scholarly journals New copulas based on general partitions-of-unity (part III) — the continuous case

2019 ◽  
Vol 7 (1) ◽  
pp. 181-201 ◽  
Author(s):  
Dietmar Pfeifer ◽  
Andreas Mändle ◽  
Olena Ragulina ◽  
Côme Girschig
Keyword(s):  
Risk Management ◽  
Natural Extension ◽  
Simple Algorithm ◽  
Tail Dependence ◽  
Partition Of Unity ◽  
Solvency Ii ◽  
Continuous Case ◽  
High Dimensional ◽  
Data Sets ◽  
Empirical Copula

AbstractIn this paper we discuss a natural extension of infinite discrete partition-of-unity copulas which were recently introduced in the literature to continuous partition of copulas with possible applications in risk management and other fields. We present a general simple algorithm to generate such copulas on the basis of the empirical copula from high-dimensional data sets. In particular, our constructions also allow for an implementation of positive tail dependence which sometimes is a desirable property of copula modelling, in particular for internal models under Solvency II.

scholarly journals New copulas based on general partitions-of-unity and their applications to risk management

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Dietmar Pfeifer ◽  
Hervé Awoumlac Tsatedem ◽  
Andreas Mändle ◽  
Côme Girschig
Keyword(s):  
Risk Management ◽  
Tail Dependence ◽  
Partition Of Unity ◽  
Real Data ◽  
Finite Partition

AbstractWe construct new multivariate copulas on the basis of a generalized infinite partition-of-unity approach. This approach allows, in contrast to finite partition-of-unity copulas, for tail-dependence as well as for asymmetry. A possibility of fitting such copulas to real data from quantitative risk management is also pointed out.

Diversifying systemic risk in agriculture

2016 ◽  
Vol 76 (4) ◽  
pp. 512-531 ◽  
Author(s):  
Xiaoguang Feng ◽  
Dermot Hayes
Keyword(s):  
Crop Yield ◽  
Systemic Risk ◽  
Crop Insurance ◽  
Tail Dependence ◽  
Model Potential ◽  
High Dimensional ◽  
Computationally Efficient ◽  
Content Type ◽  
Insurance Portfolio ◽  
Systemic Nature

Purpose Portfolio risk in crop insurance due to the systemic nature of crop yield losses has inhibited the development of private crop insurance markets. Government subsidy or reinsurance has therefore been used to support crop insurance programs. The purpose of this paper is to investigate the possibility of converting systemic crop yield risk into “poolable” risk. Specifically, this study examines whether it is possible to remove the co-movement as well as tail dependence of crop yield variables by enlarging the risk pool across different crops and countries. Design/methodology/approach Hierarchical Kendall copula (HKC) models are used to model potential non-linear correlations of the high-dimensional crop yield variables. A Bayesian estimation approach is applied to account for estimation risk in the copula parameters. A synthetic insurance portfolio is used to evaluate the systemic risk and diversification effect. Findings The results indicate that the systemic nature – both positive correlation and lower tail dependence – of crop yield risks can be eliminated by combining crop insurance policies across crops and countries. Originality/value The study applies the HKC in the context of agricultural risks. Compared to other advanced copulas, the HKC achieves both flexibility and parsimony. The flexibility of the HKC makes it appropriate to precisely represent various correlation structures of crop yield risks while the parsimony makes it computationally efficient in modeling high-dimensional correlation structure.

MOTIVES FOR CORPORATE HEDGING: EVIDENCE FROM THE UK

2005 ◽  
Vol 01 (01) ◽  
pp. 0550003 ◽  
Author(s):  
EPHRAIM CLARK ◽  
AMRIT JUDGE
Keyword(s):  
Risk Management ◽  
Financial Distress ◽  
Economies Of Scale ◽  
Foreign Currency ◽  
Annual Reports ◽  
Data Sets ◽  
Growth Options ◽  
Corporate Hedging ◽  
Currency Exposure ◽  
The Uk

In this paper, we use survey data and data from annual reports to identify the determinants of hedging activity of United Kingdom (UK) firms in the context of an overall program of risk management. Comparing the two sets of data makes it possible to identify misclassified firms, that is, firms whose hedging claims are not consistent across the two data sets. Our results on the consistent data show that the likelihood of hedging is related to growth options, foreign currency exposure, liquidity and economies of scale in hedging costs. Contrary to many previous US studies, we also find strong evidence linking the decision to hedge and the expected costs of financial distress. Results for the misclassified firms suggest that they are actually hedgers that hedge less extensively than the correctly classified (CC) hedgers.

Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality

2018 ◽  
Vol 30 (12) ◽  
pp. 3281-3308
Author(s):  
Hong Zhu ◽  
Li-Zhi Liao ◽  
Michael K. Ng
Keyword(s):  
Dimensionality Reduction ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Main Idea ◽  
Real Data ◽  
Learning Performance ◽  
High Dimensional ◽  
Data Sets ◽  
Outer Loop ◽  
Orthogonality Constraints

We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and inertial proximal alternating linearized minimization (iPALM) iterations as the inner loop. The main advantage of this method is that both sparsity and orthogonality can be satisfied in the proposed algorithm. We show the global convergence of the proposed iterative algorithm. We also demonstrate that the proposed algorithm can achieve high sparsity and orthogonality requirements, which are very important for dimensionality reduction. Experimental results on both synthetic and real data sets show that the proposed algorithm can obtain learning performance comparable to that of other tested MI learning algorithms.

Enterprise risk management and Solvency II: the system of governance and the Own Risk and Solvency Assessment

2020 ◽  
Vol 21 (4) ◽  
pp. 317-332 ◽  
Author(s):  
Pablo Durán Santomil ◽  
Luis Otero González
Keyword(s):  
Risk Management ◽  
Insurance Industry ◽  
Solvency Ii ◽  
Enterprise Risk Management ◽  
Insurance Companies ◽  
Legal Form ◽  
Content Type ◽  
Enterprise Risk ◽  
Solvency Assessment

Purpose The purpose of this paper is to analyze how enterprise risk management (ERM), the system of governance and the Own Risk and Solvency Assessment (ORSA) have been boosted with the entry of Solvency II. Design/methodology/approach For this analysis, the authors have undertaken a survey of chief risk officers (CROs) working in Spanish insurance companies. Findings The results show that Solvency II has definitely promoted ERM in the European insurance industry and improved the system of governance of the insurance companies, and that the perceived value of the ORSA for the companies is higher than the cost. It is clear that the quality of ERM implemented by companies is higher in those that face more complex risks and with greater interdependencies – that is, larger companies, foreign insurers and insurers with several lines of business – but is unaffected by the legal form of the entity (mutual/corporation). Originality/value This study conducts primary research with surveys of CROs and develops a measure of the quality of ERM implemented by insurance companies.

scholarly journals New copulas based on general partitions-of-unity and their applications to risk management (part II)

2017 ◽  
Vol 5 (1) ◽  
pp. 246-255 ◽  
Author(s):  
Dietmar Pfeifer ◽  
Andreas Mändle ◽  
Olena Ragulina
Keyword(s):  
Risk Management ◽  
Negative Binomial ◽  
Partition Of Unity ◽  
Data Driven ◽  
Asymmetric Data ◽  
Arbitrary Dimensions

Abstract We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature. In particular, we consider negative binomial and Poisson copulas and present a solution to the problem of fitting such copulas to highly asymmetric data in arbitrary dimensions.

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