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Relevant bibliographies by topics / Dynamic Pricing Models

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Academic literature on the topic 'Dynamic Pricing Models'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Dynamic Pricing Models"

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Lembhe, Pankaj. "Dynamic Toll Pricing Models and Traffic Flow Optimization." International Journal of Science and Research (IJSR) 9, no. 11 (2020): 1716–22. http://dx.doi.org/10.21275/sr24314032137.

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PONOMARENKO, Ihor, and Dmytro PONOMARENKO. "Dynamic pricing in marketing." Scientia fructuosa 161, no. 3 (2025): 74–89. https://doi.org/10.31617/1.2025(161)05.

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Forming effective marketing communications with the target audience in the digital environment involves implementing a modern and flexible pricing system that considers the dynamics of changes in a set of factors. A hypothesis has been formulated that dynamic pricing based on machine learning algorithms allows businesses to achieve optimal demand for goods and services of companies, and also helps to ensure the loyalty of consumers to the brands in the long term. Conducting the research, general scientific methods of analysis and synthesis were used to characterize the main strategies of dynam

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Lehmann, Bruce N. "Notes on dynamic factor pricing models." Review of Quantitative Finance and Accounting 2, no. 1 (1992): 69–87. http://dx.doi.org/10.1007/bf00243985.

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Narahari, Y., C. V. L. Raju, K. Ravikumar, and Sourabh Shah. "Dynamic pricing models for electronic business." Sadhana 30, no. 2-3 (2005): 231–56. http://dx.doi.org/10.1007/bf02706246.

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Ait Bachir, Lynda, Oumelkheir Elbaroud, and Fatma Bouderra. "Dynamic Pricing Models for Automobile Insurance." Financial Markets, Institutions and Risks 9, no. 1 (2025): 162–94. https://doi.org/10.61093/fmir.9(1).162-194.2025.

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The accurate pricing of automobile insurance remains a critical challenge, particularly in markets where traditional models fail to capture risk heterogeneity. This study addresses the limitations of conventional Poisson models, which assume uniform accident probabilities among insured individuals, by incorporating advanced probabilistic models that account for overdispersion and individual risk variability. The primary objective is to develop a more precise and equitable pricing model for automobile insurance premiums, integrating statistical and Bayesian inference techniques. The research fo

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Balogun Segun Segbenu, Mariam Olateju, Adebayo Sulaimon Olawale, and Victoria Kujore. "Applications of Reinforcement Learning in Dynamic Pricing Models for E-Commerce Businesses." World Journal of Advanced Research and Reviews 26, no. 3 (2025): 1562–73. https://doi.org/10.30574/wjarr.2025.26.3.2319.

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Dynamic pricing has become a cornerstone strategy for e-commerce businesses seeking to optimize revenue while maintaining competitive advantage in rapidly changing digital markets. This review examines the integration of reinforcement learning techniques into dynamic pricing models, exploring how these adaptive algorithms enable businesses to make real-time pricing decisions based on market conditions, consumer behavior, and competitive dynamics. The research synthesizes current methodologies, implementation frameworks, and performance outcomes across various e-commerce sectors. Reinforcement

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He, Zhongzhi (Lawrence), Sahn-Wook Huh, and Bong-Soo Lee. "Dynamic Factors and Asset Pricing." Journal of Financial and Quantitative Analysis 45, no. 3 (2010): 707–37. http://dx.doi.org/10.1017/s0022109010000207.

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AbstractThis study develops an econometric model that incorporates features of price dynamics across assets as well as through time. With the dynamic factors extracted via the Kalman filter, we formulate an asset pricing model, termed the dynamic factor pricing model (DFPM). We then conduct asset pricing tests in the in-sample and out-of-sample contexts. Our analyses show that the ex ante factors are a key component in asset pricing and forecasting. By using the ex ante factors, the DFPM improves upon the explanatory and predictive power of other competing models, including unconditional and c

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Divya, Chockalingam. "Dynamic Pricing Strategies in Retail: How Customer Analytics Can Optimize Pricing Models." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 1 (2020): 1–4. https://doi.org/10.5281/zenodo.15054790.

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Dynamic pricing has revolutionized the retail sector, allowing businesses to adjust prices in real-time based on customer behavior, competitor pricing, and market demand. This paper explores how customer analytics can optimize dynamic pricing models, improving revenue, customer satisfaction, and market competitiveness. By leveraging big data and machine learning, retailers can implement intelligent pricing strategies that respond to evolving consumer patterns. The study also highlights case studies of successful dynamic pricing implementations and discusses the challenges associated with these

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Behera, Prashanta kumar, and Dr Ramraj T. Nadar. "Dynamic Approach for Index Option Pricing Using Different Models." Journal of Global Economy 13, no. 2 (2017): 105–20. http://dx.doi.org/10.1956/jge.v13i2.460.

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Option pricing is one of the exigent and elementary problems of computational finance. Our aims to determine the nifty index option price through different valuation technique.Â In this paper, we illustrate the techniques for pricing of options and extracting information from option prices. We also describe various ways in which this information has been used in a number of applications. When dealing with options, we inevitably encounter the Black-Scholes-Merton option pricing formula, which has revolutionized the way in which options are priced in modern time. Black and Scholes (1973) and Mer

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Researcher. "DYNAMIC SAAS PRICING: IMPLEMENTING USAGE-BASED MODELS FOR ENHANCED CUSTOMER VALUE." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1650–62. https://doi.org/10.5281/zenodo.14243940.

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This article explores the transformative evolution of Software-as-a-Service (SaaS) pricing models, focusing on the shift from traditional subscription-based approaches to dynamic, consumption-based pricing strategies. The article examines how this transition has fundamentally changed the way software services are valued and delivered to customers, improving both provider sustainability and customer satisfaction. Through comprehensive article analysis of implementation strategies, challenges, and best practices, the article demonstrates how modern technological capabilities enable real-time usa

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Dissertations / Theses on the topic "Dynamic Pricing Models"

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Davis, Michael C. "Dynamic models of price changes /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2001. http://wwwlib.umi.com/cr/ucsd/fullcit?p3026374.

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Kachani, S. (Soulaymane). "Dynamic travel time models for pricing and route guidance : a fluid dynamics approach." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8527.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 2002.<br>Includes bibliographical references (leaves 193-201).<br>This thesis investigates dynamic phenomena that arise in a variety of systems that share similar characteristics. A common characteristic of particular interest in this work is travel time. We wish to address questions of the type: How long does it take a driver to traverse a route in a transportation network? How long does a unit of product remain in inventory before being sold? As a result, our goal is not only to develop models for travel time

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Chu, Kai-cheung, and 朱啟祥. "The effects of mean reversion on dynamic corporate finance and asset pricing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47752762.

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This thesis aims to investigate the effects of mean reversion on dynamic corporate finance decisions and stock pricing. In Chapter 1, a continuous-time real option model of mature firm that produces product with exogenous mean reverting price is developed to study the firm’s optimal exit and leverage policies. Simulation results show that both liquidation and bankruptcy triggers are negatively related to the long run price levels, while the speed of mean reversion interacts with the long run price level to affect the firm’s exit decisions in two opposite directions depending on the l

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Karam, Philippe Doumit. "Dynamic asset pricing models with incomplete markets and market frictions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22471.pdf.

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Kachani, Soulaymane, and Georgia Perakis. "A Fluid Model of Dynamic Pricing and Inventory Management for Make-to-Stock Manufacturing Systems." Massachusetts Institute of Technology, Operations Research Center, 2002. http://hdl.handle.net/1721.1/5137.

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In this paper, we introduce a fluid model of dynamic pricing and inventory management for make-to-stock manufacturing systems. Instead of considering a traditional model that is based on how price affects demand, we consider a model that relies on how price and level of inventory affect the time a unit of product remains in inventory. Our motivation is based on the observation that in inventory systems, a unit of product incurs a delay before being sold. This delay depends on the unit price of the product, prices of competitors, and the level of inventory of this product. Moreover, delay data

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GONZATO, LUCA. "Application of Sequential Monte Carlo Methods to Dynamic Asset Pricing Models." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/295144.

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In questa tesi si considera l’applicazione di metodi Monte Carlo sequenziali per modelli di asset pricing di tipo dinamico. Il primo capitolo della tesi presenta una panoramica generale sui metodi Monte Carlo sequenziali. Nello specifico, partendo da metodi Monte Carlo standard si giunge fino allo stato dell’arte per quanto riguarda i metodi Monte Carlo sequenziali. Il secondo capitolo costituisce una review della letteratura sui metodi di simulazione esatta per processi di Hawkes. Dall’analisi svolta si evince che lo schema proposto da Dassios e Zaho (2013) performa meglio degli altri algorit

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Schlosser, Rainer. "Six essays on stochastic and deterministic dynamic pricing and advertising models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2014. http://dx.doi.org/10.18452/16973.

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Die kumulative Dissertation beschäftigt sich mit stochastischen und deterministischen dynamischen Verkaufsmodellen für langlebige sowie verderbliche Güter. Die analysierten dynamischen Modelle sind durch die Möglichkeit der simultanen Variation von Preis und Werbung in stetiger Zeit charakterisiert und folgen den aktuellen Entwicklungen der Dynamischen Preissetzung. Dabei steht die Berücksichtigung und Analyse von (i) Zeitinhomogenitäten, (ii) Adoptionseffekten, (iii) Oligopolwettbewerb und (iv) der Risikoaversion des Entscheiders im Zentrum der Arbeit. Für die Spezialfälle isoelastischer u

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Heinzl, Thomas. "Dynamic hedging strategies and option pricing in bond market models with transaction costs /." Bamberg, 2000. http://aleph.unisg.ch/hsgscan/hm00006553.pdf.

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Weber, Martin. "Optimal inventory control in the presence of dynamic pricing and dynamic advertising." Doctoral thesis, Humboldt-Universität zu Berlin, 2015. http://dx.doi.org/10.18452/17339.

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Diese Dissertation analysiert das optimale Zusammenspiel dynamischer Preissetzung, dynamischer Werbung und Bestandsmanagement. Wir betrachten verschiedene Optimierungsprobleme für einen monopolistischen Händler bei gegebener zeitabhängiger deterministischer Nachfrage. In Kapitel 2 erweitern wir das Modell von Rajan et al. (1992). Der Händler darf einen dynamischen Preis, eine dynamische Werberate und die Lagergröße bei fester Verkaufsdauer wählen, so dass der Barwert von Umsatz minus Lager-, Einkaufs- und (nichtlinearen) Werbekosten maximiert wird; zusätzlich zerfällt der Lagerbestand mit expo

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Fox, David. "Dynamic demand modelling and pricing decision support systems for petroleum." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/dynamic-demand-modelling-and-pricing-decision-support-systems-for-petroleum(2ce6efed-a7eb-4d10-b325-4d4590ba57ad).html.

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Pricing decision support systems have been developed in order to help retail companies optimise the prices they set when selling their goods and services. This research aims to enhance the essential forecasting and optimisation techniques that underlie these systems. This is first done by applying the method of Dynamic Linear Models in order to provide sales forecasts of a higher accuracy compared with current methods. Secondly, the method of Support Vector Regression is used to forecast future competitor prices. This new technique aims to produce forecasts of greater accuracy compared with th

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Books on the topic "Dynamic Pricing Models"

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Christ, Steffen. Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6.

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Lehmann, Bruce N. Notes on dynamic factor pricing models. National Bureau of Economic Research, 1991.

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Hodrick, Robert J. An international dynamic asset pricing model. National Bureau of Economic Research, 1999.

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F, Gallmeyer Michael, and National Bureau of Economic Research., eds. Arbitrage-free bond pricing with dynamic macroeconomic models. National Bureau of Economic Research, 2007.

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Caballero, Ricardo J. Heterogeneity and output fluctuations in a dynamic menu-cost economy. National Bureau of Economic Research, 1991.

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Hu, Sheng C. Optimal advertising pricing policies in a mature market: A dynamic duopoly model. Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1989.

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Semmler, Willi. Asset prices, booms and recessions: Financial economics from a dynamic perspective. 3rd ed. Springer, 2011.

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Duffie, Darrell. Dynamic asset pricing theory. 2nd ed. Princeton University Press, 1996.

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Duffie, Darrell. Dynamic asset pricing theory. 3rd ed. Princeton University Press, 2001.

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Schulz, Paul E., Paul E. Schulz, and Barbara P. Hoffmann. Financial asset pricing: Theory, global policy and dynamics. Nova Science Publishers, 2010.

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Book chapters on the topic "Dynamic Pricing Models"

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Christ, Steffen. "Dynamic Pricing." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_3.

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Christ, Steffen. "Self-Learning Linear Models." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_4.

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Christ, Steffen. "Introduction." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_1.

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Christ, Steffen. "Choice Situation in Low-Cost Markets." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_10.

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Christ, Steffen. "Multinomial Logit Model for Low-Cost Travel Choice." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_11.

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Christ, Steffen. "Computational Results and Evaluation." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_12.

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Christ, Steffen. "Summary and Outlook." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_13.

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Christ, Steffen. "Motivation and Structure." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_2.

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Christ, Steffen. "Demand in Low Cost Markets." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_5.

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Christ, Steffen. "The Demand Forecasting Model." In Operationalizing Dynamic Pricing Models. Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_6.

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Conference papers on the topic "Dynamic Pricing Models"

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Miyatsu, Kazuhiro. "The Optimal Pricing Models: Dynamic Pricing Personalization based on Machine Learning." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825186.

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Terzi, Mourad, Yassine Ouazene, Alice Yalaoui, and Farouk Yalaoui. "Optimizing dynamic pricing problem under multinomial demand models using a convex nonlinear programming approach." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708399.

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Sudirman, Ivan Diryana, and Rosyidah Rahmah. "Dynamic Pricing Optimization for Coffee Shops Using a Machine Learning Approach with Random Forest Models." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986336.

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Sathyabama, K., A. P. Abishek Raj, and R. Gukan. "AI-Driven Expiry Prediction and Dynamic Pricing System for Retail Inventory Management Utilizing ARIMA and LSTM Models." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10895116.

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Bhukta, Souvik, Agniva Shil, Ankan Dutta, and Bikash Sadhukhan. "KisanConnect: Reshaping Agricultural Trade with Dynamic Pricing Model." In 2024 2nd International Conference on Networking, Embedded and Wireless Systems (ICNEWS). IEEE, 2024. http://dx.doi.org/10.1109/icnews60873.2024.10730822.

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Ashok, Sakshi, S. Athulya, and Jyothi Thomas. "Price Minds: AI-Driven Insights, Recommendations and Dynamic Pricing." In 2025 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE). IEEE, 2025. https://doi.org/10.1109/amathe65477.2025.11080732.

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Cho, Daewoong, Gowri Ramachandran, Raja Jurdak, and Salil Kanhere. "Cost-aware Blockchain Mining Strategy: Dynamic Energy Pricing (DEP) Model." In 2024 6th Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS). IEEE, 2024. http://dx.doi.org/10.1109/brains63024.2024.10732382.

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Hua, Huichun, Liang Ling, Xin Jiang, and Jiayi Sun. "Dynamic Retail Pricing Model For Electricity Retailers Considering Power Quality Demand." In 2024 21st International Conference on Harmonics and Quality of Power (ICHQP). IEEE, 2024. https://doi.org/10.1109/ichqp61174.2024.10768745.

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Ye, Qianqian, Zhengyi Cai, and Simon Hu. "Optimizing Urban Parking Pricing with a Dual Dynamic Evolution Model for Multimodal Networks." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920060.

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Tang, Wenjun, Jie Liu, Shan He, Yuming Zhao, and Xiaoying Tang. "A Dynamic Business Model for Battery Swapping Stations with Pricing and Charging Scheduling." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10990552.

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Reports on the topic "Dynamic Pricing Models"

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Lehmann, Bruce. Notes on Dynamic Factor Pricing Models. National Bureau of Economic Research, 1991. http://dx.doi.org/10.3386/w3677.

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Gallmeyer, Michael, Burton Hollifield, Francisco Palomino, and Stanley Zin. Arbitrage-Free Bond Pricing with Dynamic Macroeconomic Models. National Bureau of Economic Research, 2007. http://dx.doi.org/10.3386/w13245.

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Chen, Xin, Yanfeng Ouyang, Ebrahim Arian, Haolin Yang, and Xingyu Ba. Modeling and Testing Autonomous and Shared Multimodal Mobility Services for Low-Density Rural Areas. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-013.

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Recent developments in transformative technologies hold the promise to provide holistic solutions for affordable transportation services to rural areas and thus greatly alleviate existing social inequality through efficient planning and management of complex transportation systems and systemwide interactions among multiple modes. To realize the promise, many challenging research questions need to be addressed, which often leads to computationally intractable, large-scale, dynamic/stochastic, discrete optimization models. This project proposes to address some of the challenges by building a ser

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Hodrick, Robert, David Tat-Chee Ng, and Paul Sengmueller. An International Dynamic Asset Pricing Model. National Bureau of Economic Research, 1999. http://dx.doi.org/10.3386/w7157.

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Seema, Seema, Andreas Theocharis, and Andreas Kassler. Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstad University, 2024. http://dx.doi.org/10.59217/yjll7238.

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This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can fac

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Marty, Frédéric, and Thierry Warin. Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement. CIRANO, 2023. http://dx.doi.org/10.54932/iwpg7510.

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This paper examines algorithmic collusion from legal and economic perspectives, highlighting the growing role of algorithms in digital markets and their potential for anti-competitive behavior. Using bandit algorithms as a model, traditionally applied in uncertain decision-making contexts, we illuminate the dynamics of implicit collusion without overt communication. Legally, the challenge is discerning and classifying these algorithmic signals, especially as unilateral communications. Economically, distinguishing between rational pricing and collusive patterns becomes intricate with algorithm-

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Gedge, Christopher, James Roberts, and Andrew Sweeting. A Model of Dynamic Limit Pricing with an Application to the Airline Industry. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20293.

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Chepeliev, Maksym, Israel Osorio Rodarte, and Dominique van der Mensbrugghe. Distributional Impacts of Carbon Pricing Policies under Paris Agreement: Inter and Intra-Regional Perspectives. GTAP Working Paper, 2021. http://dx.doi.org/10.21642/gtap.wp88.

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While bringing multiple benefits for the environment, achievement of the stringent global greenhouse gas emissions reduction target, like the one outlined in the Paris Climate Agreement, is associated with significant implementation costs and could impact different dimensions of human well-being, including welfare, poverty and distributional aspects. In this paper, we analyze the poverty and distributional impacts of different carbon pricing mechanisms consistent with reaching the Paris Agreement targets. We link a global recursive dynamic computable general equilibrium model ENVISAGE with the

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Delgado-Téllez, Mar, Javier Quintana, and Daniel Santabárbara Daniel Santabárbara. Carbon pricing, border adjustment and renewable energy investment: a network approach. Banco de España, 2025. https://doi.org/10.53479/38923.

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An increase of €100 per tonne in the EU carbon price reduces the carbon footprint but lowers GDP due to higher energy costs and carbon leakage. Using a dynamic multi-sector, multi-country model augmented with an energy block that includes endogenous renewable energy investment, we analyze the macroeconomic and emissions effects of a carbon price. Investment in renewable energy mitigates electricity price increases in the medium term, leading to a smaller GDP loss (up to -0.4%) and a larger emissions reduction (24%) in the EU. Neglecting renewable energy investment overestimates the negative ec

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Hinterlang, Natascha. Effects of Carbon Pricing in Germany and Spain: An Assessment with EMuSe. Banco de España, 2023. http://dx.doi.org/10.53479/33814.

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Using the dynamic, three-region environmental multi-sector general equilibrium model EMuSe, we find that pricing carbon in Germany or Spain only leads to a permanent negative effect on output in these economies. The induced emissions reduction is not large enough to overcompensate for the increase in marginal production costs. If the rest of Europe joins the carbon pricing scheme, long-run output effects are positive. However, in this case, transition costs are even larger due to close trade relations within Europe. We find evidence for carbon leakage, which can be reduced slightly by a border

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