Tail estimation and mean–VaR portfolio selection in markets subject to financial instability

Consigli, Giorgio

doi:10.1016/s0378-4266(02)00267-4

Cited by 48 publications

(22 citation statements)

References 24 publications

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“…Table B.6 in Appendix B summarizes the interest rates used in the calculation of the debt. If the asset value falls at or below the level of debt D T m , 3 We have observed during estimation that Z …”

Section: Estimation Results and Discussionmentioning

confidence: 99%

“…Moreover, the estimation procedure, provided by Duan [7] and Duan [8] that we will discuss later, requires the likelihood function for the asset process. As demonstrated in Consigli [3], when explicitly using jumps in the model, the likelihood function requires setting the maximum number of jumps in a time interval of interest. In our study, we bypass the necessity of specifying the jump size distribution, jump times or maximum number of jumps by employing an approximation of the shot noise process.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…(A.3) in Appendix A) is much more complicated than the drift term of the GBM model. For example, it is not possible to directly compare the parameter µ (i) of our model and the counterpart of the GBM 3. Also, each parameter of the shot noise model may change from one period to another, so that the final drift in eq.…”

mentioning

confidence: 99%

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An analysis of simultaneous company defaults using a shot noise process

Egami¹,

Kevkhishvili²

2017

Journal of Banking & Finance

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During the subprime mortgage crisis, it became apparent that practical models, such as the one-factor Gaussian copula, had underestimated company default correlations. Complex models that attempt to incorporate default dependency are difficult to implement in practice. In this study, we develop a model for a company asset process, based on which we calculate simultaneous default probabilities using an option-theoretic approach. In our model, a shot noise process serves as the key element for controlling correlations among companies' assets. The risk factor driving the shot noise process is common to all companies in an industry but the shot noise parameters are assumed company-specific; therefore, every company responds differently to this common risk factor. Our model gives earlier warning of financial distress and predicts higher simultaneous default probabilities than commonly used geometric Brownian motion asset model. It is also computationally simple and can be extended to analyze any finite number of companies. $

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Section: Estimation Results and Discussionmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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An analysis of simultaneous company defaults using a shot noise process

Egami¹,

Kevkhishvili²

2017

Journal of Banking & Finance

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“…The core of the MV model is to take the expected return of portfolio as investment return and the variance of expected returns of portfolio as investment risk. According to the MV methodology, for a given specific return rate, one can derive the portfolio whose risk is minimum by minimizing the variance of portfolio; or for a given risk level which investors can tolerate, one can derive the portfolio whose return is maximum by maximizing the expected return of portfolio [14][15][16][17][18]. On the other hand, the DEA introduced by Charnes et al (1978) has gained a wide range of applications measuring comparative efficiency.…”

Section: A the Thought Of Modelmentioning

confidence: 99%

An Approach to Select Pareto-Optimal VC Project Portfolios

2007

2007 International Conference on Wireless Communications, Networking and Mobile Computing

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To solve the problems of current portfolio selection methods of venture capital (VC) projects, resulting from the capital indivisibility and the scrutability of return of investment (ROI) of VC projects, two key concepts of project portfolio preference rate (TPR) and the variance of TPR (VTPR) are defined based on the project evaluation criteria (PEC) for VC projects, using the technical thought of mean-variance (MV) model. On the basis of TPR and VTPR, a model named VCP-DEA/AR, which is able to derive the pareto-optimal project portfolios, is presented by way of the principle of data envelopment analysis (DEA) with assurance region (AR). Applied to an illustrative example, the VCP-DEA/AR model is proved to be scientific, reasonable, and well applicable to real-world portfolio-selection problem of VC.

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“…Until now, VaR is a widely used risk measure and becomes a standard tool of risk management. VaR has also been successfully applied to construct mean-VaR model for portfolio selection, see Alexander and Baptista (2002), Consigli (2002), and Chuang et al (2014) for more details. However, VaR provides non handle on the potential size of the losses beyond the VaR threshold.…”

Section: Introductionmentioning

confidence: 99%

A large CVaR-based portfolio selection model with weight constraints

Zhou

Jiang

et al. 2016

Economic Modelling

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Although the traditional CVaR-based portfolio methods are successfully used in practice, the size of a portfolio with thousands of assets makes optimizing them difficult, if not impossible to solve. In this article we introduce a large CVaR-based portfolio selection method by imposing weight constraints on the standard CVaR-based portfolio selection model, which effectively avoids extreme positions often emerging in traditional methods. We propose to solve the large CVaR-based portfolio model with weight constraints using penalized quantile regression techniques, which overcomes the difficulties of large scale optimization in traditional methods. We illustrate the method via empirical analysis of optimal portfolios on Shanghai and Shenzhen 300 (HS300) index and Shanghai Stock Exchange Composite (SSEC) index of China. The empirical results show that our method is efficient to solve a large portfolio selection and performs well in dispersing tail risk of a portfolio by only using a small amount of financial assets. AbstractAlthough the traditional CVaR-based portfolio methods are successfully used in practice, the size of a portfolio with thousands of assets makes optimizing them difficult, if not impossible to solve. In this article we introduce a large CVaR-based portfolio selection method by imposing weight constraints on the standard CVaR-based portfolio selection model, which effectively avoids extreme positions often emerging in traditional methods. We propose to solve the large CVaR-based portfolio model with weight constraints using penalized quantile regression techniques, which overcomes the difficulties of large scale optimization in traditional methods. We illustrate the method via empirical analysis of optimal portfolios on Shanghai and Shenzhen 300 (HS300) index and Shanghai Stock Exchange Composite (SSEC) index of China. The empirical results show that our method is efficient to solve a large portfolio selection and performs well in dispersing tail risk of a portfolio by only using a small amount of financial assets.

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Tail estimation and mean–VaR portfolio selection in markets subject to financial instability

Cited by 48 publications

References 24 publications

An analysis of simultaneous company defaults using a shot noise process

An analysis of simultaneous company defaults using a shot noise process

An Approach to Select Pareto-Optimal VC Project Portfolios

A large CVaR-based portfolio selection model with weight constraints

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