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An extended generalized Markov model for the spread risk and its calibration by using filtering techniques in Solvency II framework
Full text linkThe Solvency II regulatory regime requires the calculation of a capital requirement, the Solvency Capital Requirement (SCR), for the insurance and reinsurance companies, that is based on a market-consistent evaluation of the Basic Own Funds probability distribution forecast over a one-year time horizon. This work proposes an extended generalized Markov model for rating-based pricing of risky securities for spread risk assessment and management within the Solvency II framework, under an internal model or partial internal model. This model is based on Jarrow, Lando and Turnbull (1997), Lando (1998) and Gambaro et al. (2018) and models the credit rating transitions and the default process using an extension of a time-homogeneous Markov chain and two subordinator processes. This approach allows simultaneous modeling of credit spreads for different rating classes and credit spreads to fluctuate randomly even when the rating does not change.
The estimation methodologies used in this work are consistent with the scope of the work and the scope of the proposed model, i.e., pricing of defaultable bonds and calculation of SCR for the spread risk sub-module, and with the market-consistency principle required by Solvency II. For this purpose, estimation techniques on time series known as filtering techniques are used, which allow the model parameters to be jointly estimated under both the real-world probability measure (necessary for risk assessment) and the risk-neutral probability measure (necessary for pricing). Specifically, an appropriate set of time series of credit spread term structures, differentiated by economic sector and rating class, is used.
The proposed model, in its final version, returns excellent results in terms of goodness of fit to historical data, and the projected data are consistent with historical data and the Solvency II framework.
The filtering techniques, in the different configurations used in this work (particle filtering with Gauss-Legendre quadrature techniques, particle filtering with
Sequential Importance Resampling algorithm, Kalman filter), were found to be an effective and flexible tool for estimating the models proposed, able to handle the high computational complexity of the problem addressed
Efficient Algorithms for Mean-Variance Portfolio Optimization with Hard Real-World Constraints
No full textPortfolio selection problems in practice: a comparison between linear and quadratic optimization models
No full textDoes Greater Diversification Really Improve Performance in Portfolio Selection?
No full textOne of the fundamental principles in portfolio selection models is minimization of risk through diversification of the investment. This seems to require that in a given working universe, or market, the investment should be spread among all (or almost all) the available assets. Indeed, this is what some classical investment strategies, like Equally-Weighted portfolios, or more recent and refined ones, like Risk Parity, actually recommend.
The purpose of this work consists in giving some empirical evidence of the fact that diversifying through the use of larger portfolios is not the best way to achieve an improvement in out-of-sample performance. More precisely, we investigate the role of the restriction on the number of assets in a portfolio (a cardinality constraint) on the in-sample and out-of-sample outcomes of the Equally-Weighted approach and of some well-known portfolio selection models that minimize risk through the use of Variance, Semi-Mean Absolute Deviation, and Conditional Value-at-Risk.
Our empirical analysis is based on some new and on some publicly available benchmark data sets often used in the literature
A Linear Risk-Return Model for Enhanced Indexation
No full textIn this paper, we propose a linear bi-objective optimization model for enhanced indexation by maximizing average excess return and reducing underperformance over an observation period. The efficient frontier for this problem can be easily computed with standard LP solvers. Results are presented for well-known financial data sets where portfolios selected by our model exhibit several useful properties
Differential assay and biological significance of poly(ADP-ribose) polymerase activity in isolated liver nuclei.
No full textDetermination of poly(ADP-ribose) polymerase levels in blood lymphocytes: relevance to genotoxic exposure of humans
No full textIn vitro effect of 3,5,3’-triiodothyronine on poly(ADP-ribosyl)ation of DNA Topoisomerase I
No full textRisk Bounding is better then Risk Parity for portfolio selection
No full textRisk Parity (RP), also called equally weighted risk contribution, is a recent approach to risk diversification in portfolio selection. RP is based on the principle that the fractions of the capital invested in each asset should be chosen so as to make the total risk contributions of all assets equal among them.
We show here that the Risk Parity approach is theoretically dominated by an alternative similar approach that does not actually require equally weighted risk contribution of all assets but only an equal upper bound on all such risks. We call it the Equal Risk Bounding (ERB) approach.
This alternative approach might, and actually does in some cases, select portfolios that do not contain all assets and where the risk contributions of all assets is strictly smaller than in the RP portfolio.
We prove some relations between the solutions of the ERB and of the RP models and we use such relations to provide a finite method for finding an ERB portfolio. In the case of equal correlation, a closed form solution to the ERB model is also provided.
Some numerical examples illustrate the advantages of the ERB approach over the RP approach
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