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.

Economic research of the last decade linking macroeconomic fundamentals to asset prices has revealed evidence that standard intertemporal asset pricing theory is not successful in explaining (unconditional) first moments of asset market characteristics such as the risk-free interest rate, equity premium and the Sharpe-ratio. Subsequent empirical research has pursued the question whether those characteristics of asset markets are time varying and, in particular, varying over the business cycle. Recently intertemporal asset pricing models have been employed to replicate those time varying characteristics. The aim of our contribution is (1) to relax some of the assumptions that previous work has imposed on underlying economic and financial variables, (2) to extend the solution technique of Marcet and Den Haan (1990) for those models by nonparametric expectations and (3) to propose a new estimation procedure based on the above solution technique. To allow fornnonparametric expectations in the expectations approach for numerically solving the intertemporal economic model we employ the Local Linear Mapsn(LLMs) of Ritter, Martinetz and Schulten (1992) to approximate conditional expectations in the Euler equation. In our estimation approach based on non-parametric expectations we are able to use full structural information and,nconsequently, Monte Carlo simulations show that our estimations are less biased than the widely applied GMM procedure. Based on quarterly U.S. data we also empirically estimate structural parameters of the model and explore its time varying asset price characteristics for two types of preferences, power utility and habit persistence. We in particular focus on the Sharpe-ratio and find indication that the model is able to capture the time variation of thenSharpe-ratio.

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.

PurposeThis paper aims to study whether noisy public information that investors receive about the expected aggregate dividend growth rate can help better understand the large average equity premium and stock return volatility in the US financial market.Design/methodology/approachThis paper considers a dynamic asset pricing model with a representative agent, who cannot observe the expected growth rate of dividends and must learn its value by using noisy information. In addition, this paper presents a simple model for noisy information calibration.FindingsWith a coefficient of relative risk aversion below 10 and the time impatience parameter between 0 and 1, the calibrated model is able to yield an average risk-free interest rate, equity premium and stock return volatility that are close to the stylized facts in the US financial market.Originality/valueFirst, this paper presents a different equilibrium model with a simple “catching up with the Joneses” preference and noisy information. Second, this paper develops a simple calibration procedure to calibrate the information process to study whether the calibrated model can help explain the large average equity premium and stock return volatility in the US financial market data.

Procyclical Leverage in Europe and its Role in Asset Pricing

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.

In the present study we show that, based on equally weighted portfolios of continuously listed Finnish and Swedish stocks, aDynamic Model of Capital Asset Pricing (DCAPM) outperforms the static Capital Asset Pricing Model (CAPM) in the Super Criterion Test. It is demonstrated that the portfolio efficiency of the dynamic model is improved, when using a properly defined transition matrix in the Kalman Filtering Algorithm. The advice and encouragement of Professor Leif Nordberg (Department of Statistics, Abo Akademi University) is gratefully acknowledged. I thank Jaana Aaltonen for her assistance in monitoring the computer programs. I also thank an anonymous referee for his valuable comments and suggestions for improving the quality of the paper. The research has been supported by a grant from the Academy of Finland. The Finnish database used in the study was given by Kansallis-Osake-Pankki for the research project. The Swedish database was acquired from Aktiedata Ltd, by funds granted for this project by the Foundation for Supporting Capital Market Research in Finland (Stiftelsen for Framjande av Vardepappersmarknaden i Finland). The testing procedure was programmed in FORTRAN with access to the IMSL-library on VAX 8800 computer of Abo Akademi University by the author.

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.

From Dynamic Linear Evaluation Rule to Dynamic Capital Asset Pricing Model

In this paper, we take the advantage of high frequency data to develop option pricing model and select the Realized GARCH model to describe the volatility of assets, use NIG distribution to describe the distribution of underlying assets, and also build the Realized-GARCH-NIG model to price the option. Finally, we obtain the dynamic option pricing model based on the Realized-GARCH-NIG approach. To verify the effect of the dynamic option pricing model based on the Realized-GARCH- NIG approach, this paper provides the empirical analysis between the dynamic option pricing model based on the Realized-GARCH-NIG approach and the B-S option pricing model. The results show that the option value obtained from the dynamic option pricing model based on the Realized-GARCH-NIG approach is more accurate and effective than the B-S option pricing model.

The sustainability of stock price fluctuations indicated by many empirical studies hardly reconciles with the existing models in standard financial theories. This paper proposes a recursive dynamic asset pricing model based on the comprehensive impact of the sentiment investor, the information trader and the noise trader. The dynamic process of the asset price is characterized and a numerical simulation of the model is provided. The model captures the features of the actual stock price that are consistent with the empirical evidence on the sustainability of stock price fluctuations. It also offers a partial explanation for other financial anomalies, for example, asset price’s overreaction, asset bubble and the financial crisis. The major finding is that investor sentiment is the key factor to understand the sustainability of stock price fluctuations.

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.