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Relevant bibliographies by topics / Asset models

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Academic literature on the topic 'Asset models'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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Journal articles on the topic "Asset models"

1

Lalwani, Vaibhav, and Madhumita Chakraborty. "Multi-factor asset pricing models in emerging and developed markets." Managerial Finance 46, no. 3 (December 2, 2019): 360–80. http://dx.doi.org/10.1108/mf-12-2018-0607.

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Purpose The purpose of this paper is to compare the performance of various multifactor asset pricing models across ten emerging and developed markets. Design/methodology/approach The general methodology to test asset pricing models involves regressing test asset returns (left-hand side assets) on pricing factors (right-hand side assets). Then the performance of different models is evaluated based on how well they price multiple test assets together. The parameters used to compare relative performance of different models are their pricing errors (GRS statistic and average absolute intercepts) a

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Nagel, Stefan, and Amiyatosh Purnanandam. "Banks’ Risk Dynamics and Distance to Default." Review of Financial Studies 33, no. 6 (October 17, 2019): 2421–67. http://dx.doi.org/10.1093/rfs/hhz125.

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Abstract We adapt structural models of default risk to take into account the special nature of bank assets. The usual assumption of lognormally distributed asset values is not appropriate for banks. Typical bank assets are risky debt claims with concave payoffs. Because of the payoff nonlinearity, bank asset volatility rises following negative shocks to borrower asset values. As a result, standard structural models with constant asset volatility can severely understate banks’ default risk in good times when asset values are high. Additionally, bank equity return volatility is much more sensiti

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Ballotta, Laura, Gianluca Fusai, Angela Loregian, and M. Fabricio Perez. "Estimation of Multivariate Asset Models with Jumps." Journal of Financial and Quantitative Analysis 54, no. 5 (September 28, 2018): 2053–83. http://dx.doi.org/10.1017/s0022109018001321.

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We propose a consistent and computationally efficient 2-step methodology for the estimation of multidimensional non-Gaussian asset models built using Lévy processes. The proposed framework allows for dependence between assets and different tail behaviors and jump structures for each asset. Our procedure can be applied to portfolios with a large number of assets because it is immune to estimation dimensionality problems. Simulations show good finite sample properties and significant efficiency gains. This method is especially relevant for risk management purposes such as, for example, the compu

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Sinclair, N. A. "Multifactor Asset Pricing Models." Accounting & Finance 27, no. 1 (February 25, 2009): 17–36. http://dx.doi.org/10.1111/j.1467-629x.1987.tb00233.x.

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BARILLAS, FRANCISCO, and JAY SHANKEN. "Comparing Asset Pricing Models." Journal of Finance 73, no. 2 (March 31, 2018): 715–54. http://dx.doi.org/10.1111/jofi.12607.

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Dong, Ming. "A Tutorial on Nonlinear Time-Series Data Mining in Engineering Asset Health and Reliability Prediction: Concepts, Models, and Algorithms." Mathematical Problems in Engineering 2010 (2010): 1–22. http://dx.doi.org/10.1155/2010/175936.

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The primary objective of engineering asset management is to optimize assets service delivery potential and to minimize the related risks and costs over their entire life through the development and application of asset health and usage management in which the health and reliability prediction plays an important role. In real-life situations where an engineering asset operates under dynamic operational and environmental conditions, the lifetime of an engineering asset is generally described as monitored nonlinear time-series data and subject to high levels of uncertainty and unpredictability. I

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Hsiao, David W., Amy J. C. Trappey, Lin Ma, Yat Chih Fan, and Yen Chieh Mao. "Agent-Based Integrated and Collaborative Engineering Asset Management." Materials Science Forum 594 (August 2008): 481–93. http://dx.doi.org/10.4028/www.scientific.net/msf.594.481.

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Engineering assets are fundamentally important to enterprises. Thus, making the best use of engineering assets attracts equipment and system engineers’ attention. The state-of-the-art researches contribute to asset condition monitoring, asset symptom diagnosis, asset health prognosis, and the integration of above knowledge. However, they still lack the combination with enterprise resources to determine the best maintenance/renewal time for the optimization of total enterprise benefits. Consequently, this paper proposes the integrated architectural framework, activity and process models of a mu

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Halfawy, Mahmoud R., Dana J. Vanier, and Thomas M. Froese. "Standard data models for interoperability of municipal infrastructure asset management systems." Canadian Journal of Civil Engineering 33, no. 12 (December 1, 2006): 1459–69. http://dx.doi.org/10.1139/l05-098.

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Efficient management of infrastructure assets depends largely on the ability to efficiently share, exchange, and manage asset life-cycle information. Although software tools are used to support almost every asset management process in municipalities, data exchange is mainly performed using paper-based or neutral file formats based on ad hoc proprietary data models. Interoperability of various asset management systems is crucial to support better management of infrastructure data and to improve the information flow between various work processes. Standard data models can be used to significantl

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Mety Andriani Baitanu and Ni Luh Putu Wiagustini. "PENGARUH MANAJEMEN ASET TERHADAP OPTIMALISASI PEMANFAATAN ASET TETAP DI KABUPATEN KARANGASEM." Journal of Applied Management Studies 2, no. 1 (January 27, 2021): 38–48. http://dx.doi.org/10.51713/jamms.v2i1.22.

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The purpose of this research is to analyze the effect of asset management on optimizing the utilization of fixed assets in Karangasem Regency. This research is an associative research, namely the causality relationship between asset inventory, asset valuation, asset control and control to the level of optimization of fixed assets (land and buildings) owned by Karangasem Regency government. Data collection methods used in this study are through questionnaires / questionnaires. The data analysis technique in this study was quantitative statistical analysis using multiple linear regression models

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Mody, Makarand, Jochen Wirtz, Kevin Kam Fung So, Helen HaeEun Chun, and Stephanie Q. Liu. "Two-directional convergence of platform and pipeline business models." Journal of Service Management 31, no. 4 (May 8, 2020): 693–721. http://dx.doi.org/10.1108/josm-11-2019-0351.

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PurposeThis article examines the new phenomenon of the convergence of platform and pipeline business models. It examines the potential synergies and challenges for platforms to add pipeline components and vice versa for pipeline businesses.Design/methodology/approachThis paper uses a conceptual approach that synthesizes and integrates the literature from service, hospitality, and strategy, and supplements them with two illustrative mini-case studies.FindingsWhile the extant literature typically focuses on the dichotomy between incumbent pipeline businesses that create value by controlling a li

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Dissertations / Theses on the topic "Asset models"

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Davies, Philip R. "Empirical tests of asset pricing models." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1184592627.

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Fu, Jun, and 付君. "Asset pricing, hedging and portfolio optimization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48199345.

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Starting from the most famous Black-Scholes model for the underlying asset price, there has been a large variety of extensions made in recent decades. One main strand is about the models which allow a jump component in the asset price. The first topic of this thesis is about the study of jump risk premium by an equilibrium approach. Different from others, this work provides a more general result by modeling the underlying asset price as the ordinary exponential of a L?vy process. For any given asset price process, the equity premium, pricing kernel and an equilibrium option pricing for

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Murara, Jean-Paul. "Asset Pricing Models with Stochastic Volatility." Licentiate thesis, Mälardalens högskola, Utbildningsvetenskap och Matematik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-31576.

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Asset pricing modeling is a wide range area of research in Financial Engineering. In this thesis, which consists of an introduction, three papers and appendices; we deal with asset pricing models with stochastic volatility. Here stochastic volatility modeling includes diffusion models and regime-switching models. Stochastic volatility models appear as a response to the weakness of the constant volatility models. In Paper A , we present a survey on popular diffusion models where the volatility is itself a random process and we present the techniques of pricing European options under each model.

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Limkriangkrai, Manapon. "An empirical investigation of asset-pricing models in Australia." University of Western Australia. Faculty of Business, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0197.

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[Truncated abstract] This thesis examines competing asset-pricing models in Australia with the goal of establishing the model which best explains cross-sectional stock returns. The research employs Australian equity data over the period 1980-2001, with the major analyses covering the more recent period 1990-2001. The study first documents that existing asset-pricing models namely the capital asset pricing model (CAPM) and domestic Fama-French three-factor model fail to meet the widely applied Merton?s zero-intercept criterion for a well-specified pricing model. This study instead documents th

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Galagedera, Don U. A. "Investment performance appraisal and asset pricing models." Monash University, Dept. of Econometrics and Business Statistics, 2003. http://arrow.monash.edu.au/hdl/1959.1/5780.

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Chen, Ping, and 陈平. "Asset-liability management under regime-switching models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43223928.

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Ong, Alen Sen Kay. "Asset location decision models in life insurance." Thesis, City University London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336430.

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Hong, Harrison G. (Harrison Gregory). "Dyanmic models of asset returns and trading." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10315.

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De, Araujo Pedro Falcão. "Heterogeneity in macro models of asset accumulation." [Bloomington, Ind.] : Indiana University, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3337250.

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Thesis (Ph.D.)--Indiana University, Dept. of Economics, 2008.<br>Title from PDF t.p. (viewed on Jul 28, 2009). Source: Dissertation Abstracts International, Volume: 69-12, Section: A, page: 4804. Adviser: Gerhard Glomm.

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Chen, Ping. "Asset-liability management under regime-switching models." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43223928.

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Books on the topic "Asset models"

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Jeng, Jau-Lian. Empirical Asset Pricing Models. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74192-5.

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Asset pricing theory. Princeton, N.J: Princeton University Press, 2009.

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Vassiliou, P.-C. G. Discrete-time asset pricing models. London: ISTE Ltd/John Wiley & Sons, 2010.

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Vassiliou, P.-C. G. Discrete-time asset pricing models. London: ISTE Ltd/John Wiley & Sons, 2010.

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Discrete-time asset pricing models. London: ISTE Ltd/John Wiley & Sons, 2010.

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Vassiliou, P.-C. G. Discrete-time asset pricing models. London: ISTE Ltd/John Wiley & Sons, 2010.

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7

Emmanuel, Jurczenko, and Maillet Bertrand, eds. Multi-moment asset allocation and pricing models. Chichester: John Wiley & Sons, 2006.

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8

Hansen, Lars Peter. Econometric evaluation of asset pricing models. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1993.

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9

Heston, Steven L. Testing approximate linear asset pricing models. New Haven, CT: Yale University, School ofOrganization and Management, 1992.

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10

Lehmann, Bruce Neal. Empirical testing of asset pricing models. Cambridge, MA: National Bureau of Economic Research, 1992.

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Book chapters on the topic "Asset models"

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Ferson, Wayne E. "Asset pricing models." In Encyclopedia of Finance, 364–75. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-26336-6_34.

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Arouri, Mohamed El Hedi, Fredj Jawadi, and Duc Khuong Nguyen. "Asset Pricing Models." In The Dynamics of Emerging Stock Markets, 55–71. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2389-9_3.

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Glabadanidis, Paskalis. "Asset Pricing Models." In Absence of Arbitrage Valuation, 1–13. New York: Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137372871_1.

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Ferson, Wayne E. "Asset Pricing Models." In Encyclopedia of Finance, 613–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91231-4_9.

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Ferson, Wayne E. "Asset Pricing Models." In Encyclopedia of Finance, 263–71. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5360-4_9.

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Kariya, Takeaki, and Regina Y. Liu. "Pricing Models for Financial Assets." In Asset Pricing, 43–63. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9230-7_4.

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Li, Zongzhi. "Transportation decision models." In Transportation Asset Management, 517–605. Boca Raton ; London : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781315117966-17.

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Gupta, Arjun K., Wei-Bin Zeng, and Yanhong Wu. "Asset Pricing Theory." In Probability and Statistical Models, 199–219. Boston, MA: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-4987-6_10.

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Thompson, Neil. "Multi-asset Portfolio Models." In Portfolio Theory and the Demand for Money, 136–66. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-22827-0_10.

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Hol, Eugenie M. J. H. "Asset Return Volatility Models." In Dynamic Modeling and Econometrics in Economics and Finance, 7–26. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-5129-1_2.

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Conference papers on the topic "Asset models"

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Moore, Christine, Martin Tjioe, Anthony Manzella, Kristen L. Sanford Bernhardt, and Sue McNeil. "Agent Models for Asset Management." In International Workshop on Computing in Civil Engineering 2007. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40937(261)22.

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Hurtubise, Daniel. "Designing Data Models for Asset Metadata." In SMPTE Advanced Motion Imaging Conference. IEEE, 2001. http://dx.doi.org/10.5594/m00365.

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Song, Na, Wai-Ki Ching, Dong-Mei Zhu, and Tak-Kuen Siu. "Asset Allocation under Regime-Switching Models." In 2012 Fifth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2012. http://dx.doi.org/10.1109/bife.2012.38.

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Vasilevskaya, Maria, Simin Nadjm-Tehrani, Linda Ariani Gunawan, and Peter Herrmann. "Security asset elicitation for collaborative models." In the Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2422498.2422505.

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Osladil, Michal, and Libor Kozubik. "Auto-calibration of mathematical asset models: Refflecting change of behavior of energy assets." In 2017 18th International Scientific Conference on Electric Power Engineering (EPE). IEEE, 2017. http://dx.doi.org/10.1109/epe.2017.7967355.

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Theurich, Stefan, Robert Lehmann, and Martin Wollschlaeger. "Network asset models in intelligent field devices." In 2010 8th IEEE International Workshop on Factory Communication Systems - (WFCS 2010). IEEE, 2010. http://dx.doi.org/10.1109/wfcs.2010.5548614.

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Teng, Huei Wen, Yu-Hsien Li, and Shang-Wen Chang. "Machine Learning in Empirical Asset Pricing Models." In 2020 International Conference on Pervasive Artificial Intelligence (ICPAI). IEEE, 2020. http://dx.doi.org/10.1109/icpai51961.2020.00030.

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Foote, W., J. Kraemer, and G. Foster. "APL2 implementation of numerical asset pricing models." In the international conference. New York, New York, USA: ACM Press, 1988. http://dx.doi.org/10.1145/55626.55643.

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Markov, Pavel Vladimirovich, Andrey Yuryevich Botalov, Inna Vladimirovna Gaidamak, Margarita Andreevna Smetkina, Andrey Fyodorovich Rychkov, and Timur Aleksandrovich Koshkin. "Methodology for Constructing Simplified Reservoir Models for Integrated Asset Models." In SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206544-ms.

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Abstract The paper presents the developed methodology for building simplified reservoir models for integrated asset models (IAM) of oil and gas fields: allocation and substantiation of areas, substantiation of model parameters, substantiation of actual weighted average reservoir pressure for areas, history matching and validation, evaluation of effective injection factors, integration in an IAM, prediction calculations, model updating. The novelty of the methodology is the developed approaches and methods of considering different features of fields with a high extent of automation for areas an

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Pak, Charles, and James Cannady. "Asset priority risk assessment using hidden markov models." In the 10th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631728.1631750.

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Reports on the topic "Asset models"

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Barillas, Francisco, and Jay Shanken. Comparing Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, December 2015. http://dx.doi.org/10.3386/w21771.

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Hansen, Lars Peter, John Heaton, and Erzo G. J. Luttmer. Econometric Evaluation of Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, October 1993. http://dx.doi.org/10.3386/t0145.

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Lehmann, Bruce. Empirical Testing of Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, April 1992. http://dx.doi.org/10.3386/w4043.

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Pastor, Lubos, and Robert Stambaugh. Comparing Asset Pricing Models: An Investment Perspective. Cambridge, MA: National Bureau of Economic Research, August 1999. http://dx.doi.org/10.3386/w7284.

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Chen, Hui, Winston Wei Dou, and Leonid Kogan. Measuring “Dark Matter” in Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, November 2019. http://dx.doi.org/10.3386/w26418.

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Cortazar, Gonzalo, Ivo Kovacevic, and Eduardo Schwartz. Commodity and Asset Pricing Models: An Integration. Cambridge, MA: National Bureau of Economic Research, June 2013. http://dx.doi.org/10.3386/w19167.

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Berk, Jonathan, and Jules van Binsbergen. Assessing Asset Pricing Models Using Revealed Preference. Cambridge, MA: National Bureau of Economic Research, August 2014. http://dx.doi.org/10.3386/w20435.

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Barillas, Francisco, and Jay Shanken. Comparing Priors for Comparing Asset Pricing Models. American Finance Association, June 2022. http://dx.doi.org/10.37214/jofweb.5.

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Nagel, Stefan, and Kenneth Singleton. Estimation and Evaluation of Conditional Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, October 2010. http://dx.doi.org/10.3386/w16457.

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Hodrick, Robert, and Xiaoyan Zhang. Evaluating the Specification Errors of Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, April 2000. http://dx.doi.org/10.3386/w7661.

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