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Journal articles on the topic 'Asset models'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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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) 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, 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 sensitive to negative shocks to asset values than in standard structural models.

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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 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, no. 1 (February 25, 2009): 17–36. http://dx.doi.org/10.1111/j.1467-629x.1987.tb00233.x.

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5

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. 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, 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 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, 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 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 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 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, 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 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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11

Anson, Mark J. P. "Business Models for Asset Management." Journal of Investing 15, no. 2 (May 31, 2006): 12–18. http://dx.doi.org/10.3905/joi.2006.635624.

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12

Jayeola, Dare. "Evaluation of Asset Allocation Models." NIPES Journal of Science and Technology Research 2, no. 3 (August 31, 2020): 328. http://dx.doi.org/10.37933/nipes/2.3.2020.31.

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13

Magiera, Frank T. "Business Models for Asset Management." CFA Digest 36, no. 4 (November 2006): 94–95. http://dx.doi.org/10.2469/dig.v36.n4.4330.

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14

Schanbacher, Peter. "Averaging Across Asset Allocation Models." Jahrbücher für Nationalökonomie und Statistik 235, no. 1 (February 1, 2015): 61–81. http://dx.doi.org/10.1515/jbnst-2015-0106.

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Summary Combination of asset allocation models is rewarding if (i) the applied risk function is concave and (ii) there is no dominating model. We show that most common risk functions are either concave or at least concave in common applications. In a comprehensive empirical study using standard asset allocation models we find that there is no constantly dominating model. The ranking of the models depends on the data set, the risk function and even changes over time. We find that a simple average of all asset allocation models can outperform each individual model. Our contribution is twofold. We present a theory why the combined model is expected to dominate most individual models. In a comprehensive empirical study we show that model combinations perform exceptionally well in asset allocation.

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15

Lawrence, Edward R., John Geppert, and Arun J. Prakash. "Asset pricing models: a comparison." Applied Financial Economics 17, no. 11 (July 2007): 933–40. http://dx.doi.org/10.1080/09603100600892863.

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16

CHIB, SIDDHARTHA, XIAMING ZENG, and LINGXIAO ZHAO. "On Comparing Asset Pricing Models." Journal of Finance 75, no. 1 (November 21, 2019): 551–77. http://dx.doi.org/10.1111/jofi.12854.

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17

Malevergne, Y., and D. Sornette. "Self-consistent asset pricing models." Physica A: Statistical Mechanics and its Applications 382, no. 1 (August 2007): 149–71. http://dx.doi.org/10.1016/j.physa.2007.02.076.

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18

Mehra, Rajnish. "Consumption-Based Asset Pricing Models." Annual Review of Financial Economics 4, no. 1 (October 2012): 385–409. http://dx.doi.org/10.1146/annurev-financial-102710-144825.

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19

Lamm, Kurt R., Justin D. Delorit, Michael N. Grussing, and Steven J. Schuldt. "Improving Data-Driven Infrastructure Degradation Forecast Skill with Stepwise Asset Condition Prediction Models." Buildings 12, no. 8 (August 22, 2022): 1288. http://dx.doi.org/10.3390/buildings12081288.

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Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) Nearest Neighbor. Model performance was evaluated against BUILDER SMS, the industry-standard asset management database, using data for five roof types on 8549 facilities across 61 U.S. military bases within the United States. The stepwise Weighted Slope model more accurately predicted asset degradation 92% of the time, as compared to the industry standard’s continuous self-correcting prediction model. These results suggest that using historical condition data, alongside or in-place of manufacturer expected service life, may increase the accuracy of degradation and failure prediction models. Additionally, as data quantity increases over time, the models presented are expected to improve prediction skills. The resulting improvements in forecasting enable decision makers to manage facility assets more proactively and achieve better returns on facility investments.

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20

SNOW, KARL N. "Diagnosing Asset Pricing Models Using the Distribution of Asset Returns." Journal of Finance 46, no. 3 (July 1991): 955–83. http://dx.doi.org/10.1111/j.1540-6261.1991.tb03773.x.

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21

Rosenbaum, Mathieu, and Mehdi Tomas. "From microscopic price dynamics to multidimensional rough volatility models." Advances in Applied Probability 53, no. 2 (June 2021): 425–62. http://dx.doi.org/10.1017/apr.2020.60.

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AbstractRough volatility is a well-established statistical stylized fact of financial assets. This property has led to the design and analysis of various new rough stochastic volatility models. However, most of these developments have been carried out in the mono-asset case. In this work, we show that some specific multivariate rough volatility models arise naturally from microstructural properties of the joint dynamics of asset prices. To do so, we use Hawkes processes to build microscopic models that accurately reproduce high-frequency cross-asset interactions and investigate their long-term scaling limits. We emphasize the relevance of our approach by providing insights on the role of microscopic features such as momentum and mean-reversion in the multidimensional price formation process. In particular, we recover classical properties of high-dimensional stock correlation matrices.

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Kato, Takeshi, Yasuyuki Kudo, Hiroyuki Mizuno, and Yoshinori Hiroi. "Regional Inequality Simulations Based on Asset Exchange Models with Exchange Range and Local Support Bias." Applied Economics and Finance 7, no. 5 (July 24, 2020): 10. http://dx.doi.org/10.11114/aef.v7i5.4945.

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To gain insights into the problem of regional inequality, we proposed new regional asset exchange models based on existing kinetic income-exchange models in economic physics. We did this by setting the spatial exchange range and adding bias to asset fraction probability in equivalent exchanges. Simulations of asset distribution and Gini coefficients showed that suppressing regional inequality requires, firstly an increase in the intra-regional economic circulation rate, and secondly the narrowing down of the exchange range (inter-regional economic zone). However, avoiding over-concentration of assets due to repeat exchanges requires adding a third measure; the local support bias (distribution norm). A comprehensive solution incorporating these three measures enabled shifting the asset distribution from over-concentration to exponential distribution and eventually approaching the normal distribution, reducing the Gini coefficient further. Going forward, we will expand these models by setting production capacity based on assets, path dependency on two-dimensional space, bias according to disparity, and verify measures to reduce regional inequality in actual communities.

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Marushkevych, Dmytro, and Yevheniia Munchak. "Estimation of Parameters and Verification of Statistical Hypotheses for Gaussian Models of Stock Price." Lietuvos statistikos darbai 55, no. 1 (December 20, 2016): 91–101. http://dx.doi.org/10.15388/ljs.2016.13871.

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We construct models of asset prices on the Ukrainian stock market and analyse their applicability by checkingappropriate statistical hypotheses using actual observed data. We also analyse the presence of jumps in the dynamics ofdifferent assets and estimate the Hurst coefficient for the logarithm of the price of the asset by two different methods.

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Kaplan, Greg, and Giovanni L. Violante. "The Marginal Propensity to Consume in Heterogeneous Agent Models." Annual Review of Economics 14, no. 1 (August 12, 2022): 747–75. http://dx.doi.org/10.1146/annurev-economics-080217-053444.

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What model features and calibration strategies yield a large average marginal propensity to consume (MPC) in heterogeneous agent models? Through a systematic investigation of models with different preferences, dimensions of ex-ante heterogeneity, income processes, and asset structures, we show that the most important factor is the share and type of hand-to-mouth households. One-asset models either feature a trade-off between a high average MPC and a realistic level of aggregate wealth or generate an excessively polarized wealth distribution that vastly understates the wealth held by households in the middle of the distribution. Two-asset models that include both liquid and illiquid assets can resolve this tension with a large enough gap between liquid and illiquid returns. We discuss how such return differential can be justified from the perspective of theory and data.

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Popovic, Zoran. "Pareto’s optimum in models of general economic equilibrium with the asset market." Ekonomski anali 52, no. 173 (2007): 36–84. http://dx.doi.org/10.2298/eka0773036p.

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A model of the general economic equilibrium of sequential structures includes the asset market, where assets are instruments of sequential income redistribution. The model should explain relative prices of commodities, on one hand, and establish the asset pricing as an instrument of income redistribution, on the other, enabling the analysis of sequential income transfers. This paper mainly researches Pareto?s optimum of a defined mathematical model of the general economic equilibrium in both complete and incomplete asset markets. The existence of the latter partly disables an economic system to transfer income through time sequences properly, which results in equilibrium allocations not reaching Pareto?s optimum. .

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Robison, Lindon J., and Peter J. Barry. "Accrual income statements and present value models." Agricultural Finance Review 80, no. 5 (June 22, 2020): 715–31. http://dx.doi.org/10.1108/afr-11-2019-0123.

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PurposeThis paper demonstrates that present value (PV) models can be viewed as multiperiod extensions of accrual income statements (AISs). Failure to include AIS details in PV models may lead to inaccurate estimates of earnings and rates of return on assets and equity and inconsistent rankings of mutually exclusive investments. Finally, this paper points out that rankings based on assets and equity earnings and rates of return need not be consistent, requiring financial managers to consider carefully the questions they expect PV models to answer.Design/methodology/approachAISs are used to guide the construction of PV models. Numerical examples illustrate the results. Deductions from AIS definitions demonstrate the potential conflict between asset and equity earnings and rates of return.FindingsPV models can be viewed as multiperiod extensions of AISs. Mutually exclusive rankings based on assets and equity earnings and rates of return need not be consistent.Research limitations/implicationsPV models are sometimes constructed without the details included in AISs. The result of this simplified approach to PV model construction is that earnings and rates of return may be miscalculated and rankings based as asset and equity earnings and rates of return are inconsistent. Tax adjustments for asset and equity earnings may be miscalculated in applied models.Practical implicationsThis paper provides guidelines for properly constructing PV models consistent with AISs.Social implicationsPV models are especially important for small to medium size firms that characterize much of agricultural. Providing a model consistent with AIS construction principles should help financial managers view the linkage between building financial statements and investment analysis.Originality/valueThis is the first paper to develop the idea that the PV model can be viewed as a multiperiod extension of an AIS.

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Mohammad Salameh, Hussein. "Application of asset pricing models: evidence from Saudi exchange." Investment Management and Financial Innovations 17, no. 1 (April 6, 2020): 348–68. http://dx.doi.org/10.21511/imfi.17(1).2020.29.

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The Saudi Arabia Stock Exchange (Tadawul) is one of the biggest emerging Stock Exchanges in the Middle East region. Therefore, this research aims to apply Fama and French (2015) 5-factor model on Tadawul, and compares it with the Fama and French 3-factor model and CAPM to check the applicability of the models in Tadawul and the identity of the factors that can affect stock returns. Furthermore, the Generalized Method of Moments (GMM) regression has been implemented to examine the impact between the variables in the models. Empirically, the results show that Fama and French (2015) 5-factor model is the most consistent model in comparison to the other two models in terms of explaining the cross-section of average stock returns in Tadawul. However, it is not the best according to the intercepts results of all the regressions in 2x3, 2x2, or 2x2x2x2 sorts. Besides, Fama and French (2015) 5-factor model has the highest explanatory power in most of the portfolios based on the adjusted R2 regardless of the sort (2x3, 2x2, or 2x2x2x2). Finally, the results conclude that Fama and French (2015) 5-factor model can be an applicable model in Tadawul but only market and size can affect the stock returns, while the value, profitability, and investment cannot. Accordingly, the author recommends that, as a continuation of this research, further research can be done, which investigates a model with additional factors like momentum and illiquidity.

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Cairns, Andrew. "Some Notes on the Dynamics and Optimal Control of Stochastic Pension Fund Models in Continuous Time." ASTIN Bulletin 30, no. 1 (May 2000): 19–55. http://dx.doi.org/10.2143/ast.30.1.504625.

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AbstractThis paper discusses the modelling and control of pension funds.A continuous-time stochastic pension fund model is proposed in which there are n risky assets plus the risk-free asset as well as randomness in the level of benefit outgo. We consider Markov control strategies which optimise over the contribution rate and over the range of possible asset-allocation strategies.For a general (not necessarily quadratic) loss function it is shown that the optimal proportions of the fund invested in each of the risky assets remain constant relative to one another. Furthermore, the asset allocation strategy always lies on the capital market line familiar from modern portfolio theory.A general quadratic loss function is proposed which provides an explicit solution for the optimal contribution and asset-allocation strategies. It is noted that these solutions are not dependent on the level of uncertainty in the level of benefit outgo, suggesting that small schemes should operate in the same way as large ones. The optimal asset-allocation strategy, however, is found to be counterintuitive leading to some discussion of the form of the loss function. Power and exponential loss functions are then investigated and related problems discussed.The stationary distribution of the process is considered and optimal strategies compared with dynamic control strategies.Finally there is some discussion of the effects of constraints on contribution and asset-allocation strategies.

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Solórzano-Taborga, Pablo, Ana Belén Alonso-Conde, and Javier Rojo-Suárez. "Data Envelopment Analysis and Multifactor Asset Pricing Models." International Journal of Financial Studies 8, no. 2 (April 17, 2020): 24. http://dx.doi.org/10.3390/ijfs8020024.

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Recent literature shows that market anomalies have significantly diminished, while research on market factors has largely improved the performance of asset pricing models. In this paper we study the extent to which data envelopment analysis (DEA) techniques can help improve the performance of multifactor models. Specifically, we test the explanatory power of the Fama and French three-factor model, combined with an additional factor based on DEA, on a sample of 2101 European equity funds, for the period from 2001 to 2016. Accordingly, we first form the fund portfolios that constitute our test assets and create the efficiency factor. Secondly, we estimate the prices of risk tied to the four factors using ordinary least squares (OLS) on a two-stage cross-sectional regression. Finally, we use the R-squared statistic estimated by generalized least squares (GLS), as well as the Gibbons Ross and Shanken test and the J-test for overidentifying restrictions in order to study the performance of the model, including and omitting the efficiency factor. The results show that the efficiency factor improves the performance of the model and reduces the pricing errors of the assets under consideration, which allows us to conclude that the efficiency index may be used as a factor in asset pricing models.

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MEINERDING, CHRISTOPH. "ASSET ALLOCATION AND ASSET PRICING IN THE FACE OF SYSTEMIC RISK: A LITERATURE OVERVIEW AND ASSESSMENT." International Journal of Theoretical and Applied Finance 15, no. 03 (May 2012): 1250023. http://dx.doi.org/10.1142/s0219024912500239.

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This paper provides a detailed overview of the current research linking systemic risk, financial crises and contagion effects among assets on the one hand with asset allocation and asset pricing theory on the other hand. Based on the ample literature about definitions, measurement and properties of systemic risk, we derive some elementary ingredients for models of financial contagion and assess the current state of knowledge about asset allocation and asset pricing with explicit focus on systemic risk. The paper closes with a brief outlook on future research possibilities and some recommendations for the further development of capital market models incorporating financial contagion.

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Swanson, Eric T. "Risk Aversion and the Labor Margin in Dynamic Equilibrium Models." American Economic Review 102, no. 4 (June 1, 2012): 1663–91. http://dx.doi.org/10.1257/aer.102.4.1663.

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The household's labor margin has a substantial effect on risk aversion, and hence asset prices, in dynamic equilibrium models even when utility is additively separable between consumption and labor. This paper derives simple, closed-form expressions for risk aversion that take into account the household's labor margin. Ignoring this margin can dramatically overstate the household's true aversion to risk. Risk premia on assets priced with the stochastic discount factor increase essentially linearly with risk aversion, so measuring risk aversion correctly is crucial for asset pricing in the model.

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Abraham, Rebecca, and Zhi Tao. "The Valuation of Cryptocurrencies in Single-Asset and Multiple-Asset Models." Theoretical Economics Letters 09, no. 04 (2019): 1093–113. http://dx.doi.org/10.4236/tel.2019.94071.

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Korbel, Jakob J., Umar H. Siddiq, and Rüdiger Zarnekow. "Towards Virtual 3D Asset Price Prediction Based on Machine Learning." Journal of Theoretical and Applied Electronic Commerce Research 17, no. 3 (July 7, 2022): 924–48. http://dx.doi.org/10.3390/jtaer17030048.

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Although 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual 3D asset price prediction tool based on the analysis results. The evaluation revealed that the random forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore, the findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. However, the analysis also showed that the pricing behavior is still substantially influenced by the subjective assessment of virtual 3D asset creators.

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Bharat, Chrianna I., Kevin Murray, Edward Cripps, and Melinda R. Hodkiewicz. "Methods for displaying and calibration of Cox proportional hazards models." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 1 (November 26, 2017): 105–15. http://dx.doi.org/10.1177/1748006x17742779.

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Cox proportional hazards modelling is a widely used technique for determining relationships between observed data and the risk of asset failure when model performance is satisfactory. Cox proportional hazards models possess good explanatory power and are used by asset managers to gain insight into factors influencing asset life. However, validation of Cox proportional hazards models is not straightforward and is seldom considered in the maintenance literature. A comprehensive validation process is a necessary foundation to build trust in the failure models that underpin remaining useful life prediction. This article describes data splitting, model discrimination, misspecification and fit methods necessary to build trust in the ability of a Cox proportional hazards model to predict failures on out-of-sample assets. Specifically, we consider (1) Prognostic Index comparison for training and test sets, (2) Kaplan–Meier curves for different risk bands, (3) hazard ratios across different risk bands and (4) calibration of predictions using cross-validation. A Cox proportional hazards model on an industry data set of water pipe assets is used for illustrative purposes. Furthermore, because we are dealing with a non-statistical managerial audience, we demonstrate how graphical techniques, such as forest plots and nomograms, can be used to present prediction results in an easy to interpret way.

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Cummins, J. David. "Asset Pricing Models and Insurance Ratemaking." ASTIN Bulletin 20, no. 2 (November 1990): 125–66. http://dx.doi.org/10.2143/ast.20.2.2005438.

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AbstractThis paper provides an introduction to asset pricing theory and its applications in non-life insurance. The first part of the paper presents a basic review of asset pricing models, including discrete and continuous time capital asset pricing models (the CAPM and ICAPM), arbitrage pricing theory (APT), and option pricing theory (OPT). The second part discusses applications in non-life insurance. Among the insurance models reviewed are the insurance CAPM, discrete time discounted cash flow models, option pricing models, and more general continuous time models. The paper concludes that the integration of actuarial and financial theory can provide major advances in insurance pricing and financial management.

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Vu, Joseph D. "Portfolio Selection and Asset Pricing Models." CFA Digest 30, no. 4 (November 2000): 56–57. http://dx.doi.org/10.2469/dig.v30.n4.774.

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Ferrando, Sebastian, Andrew Fleck, Alfredo Gonzalez, and Alexey Rubtsov. "Trajectorial asset models with operational assumptions." Quantitative Finance and Economics 3, no. 4 (2019): 661–708. http://dx.doi.org/10.3934/qfe.2019.4.661.

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Pástor, Ľuboš. "Portfolio Selection and Asset Pricing Models." Journal of Finance 55, no. 1 (February 2000): 179–223. http://dx.doi.org/10.1111/0022-1082.00204.

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Jacquier, Eric, and Alan J. Marcus. "Asset Allocation Models and Market Volatility." Financial Analysts Journal 57, no. 2 (March 2001): 16–30. http://dx.doi.org/10.2469/faj.v57.n2.2430.

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Constantinides, George M. "Asset Pricing: Models and Empirical Evidence." Journal of Political Economy 125, no. 6 (December 2017): 1782–90. http://dx.doi.org/10.1086/694621.

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Gordon, Stephen, and Lucie Samson. "Comparing Consumption-Based Asset-Pricing models." Canadian Journal of Economics/Revue Canadienne d`Economique 35, no. 3 (August 2002): 586–610. http://dx.doi.org/10.1111/1540-5982.00147.

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Barone Adesi, Giovanni, Patrick Gagliardini, and Giovanni Urga. "Testing Asset Pricing Models With Coskewness." Journal of Business & Economic Statistics 22, no. 4 (October 1, 2004): 474–85. http://dx.doi.org/10.1198/073500104000000244.

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FILIPOVIĆ, DAMIR, LANE P. HUGHSTON, and ANDREA MACRINA. "CONDITIONAL DENSITY MODELS FOR ASSET PRICING." International Journal of Theoretical and Applied Finance 15, no. 01 (February 2012): 1250002. http://dx.doi.org/10.1142/s0219024912500021.

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We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional probability density process for the value of an asset at some specified time in the future. In the case where the price process is driven by Brownian motion, an associated "master equation" for the dynamics of the conditional probability density is derived and expressed in integral form. By a "model" for the conditional density process we mean a solution to the master equation along with the specification of (a) the initial density, and (b) the volatility structure of the density. The volatility structure is assumed at any time and for each value of the argument of the density to be a functional of the history of the density up to that time. In practice one specifies the functional modulo sufficient parametric freedom to allow for the input of additional option data apart from that implicit in the initial density. The scheme is sufficiently flexible to allow for the input of various types of data depending on the nature of the options market and the class of valuation problem being undertaken. Various examples are studied in detail, with exact solutions provided in some cases.

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MACRINA, ANDREA. "HEAT KERNEL MODELS FOR ASSET PRICING." International Journal of Theoretical and Applied Finance 17, no. 07 (November 2014): 1450048. http://dx.doi.org/10.1142/s0219024914500484.

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A heat kernel approach is proposed for the development of a novel method for asset pricing over a finite time horizon. We work in an incomplete market setting and assume the existence of a pricing kernel that determines the prices of financial instruments. The pricing kernel is modeled by a weighted heat kernel driven by a multivariate Markov process. The heat kernel is chosen so as to provide enough freedom to ensure that the resulting model can be calibrated to appropriate data, e.g. to the initial term structure of bond prices. A class of models is presented for which the prices of bonds, caplets, and swaptions can be computed in closed form. The dynamical equations for the price processes are derived, and explicit formulae are obtained for the short rate of interest, the risk premium, and for the stochastic volatility of prices. Several of the closed-form models presented are driven by combinations of Markovian jump processes with different probability laws. Such models provide a basis for consistent applications in various market sectors, including equity markets, fixed-income markets, commodity markets, and insurance. The flexible multidimensional and multivariate structure on which the resulting price models are based lends itself well to the modeling of dependence across asset classes. As an illustration, the impact of spiraling debt, a typical feature of a financial crisis, is modeled explicitly, and the contagion effects can be readily observed in the dynamics of the associated asset returns.

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Velu, Raja, and Guofu Zhou. "Testing multi-beta asset pricing models." Journal of Empirical Finance 6, no. 3 (September 1999): 219–41. http://dx.doi.org/10.1016/s0927-5398(99)00002-x.

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Zin, Stanley E. "Are behavioral asset-pricing models structural?" Journal of Monetary Economics 49, no. 1 (January 2002): 215–28. http://dx.doi.org/10.1016/s0304-3932(01)00101-5.

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Turtle, Harry J. "Temporal dependence in asset pricing models." Economics Letters 45, no. 3 (January 1994): 361–66. http://dx.doi.org/10.1016/0165-1765(94)90038-8.

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Karatzas, Ioannis, John P. Lehoczky, and Steven E. Shreve. "Equilibrium Models With Singular Asset Prices." Mathematical Finance 1, no. 3 (July 1991): 11–29. http://dx.doi.org/10.1111/j.1467-9965.1991.tb00013.x.

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Hansen, Lars Peter, John Heaton, and Erzo G. J. Luttmer. "Econometric Evaluation of Asset Pricing Models." Review of Financial Studies 8, no. 2 (April 1995): 237–74. http://dx.doi.org/10.1093/rfs/8.2.237.

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Kelly, Bryan, and Alexander Ljungqvist. "Testing Asymmetric-Information Asset Pricing Models." Review of Financial Studies 25, no. 5 (January 5, 2012): 1366–413. http://dx.doi.org/10.1093/rfs/hhr134.

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