Dynamic Option Pricing Model Based on the Realized-GARCH-NIG Approach

The article deals with dynamic pricing (DP) models methodological aspects, how to model DP in different spheres of services/industries, which factors have a significant effect on the DP formation and what are the benefits of DP application in practice. This paper discusses the concept of dynamic pricing, the dynamic pricing models principles, problems, main methodological assumptions and methods in retail. Therefore the aim of this paper is to analyse the dynamic pricing models principles and problems and to represent the results of DP forming factors evaluated by AHP method.

Due to development of the batteries and it's charging technologies, public incentives and growing criticism on dense air pollution, the electrical vehicles (EVs) gaining more popularity as compared to traditional fuel vehicles. In this paper, the multi-objective optimal scheduling of Electric Vehicle batteries in Battery Swapping Station (BSS) is aimed in order to optimize the number of EV batteries taken from battery stock. Further, degradation cost of batteries and electricity charging cost of batteries also considered for optimal scheduling of EV batteries in BSS. The dynamic electricity pricing model is considered to avoid new peaks of battery charging demand in BSS. EV battery swapping demand in each hour of the day is solved by using a multi-objective Shuffled Frog Leaping Algorithm (SFLA). The simulation results present the effectiveness of multi-objective optimization and dynamic pricing model.

A Novel Simple but Empirically Consistent Model for Stock Price and Option Pricing

Many of the companies started moving towards cloud computing because of its characteristics like pay as you go, easy to use, scalable, multi-tenant, secure, etc. There are many cloud providers providing cloud services over the internet. There are number of pricing policies for the user. These pricing policies can be categorised as static and dynamic pricing. This paper represents the enhancement and evaluation of the proposed dynamic resource pricing model for cloud resources based on the consumer behaviour. For model evaluation, one tail two sample t-test has been used. The results show the model will be helpful to the cloud provider for profit maximisation.

We propose an analytically tractable local volatility model for asset price dynamics leading to volatility smile/skew and fatter-tailed probability distribution. The proposed local volatility model is based on stochastic process of hyperbolic-sine type. We derive the transition probability density function for hyperbolic-sine model and justify that this function has delta function terminal condition at initial time. We compare the probability density functions in Black-Scholes and hyperbolic-sine models to demonstrate that the probability distribution in hyperbolic sine model has some features of fat-tailed distributions. Risk neutral valuation technique is applied to find explicit valuation formula for European call option price in hyperbolic-sine model. In hyperbolic-sine model European call option is more valuable than an identical option in Black-Scholes model for ATM options. We verify that in hyperbolic-sine model Breeden-Litzenberger formula (relation between European call option price and probability density function) holds true. We also examine that Dupire formula correctly recovers volatility function from European call option price in hyperbolic-sine model.

In this paper, we provide a new dynamic asset pricing model for plain vanilla options and we discuss its ability to produce minimum mispricing errors on equity option books. Given the historical measure, the dynamics of assets being modeled by Garch-type models with generalized hyperbolic innovations and the pricing kernel is an exponential affine function of the state variables, we show that the risk-neutral distribution is unique and again implies a generalized hyperbolic dynamics with changed parameters. We provide an empirical test for our pricing methodology on two data sets of options, respectively written on the French CAC 40 and the American SP 500. Then, using our theoretical result associated with Monte Carlo simulations, we compare this approach with natural competitors in order to test its efficiency. More generally, our empirical investigations analyse the ability of specific parametric innovations to reproduce market prices in the context of an exponential affine specification of the stochastic discount factor.

In this article, we explore the current‐state of literature on how dynamic pricing models incorporate consumer reference‐price effects in developing more informed dynamic pricing strategies for products that have repeated consumer interactions. We first examine the literature on how consumer demand is impacted by reference‐price effects and how consumers form reference prices through their purchase experience. We then explore, in detail, the dynamic pricing models with consumer reference‐price effects in major studies, in order to highlight their key findings and insights. We conclude our review by pointing out research gaps and future research directions on dynamic pricing area in the presence of consumer reference‐price effects.

We advance the literature on dynamic oligopoly pricing models in the presence of switching costs by additionally modeling the strategic pricing role of the retailer within the distribution channel. In doing this, we study the relative dynamic pricing implications of how current retail and wholesale prices for a brand must optimally take into account past and future demand, respectively, for the brand. Using scanner data from the cola market, we find that while the retailer exploits the benefit of inertial demand by appropriately increasing the retail profit margin, the cost of investing is borne entirely by the manufacturers. We use simulation studies to show how the retailer will lose its ability to leverage the benefits of inertial demand as consumers become more price sensitive. We also show that when inertia of the more price-sensitive customer segment increases, the aggregate welfare of consumers, the retailer, and manufacturers may increase. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2649 . This paper was accepted by J. Miguel Villas-Boas, marketing.

This article analyzes a dynamic pricing and advertising model for the sale of perishable products under constant absolute risk aversion. We consider a time-dependent version of Gallego and van Ryzin’s dynamic pricing model with exponential demand and include isoelastic advertising effects as well as marginal unit costs. We derive closed-form expressions of the optimal risk-averse pricing and advertising policies of the value function and of the certainty equivalent. The formulas provide insight into the (complex) interplay between risk-sensitive pricing and advertising decisions. Moreover, to evaluate the optimally controlled sales process over time we propose efficient simulation techniques. These are used to analyze the characteristics of different degrees of risk aversion, particularly the concentration of the profit distribution and the impact on the expected evolution of price and advertising rates.

E-commerce has transformed the way firms develop their pricing strategies, producing shift away from fixed pricing to dynamic pricing. In this paper, we use two different Estimation of distribution algorithms (EDAs), a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm for solving two different dynamic pricing models. Promising results were obtained for an EDA confirming its suitability for resource management in the proposed model. Our analysis gives interesting insights into the application of population based optimization techniques for dynamic pricing.

This paper studies the reference effect on dynamic pricing in revenue management for cases with limited capacity and stochastic demand. We first present a single‐period fixed pricing (FP) model in finite horizon with fixed capacity and stochastic demand, and show that there is a unique optimal solution. The model is then extended to a discrete‐time dynamic pricing (DP) model as a benchmark case. We subsequently propose a DP model with reference effect (DPR), investigate the properties of the revenue function, and present a computational scheme to compute the dynamic optimal price. Numerical experiments are conducted to exhibit how the reference effect may influence the initial price, pricing trend, price dispersion, and expected revenue, with the FP, DP, and DPR policies in the same environment of a fixed capacity facing stochastic demand. We also present, through numerical examples, a comparison between deterministic demand and stochastic demand scenarios under reference effect, and with a fixed initial price versus a variable initial price.

A rational pricing model for demand response programs in contemporary distribution systems

Bayesian estimation of dynamic asset pricing models with informative observations

Important features of certain economic models may be revealed by studying positive eigenfunctions of appropriately chosen linear operators. Examples include long-run risk-return relationships in dynamic asset pricing models and components of marginal utility in external habit formation models. This paper provides identification conditions for positive eigenfunctions in nonparametric models. Identification is achieved if the operator satisfies two mild positivity conditions and a power compactness condition. Both existence and identification are achieved under a further non-degeneracy condition. The general results are applied to obtain new identification conditions for external habit formation models and for positive eigenfunctions of pricing operators in dynamic asset pricing models.

A novel dynamic fare pricing model based on fuzzy bi-level programming for subway systems with heterogeneous passengers