Gotowe bibliografie tematyczne / Asset models

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Asset models”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 31 stycznia 2023

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Artykuły w czasopismach na temat "Asset models"

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Lalwani, Vaibhav, i Madhumita Chakraborty. "Multi-factor asset pricing models in emerging and developed markets". Managerial Finance 46, nr 3 (2.12.2019): 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) and explained variation (average adjusted R2). Findings The Fama-French five-factor model improves the pricing performance for stocks in Australia, Canada, China and the USA. The pricing in these countries appears to be more integrated. However, the superior performance in these four countries is not consistent across a variety of test assets and the magnitude of reduction in pricing errors vis-à-vis three- or four-factor models is often economically insignificant. For other markets, the parsimonious three-factor model or its four-factor variants appear to be more suitable. Originality/value Unlike most asset pricing studies that use test assets based on variables that are already used to construct RHS factors, this study uses test assets that are generally different from RHS sorts. This makes the tests more robust and less biased to be in favour of any multifactor model. Also, most international studies of asset pricing tests use data for different markets and combine them into regions. This study provides the evidence from ten countries separately because prior research has shown that locally constructed factors are more suitable to explain asset prices. Further, this study also tests for the usefulness of adding a quality factor in the existing asset pricing models.
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Nagel, Stefan, i Amiyatosh Purnanandam. "Banks’ Risk Dynamics and Distance to Default". Review of Financial Studies 33, nr 6 (17.10.2019): 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 sensitive to negative shocks to asset values than in standard structural models.
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Ballotta, Laura, Gianluca Fusai, Angela Loregian i M. Fabricio Perez. "Estimation of Multivariate Asset Models with Jumps". Journal of Financial and Quantitative Analysis 54, nr 5 (28.09.2018): 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 computation of portfolio Value at Risk and intra-horizon Value at Risk, as we show in detail in an empirical illustration.
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Sinclair, N. A. "Multifactor Asset Pricing Models". Accounting & Finance 27, nr 1 (25.02.2009): 17–36. http://dx.doi.org/10.1111/j.1467-629x.1987.tb00233.x.

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BARILLAS, FRANCISCO, i JAY SHANKEN. "Comparing Asset Pricing Models". Journal of Finance 73, nr 2 (31.03.2018): 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. It has been proved that application of data mining techniques is very useful for extracting relevant features which can be used as parameters for assets diagnosis and prognosis. In this paper, a tutorial on nonlinear time-series data mining in engineering asset health and reliability prediction is given. Besides that an overview on health and reliability prediction techniques for engineering assets is covered, this tutorial will focus on concepts, models, algorithms, and applications of hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) in engineering asset health prognosis, which are representatives of recent engineering asset health prediction techniques.
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Hsiao, David W., Amy J. C. Trappey, Lin Ma, Yat Chih Fan i Yen Chieh Mao. "Agent-Based Integrated and Collaborative Engineering Asset Management". Materials Science Forum 594 (sierpień 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 multi-agent system called agent-based integrated engineering asset management (AIEAM) based on agent techniques to build collaborative environment for asset manager, diagnosis expert, prognosis expert and enterprise resource manager. An engineering asset management case (for repair and maintenance of automatic parking tower) applying the proposed architecture and models is depicted in the paper.
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Halfawy, Mahmoud R., Dana J. Vanier i Thomas M. Froese. "Standard data models for interoperability of municipal infrastructure asset management systems". Canadian Journal of Civil Engineering 33, nr 12 (1.12.2006): 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 significantly improve the availability and consistency of asset data across different software systems, to integrate data across various disciplines, and to exchange information between various stakeholders. This paper surveys a number of data standards that might be used in implementing interoperable and integrated infrastructure asset management systems. The main requirements for standard data models are outlined, and the importance of interoperability from an asset management perspective is highlighted. The role that spatial data and geographic information systems (GIS) can play in enhancing the efficiency of managing asset life-cycle data is also discussed. An ongoing effort to develop a standard data model for sewer systems is presented, and an example implementation of interoperable GIS and hydraulic modeling software is discussed.Key words: data standards, municipal infrastructure, asset management, data models, interoperability.
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Mety Andriani Baitanu i Ni Luh Putu Wiagustini. "PENGARUH MANAJEMEN ASET TERHADAP OPTIMALISASI PEMANFAATAN ASET TETAP DI KABUPATEN KARANGASEM". Journal of Applied Management Studies 2, nr 1 (27.01.2021): 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 that were completed with the SPSS (Statistical Package for the Social Science) for windows The results of hypothesis testing conducted using multiple linear regression models indicate that: 1). Asset inventory has a positive and significant effect on optimizing the management of fixed assets. 2). Asset valuation has a positive and significant effect on optimizing asset management and 3). Asset control and supervision has a positive and significant effect on optimizing the management of the Karangasem Regency Government assets which shows that any improvement in asset control and supervision will be followed by increased optimization of asset management still the Karangasem Regency government. The implications of this study are a) Asset Inventory has a positive and significant effect on Asset Optimization. This proves that with the implementation of the inventory includes data collection, codification / labeling, grouping and bookkeeping / good administration, it will provide an optimal utilization of assets. b) Asset Valuation has a positive and significant effect on Asset Optimization. Asset valuation conducted by an independent institution is needed by the local government of Karangasem Regency, where the results of this value will be used to determine the value of wealth and information for the application of prices for the assets to be sold. c) Asset Monitoring and Control has a positive and significant effect on Asset Optimization. Supervision and Control have been carried out well by the Karangasem Regency government through the development of the Asset Management Information System.
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Mody, Makarand, Jochen Wirtz, Kevin Kam Fung So, Helen HaeEun Chun i Stephanie Q. Liu. "Two-directional convergence of platform and pipeline business models". Journal of Service Management 31, nr 4 (8.05.2020): 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 linear series of activities and network effects-driven platforms, we differentiate between two types of platform business models (i.e. platforms with asset control and platforms with peer-provided assets). Further, we identify three common pathways of convergence; that is, pipelines moving towards (1) platforms with asset control and (2) those with peer-provided assets, and (3) platforms with peer-provided assets adopting defining business characteristics of pipelines. Furthermore, we contrast key characteristics of the three business models and examine potential synergies and challenges for business model convergence. Our findings suggest that convergence from pipelines to platforms with asset control seems to be a natural extension that offers many potential synergies and relatively minor challenges. In contrast, convergence from pipelines to platforms with peer-provided assets is likely to encounter more serious challenges and few synergies. Finally, the synergies and challenges of convergence from platforms with peer-provided assets to pipelines seem to be in between the other two in terms of synergies and challenges.Practical implicationsThis article helps managers think through key considerations regarding potential synergies to develop and challenges to mitigate for embarking on convergence strategies between pipeline and platform business models.Originality/valueThis article is the first in the service, business model and strategy literature to identify, define, and conceptualize business model convergence between platforms with asset control, those with peer-provided assets and pipeline businesses. It is also the first to examine potential synergies and challenges these different paths of business model convergence may entail.
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Rozprawy doktorskie na temat "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, i 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 formula can be derived. Moreover, some empirical evidence such as the negative variance risk premium, implied volatility smirk, and negative skewness risk premium can be well explained by using the relation between the physical and risk-neutral distributions for the jump component. Another strand of the extensions of the Black-Scholes model is about the models which can incorporate stochastic volatility in the asset price. The second topic of this thesis is about the replication of exponential variance, where the key risks are the ones induced by the stochastic volatility and moreover it can be correlated with the returns of the asset, referred to as leverage effect. A time-changed L?vy process is used to incorporate jumps, stochastic volatility and leverage effect all together. The exponential variance can be robustly replicated by European portfolios, without any specification of a model for the stochastic volatility. Beyond the above asset pricing and hedging, portfolio optimization is also discussed. Based on the Merton (1969, 1971)'s reduced portfolio optimization and the delta hedging problem, a portfolio of an option, the underlying stock and a risk-free bond can be optimized in discrete time and its optimal solution can be shown to be a mixture of the Merton's result and the delta hedging strategy. The main approach is the elasticity approach, which has initially been proposed in continuous time. In addition to the above optimization problem in discrete time, the same topic but in a continuous-time regime-switching market is also presented. The use of regime-switching makes our market incomplete, and makes it difficult to use some approaches which are applicable in complete market. To overcome this challenge, two methods are provided. The first method is that we simply do not price the regime-switching risk when obtaining the risk-neutral probability. Then by the idea of elasticity, the utility maximization problem can be formulated as a stochastic control problem with only a single control variable, and explicit solutions can be obtained. The second method is to introduce a functional operator to general value functions of stochastic control problem in such a way that the optimal value function in our setting can be given by the limit of a sequence of value functions defined by iterating the operator. Hence the original problem can be deduced to an auxiliary optimization problem, which can be solved as if we were in a single-regime market, which is complete.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy
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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. Comparing single factor stochastic volatility models to constant factor volatility models it seems evident that the stochastic volatility models represent nicely the movement of the asset price and its relations with changes in the risk. However, these models fail to explain the large independent fluctuations in the volatility levels and slope. We consider Chiarella and Ziveyi model, which is a subclass of the model presented in Christoffersen and in paper A, we also explain a multi-factor stochastic volatility model presented in Chiarella and Ziveyi. We review the first-order asymptotic expansion method for determining European option price in such model. Multiscale stochastic volatilities models can capture the smile and skew of volatilities and therefore describe more accurately the movements of the trading prices. In paper B, we provide experimental and numerical studies on investigating the accuracy of the approximation formulae given by this asymptotic expansion. We present also a procedure for calibrating the parameters produced by our first-order asymptotic approximation formulae. Our approximated option prices will be compared to the approximation obtained by Chiarella and Ziveyi. In paper C, we implement and analyze the Regime-Switching GARCH model using real NordPool Electricity spot data. We allow the model parameters to switch between a regular regime and a non-regular regime, which is justified by the so-called structural break behaviour of electricity price series. In splitting the two regimes we consider three criteria, namely the intercountry price di_erence criterion, the capacity/flow difference criterion and the spikes-in-Finland criterion. We study the correlation relationships among these criteria using the mean-square contingency coe_cient and the co-occurrence measure. We also estimate our model parameters and present empirical validity of the 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 that the US three-factor model provides the best description of Australian stock returns. The three US Fama-French factors are statistically significant for the majority of portfolios consisting of large stocks. However, no significant coefficients are found for portfolios in the smallest size quintile. This result initially suggests that the largest firms in the Australian market are globally integrated with the US market while the smallest firms are not. Therefore, the evidence at this point implies domestic segmentation in the Australian market. This is an unsatisfying outcome, considering that the goal of this research is to establish the pricing model that best describes portfolio returns. Given pervasive evidence that liquidity is strongly related to stock returns, the second part of the major analyses derives and incorporates this potentially priced factor to the specified pricing models ... This study also introduces a methodology for individual security analysis, which implements the portfolio analysis, in this part of analyses. The technique makes use of visual impressions conveyed by the histogram plots of coefficients' p-values. A statistically significant coefficient will have its p-values concentrated at below a 5% level of significance; a histogram of p-values will not have a uniform distribution ... The final stage of this study employs daily return data as an examination of what is indeed the best pricing model as well as to provide a robustness check on monthly return results. The daily result indicates that all three US Fama-French factors, namely the US market, size and book-to-market factors as well as LIQT are statistically significant, while the Australian three-factor model only exhibits one significant market factor. This study has discovered that it is in fact the US three-factor model with LIQT and not the domestic model, which qualifies for the criterion of a well-specified asset-pricing model and that it best describes Australian stock returns.
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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, i 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.
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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Książki na temat "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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Emmanuel, Jurczenko, i Maillet Bertrand, red. Multi-moment asset allocation and pricing models. Chichester: John Wiley & Sons, 2006.

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

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

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Części książek na temat "Asset models"

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Ferson, Wayne E. "Asset pricing models". W 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 i Duc Khuong Nguyen. "Asset Pricing Models". W 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". W 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". W 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". W 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, i Regina Y. Liu. "Pricing Models for Financial Assets". W 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". W 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 i Yanhong Wu. "Asset Pricing Theory". W 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". W 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". W 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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Streszczenia konferencji na temat "Asset models"

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Moore, Christine, Martin Tjioe, Anthony Manzella, Kristen L. Sanford Bernhardt i Sue McNeil. "Agent Models for Asset Management". W 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". W 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 i Tak-Kuen Siu. "Asset Allocation under Regime-Switching Models". W 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 i Peter Herrmann. "Security asset elicitation for collaborative models". W the Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2422498.2422505.

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Osladil, Michal, i Libor Kozubik. "Auto-calibration of mathematical asset models: Refflecting change of behavior of energy assets". W 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 i Martin Wollschlaeger. "Network asset models in intelligent field devices". W 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 i Shang-Wen Chang. "Machine Learning in Empirical Asset Pricing Models". W 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 i G. Foster. "APL2 implementation of numerical asset pricing models". W 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 i Timur Aleksandrovich Koshkin. "Methodology for Constructing Simplified Reservoir Models for Integrated Asset Models". W SPE Russian Petroleum Technology Conference. SPE, 2021. http://dx.doi.org/10.2118/206544-ms.

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Streszczenie:
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 and fields as a whole. Models based on the material balance method and two-dimensional proxy models of one-phase flow in porous media are used as simplified reservoir models in the paper. The developed methodology has been successfully tested for four oil and gas fields of Russia, which have different geological and production features: a large field with a long development history and a large number of active wells, a field with low permeability in all pay zones and high scopes of new wells commissioning, a field with a gas cap and high gas/oil ratios (GOR) for individual wells, a field with a complex system of reservoirs and tectonic faults and a large number of multi-pay production wells. For three out of four fields, at the moment, the IAMs have been transferred to commercial operation based on the pilot projects performed and are used by field specialists to solve the following problems: quality analysis of reservoir pressure measurements; assessments of actual reservoir pressure trends by areas; assessments of ineffective injection for areas; prediction of reservoir pressure, water cut and GOR profiles for wells (up to one year) for various prediction scenarios, including optimization scenarios (taking into account the limitations of the material balance method).
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Pak, Charles, i James Cannady. "Asset priority risk assessment using hidden markov models". W the 10th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1631728.1631750.

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Raporty organizacyjne na temat "Asset models"

1

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

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Hansen, Lars Peter, John Heaton i Erzo G. J. Luttmer. Econometric Evaluation of Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, październik 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, kwiecień 1992. http://dx.doi.org/10.3386/w4043.

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

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

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

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

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

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

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

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