by relying on the symmetrization, a technique first introduced in Imamura-Ishigaki-Okumura (2014) and generalized in Akahori-Imamura (2014), and also work on parametrix, a classical technique from perturbation theory to construct a fundamental solution of a partial differential equation. Due to a lack of continuity in the diffusion coefficient, however, a careful study of the integrability of the relevant functions is required. The long lines of proof itself could be a contribution to the parametrix analysis.

In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin convolution of functions, and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-diffusion models and stochastic volatility models with jumps. We apply our general results to the Heston model with double exponential jumps, and make a detailed analysis of the asymptotic behavior of the stock price density, the call option pricing function, and the implied volatility in this model. We also obtain similar results for the Heston model with jumps distributed according to the NIG law.

Financial markets based on Lévy processes are typically incomplete and option prices depend on risk attitudes of individual agents. In this context, the notion of utility indifference price has gained popularity in the academic circles. Although theoretically very appealing, this pricing method remains difficult to apply in practice, due to the high computational cost of solving the nonlinear partial integro-differential equation associated to the indifference price. In this work, we develop closed form approximations to exponential utility indifference prices in exponential Lévy models. To this end, we first establish a new non-asymptotic approximation of the indifference price which extends earlier results on small risk aversion asymptotics of this quantity. Next, we use this formula to derive a closed-form approximation of the indifference price by treating the Lévy model as a perturbation of the Black-Scholes model. This extends the methodology introduced in a recent paper for smooth linear functionals of Lévy processes (A. Černý, S. Denkl and J. Kallsen, arXiv:1309.7833) to nonlinear and non-smooth functionals. Our closed formula represents the indifference price as the linear combination of the Black-Scholes price and correction terms which depend on the variance, skewness and kurtosis of the underlying Lévy process, and the derivatives of the Black-Scholes price. As a by-product, we obtain a simple explicit formula for the spread between the buyer's and the seller's indifference price. This formula allows to quantify, in a model-independent fashion, how sensitive a given product is to jump risk in the limit of small jump size.

This paper is concerned with the asymptotics for Greeks of European-style options and the risk-neutral density function calculated under the constant elasticity of variance model. Formulae obtained help financial engineers to construct a perfect hedge with known behaviour and to price any options on financial assets.

Using the large deviation principle (LDP) for a re-scaled fractional Brownian motion B H t where the rate function is defined via the reproducing kernel Hilbert space, we compute small-time asymptotics for a correlated fractional stochastic volatility model of the form d S t = S t σ( Y t )( ρ ¯ d W t +ρd B t ),d Y t =d B H t where σ is α -Hölder continuous for some α∈(0,1] ; in particular, we show that t H− 1 2 log S t satisfies the LDP as t→0 and the model has a well-defined implied volatility smile as t→0 , when the log-moneyness k(t)=x t 1 2 −H . Thus the smile steepens to infinity or flattens to zero depending on whether H∈(0, 1 2 ) or H∈( 1 2 ,1) . We also compute large-time asymptotics for a fractional local-stochastic volatility model of the form: d S t = S β t | Y t | p d W t ,d Y t =d B H t , and we generalize two identities in Matsumoto&Yor05 to show that 1 t 2H log 1 t ∫ t 0 e 2 B H s ds and 1 t 2H (log ∫ t 0 e 2(μs+ B H s ) ds−2μt) converge in law to 2 max 0≤s≤1 B H s and 2 B 1 respectively for H∈(0, 1 2 ) and μ>0 as t→∞ .

The time average of geometric Brownian motion plays a crucial role in the pricing of Asian options in mathematical finance. In this paper we consider the asymptotics of the discrete-time average of a geometric Brownian motion sampled on uniformly spaced times in the limit of a very large number of averaging time steps. We derive almost sure limit, fluctuations, large deviations, and also the asymptotics of the moment generating function of the average. Based on these results, we derive the asymptotics for the price of Asian options with discrete-time averaging in the Black-Scholes model, with both fixed and floating strike.

We study the properties of nonlinear Backward Stochastic Differential Equations (BSDEs) driven by a Brownian motion and a martingale measure associated with a default jump with intensity process ( λ t ) . We give a priori estimates for these equations and prove comparison and strict comparison theorems. These results are generalized to drivers involving a singular process. The special case of a λ -linear driver is studied, leading to a representation of the solution of the associated BSDE in terms of a conditional expectation and an adjoint exponential semi-martingale. We then apply these results to nonlinear pricing of European contingent claims in an imperfect financial market with a totally defaultable risky asset. The case of claims paying dividends is also studied via a singular process.

We propose an elementary model to price European physical delivery swaptions in multicurve setting with a simple exact closed formula. The proposed model is very parsimonious: it is a three-parameter multicurve extension of the two-parameter Hull-White (1990) model. The model allows also to obtain simple formulas for all other plain vanilla Interest Rate derivatives. Calibration issues are discussed in detail.

We derive behavioral finance option pricing formulas consistent with the rational dynamic asset pricing theory. In the existing behavioral finance option pricing formulas, the price process of the representative agent is not a semimartingale, which leads to arbitrage opportunities for the option seller. In the literature on behavioral finance option pricing it is allowed the option buyer and seller to have different views on the instantaneous mean return of the underlying price process, which leads to arbitrage opportunities according to Black (1972). We adjust the behavioral finance option pricing formulas to be consistent with the rational dynamic asset pricing theory, by introducing transaction costs on the velocity of trades which offset the gains from the arbitrage trades.