Impact of factor models on portfolio risk measures: a structural approach

2012 ◽  
Vol 8 (2) ◽  
pp. 47-79 ◽  
Author(s):  
Marcos Escobar ◽  
Tobias Frielingsdorf ◽  
Rudi Zagst
Keyword(s):  
Risk Measures ◽  
Factor Models ◽  
Structural Approach ◽  
Portfolio Risk

scholarly journals Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

2020 ◽  
Author(s):  
Inés Jiménez ◽  
Andrés Mora-Valencia ◽  
Trino-Manuel Ñíguez ◽  
Javier Perote
Keyword(s):  
Risk Assessment ◽  
Value At Risk ◽  
Risk Measures ◽  
Joint Estimation ◽  
Portfolio Risk ◽  
Conditional Correlation ◽  
Confidence Levels ◽  
Stepwise Procedure ◽  
Parsimonious Model ◽  
Two Step Estimation

The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of Dynamic Equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio on cryptocurrencies by implementing backtesting techniques and for different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. The results support our proposal showing that the SNP-DCC model has better performance for a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.

Contagion effect on bond portfolio risk measures in a hybrid credit risk model

2014 ◽  
Vol 11 (2) ◽  
pp. 131-139 ◽  
Author(s):  
Mathieu Boudreault ◽  
Geneviève Gauthier ◽  
Tommy Thomassin
Keyword(s):  
Credit Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Contagion Effect ◽  
Portfolio Risk ◽  
Bond Portfolio ◽  
Credit Risk Model

scholarly journals A Reformulated Architecture of Cognitive Risks for Psychopathology: Common and Specific Dimensions and Links to Internalizing Outcomes in Adolescence

Assessment ◽  
2018 ◽  
Vol 27 (2) ◽  
pp. 334-355
Author(s):  
Tina H. Schweizer ◽  
Hannah R. Snyder ◽  
Benjamin L. Hankin
Keyword(s):  
Construct Validity ◽  
Factor Model ◽  
Risk Measures ◽  
Community Sample ◽  
Common Factor ◽  
Factor Models ◽  
Dysfunctional Attitudes ◽  
Cognitive Risk ◽  
Factor C ◽  
Depressive Episodes

Multiple cognitive risk products (dysfunctional attitudes [DA], negative inferential style [NIS], self-criticism, dependency, rumination) predict internalizing disorders; however, an optimal structure to assess these risks is unknown. We evaluated the fit, construct validity, and utility of a bifactor, single, and correlated factor model in a community sample of 382 adolescents (age 11-15 years; 59% female). The bifactor, hierarchical single, and correlated factor models all fit well. The bifactor model included a common factor ( c), capturing covariance across all cognitive risk measures, and specific latent factors for DA, NIS, dependency and rumination. Construct validity of these factor structures was evaluated with external validators, including depression and anxious arousal (AA) symptoms, positive affect (PA) and negative affect (NA), and onset of depression diagnostic onset over 2 years. C was associated with higher depression, NA, and AA; lower PA; and predicted depressive episodes. Hierarchical single and correlated factor models also related to external validators.

scholarly journals MODELING LONGEVITY RISK WITH GENERALIZED DYNAMIC FACTOR MODELS AND VINE-COPULAE

2015 ◽  
Vol 46 (1) ◽  
pp. 165-190 ◽  
Author(s):  
Helena Chuliá ◽  
Montserrat Guillén ◽  
Jorge M. Uribe
Keyword(s):  
Factor Model ◽  
Risk Measures ◽  
Mortality Rates ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mortality Risks ◽  
Static Factor ◽  
Vine Copula ◽  
The Uk

AbstractWe present a methodology to forecast mortality rates and estimate longevity and mortality risks. The methodology uses generalized dynamic factor models fitted to the differences in the log-mortality rates. We compare their prediction performance with that of models previously described in the literature, including the traditional static factor model fitted to log-mortality rates. We also construct risk measures using vine-copula simulations, which take into account the dependence between the idiosyncratic components of the mortality rates. The methodology is applied to forecast mortality rates for a population portfolio for the UK and to estimate longevity and mortality risks.

scholarly journals Estimation of the Portfolio Risk from Conditional Value at Risk Using Monte Carlo Simulation

2021 ◽  
Vol 17 (3) ◽  
pp. 370-380
Author(s):  
Ervin Indarwati ◽  
Rosita Kusumawati
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Estimation ◽  
Conditional Value At Risk ◽  
Portfolio Risk ◽  
Portfolio Returns

Portfolio risk shows the large deviations in portfolio returns from expected portfolio returns. Value at Risk (VaR) is one method for determining the maximum risk of loss of a portfolio or an asset based on a certain probability and time. There are three methods to estimate VaR, namely variance-covariance, historical, and Monte Carlo simulations. One disadvantage of VaR is that it is incoherent because it does not have sub-additive properties. Conditional Value at Risk (CVaR) is a coherent or related risk measure and has a sub-additive nature which indicates that the loss on the portfolio is smaller or equal to the amount of loss of each asset. CVaR can provide loss information above the maximum loss. Estimating portfolio risk from the CVaR value using Monte Carlo simulation and its application to PT. Bank Negara Indonesia (Persero) Tbk (BBNI.JK) and PT. Bank Tabungan Negara (Persero) Tbk (BBTN.JK) will be discussed in this study.  The  daily  closing  price  of  each  BBNI  and BBTN share from 6 January 2019 to 30 December 2019 is used to measure the CVaR of the two banks' stock portfolios with this Monte Carlo simulation. The steps taken are determining the return value of assets, testing the normality of return of assets, looking for risk measures of returning assets that form a normally distributed portfolio, simulate the return of assets with monte carlo, calculate portfolio weights, looking for returns portfolio, calculate the quartile of portfolio return as a VaR value, and calculate the average loss above the VaR value as a CVaR value. The results of portfolio risk estimation of the value of CVaR using Monte Carlo simulation on PT. Bank Negara Indonesia (Persero) Tbk and PT. Bank Tabungan Negara (Persero) Tbk at a confidence level of 90%, 95%, and 99% is 5.82%, 6.39%, and 7.1% with a standard error of 0.58%, 0.59%, and 0.59%. If the initial funds that will be invested in this portfolio are illustrated at Rp 100,000,000, it can be interpreted that the maximum possible risk that investors will receive in the future will not exceed Rp 5,820,000, Rp 6,390,000 and Rp 7,100,000 at the significant level 90%, 95%, and 99%

scholarly journals Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: An Application to Cryptocurrencies

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2110
Author(s):  
Inés Jiménez ◽  
Andrés Mora-Valencia ◽  
Trino-Manuel Ñíguez ◽  
Javier Perote
Keyword(s):  
Risk Assessment ◽  
Value At Risk ◽  
Risk Measures ◽  
Joint Estimation ◽  
Portfolio Risk ◽  
Conditional Correlation ◽  
Confidence Levels ◽  
Stepwise Procedure ◽  
Parsimonious Model ◽  
Two Step Estimation

The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of dynamic equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio of cryptocurrencies by implementing backtesting techniques and for different risk measures: value-at-risk, expected shortfall and median shortfall. The results support our proposal showing that the SNP-DCC model has better performance for lower confidence levels than the SNP-DECO model and is more appropriate for portfolio diversification purposes.

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