Value-at-Risk dynamics: a copula-VAR approach

Luca, Giovanni De; Rivieccio, Giorgia; Corsaro, Stefania

doi:10.1080/1351847x.2019.1652665

Cited by 9 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result showed that a mixed R-vine copula was appropriate to model the complicated dependence structure of oil price. In line with this study, De Luca et al (2020) suggested that the copula approach with the time-varying model is highly competitive and provides better specification on the VaR in terms of the loss function.…”

Section: Literature Reviewsupporting

confidence: 62%

Quantifying Foreign Exchange Risk in the Selected Listed Sectors of the Johannesburg Stock Exchange: An SV-EVT Pairwise Copula Approach

Eita

Djemo

2022

IJFS

View full text Add to dashboard Cite

This paper attempted to apply an EVT-based pairwise copula method for modelling risk interaction between foreign exchange rates and equity indices of the Johannesburg Stock Exchange (JSE) and to model the dependence structure of the underlying assets with some selected listed stock indices. We filtered the return residuals using the stochastic volatility and GJR-GARCH (1,1) models with different distributions, and we selected the best-fitted model in the GARCH framework. We applied the peaks-over-threshold (POT) method to the filtered residuals to fit it by the generalised Pareto distribution (GPD), and we used the vine copula to model the co-movement between foreign exchange rates and equity indices and value at risk (VaR) for risk quantification. We used three exchange rates (USD, GDP, and EUR) against the South African rand (ZAR) and six industry indices (banking, life insurance, non-life insurance, leisure, telecommunications, and mining). Our empirical findings show that the GJR-GARCH with Student’s t-distribution, combined with a regular (R)-vine copula, outperforms the alternatives models. Dependence structure analysis reveals a strong co-dependency between the stock from the financial industry and foreign exchange rates. The results also show that VaR-based R-vine copula outperforms the model compared to VaR-based D-vine and C-vine before the COVID-19 outbreak, while the D-vine copula produced appears to be the most suitable risk model specification for quantifying risk during the COVID-19 pandemic. Therefore, VaR-based R-vine copula is suitable for risk quantification, while GJR-GARCH with Student’s t-distribution produces better results in the GARCH framework. Further, we find that equity indices and foreign exchange rates exhibit higher tail risk contagion during the COVID-19 pandemic, with the non-life-insurance and telecommunications sectors appearing to be the investor’s safe haven among the listed sectors of the JSE. Our results will help South African investors seek risk-adjusted returns to substantially reduce the hedging cost of potential loss due to the misspecification of a risk model and make an investment decision during the global health crisis.

show abstract

Section: Literature Reviewsupporting

confidence: 62%

Quantifying Foreign Exchange Risk in the Selected Listed Sectors of the Johannesburg Stock Exchange: An SV-EVT Pairwise Copula Approach

Eita

Djemo

2022

IJFS

View full text Add to dashboard Cite

show abstract

“…After having estimated for each bank and for the system the univariate discrete-time dynamic volatility model defined in equation ( 2.3), we extract the innovations and estimate different dependence structures. We consider the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic (MGH) model, as described in Bianchi et al (2019), and the best copula function in terms of AIC among normal, t, BB1 and BB7 copulas, as described in De Luca and Rivieccio (2018) and De Luca et al (2019). For the MNTS and MGH models we estimate a 13-dimensional model by using an ad-hoc procedure implemented in R considering an expectation-maximization maximum-likelihood approach.…”

Section: Methodsmentioning

confidence: 99%

“…More in details, we assume that the univariate time series have AR-GARCH dynamics with Glosten-Jagannathan-Runkle (GJR) volatility (see Glosten et al (1993)) and then we analyze different dependence structures. Among possible multivariate parametric models applied to finance (see Bianchi et al ( 2020)), we select the multivariate normal tempered stable (MNTS) and the multivariate generalized hyperbolic (MGH) model, and four copula functions, normal, t and BB1 and BB7, as described in Jaworski (2017), De Luca and Rivieccio (2018) and De Luca et al (2019). Both non-normal multivariate distributions and copula functions are widely known in the financial literature.…”

Section: Introductionmentioning

confidence: 99%

CoVaR with volatility clustering, heavy tails and non-linear dependence

Bianchi,

De Luca,

Rivieccio

2020

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper we estimate the conditional value-at-risk by fitting different multivariate parametric models capturing some stylized facts about multivariate financial time series of equity returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is got by AR-GARCH dynamics of the GJR type, the other stylized facts are captured through non-Gaussian multivariate models and copula functions. The CoVaR ≤ is computed on the basis on the multivariate normal model, the multivariate normal tempered stable (MNTS) model, the multivariate generalized hyperbolic model (MGH) and four possible copula functions. These risk measure estimates are compared to the CoVaR = based on the multivariate normal GARCH model. The comparison is conducted by backtesting the competitor models over the time span from January 2007 to March 2020. In the empirical study we consider a sample of listed banks of the euro area belonging to the main or to the additional global systemically important banks (GSIBs) assessment sample.

show abstract

“…tail index estimation, as well as backtesting validation of the VaR model application in investment processes, e.g. Ahmad et al (2019); Bekiros et al (2019);De Luca et al (2020); Jia et al (2018); Kratz et al (2018). This topic is even more crucial in emerging markets with their inherent abrupt changes in volatility regimes, especially characterized by lower liquidity, frequent internal and external shocks (Zikovic & Aktan, 2009).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Modelling Risk under Volatile Conditions: Tail Index Estimation and Validation

Djakovic

Ivetić

Andjelic

2021

View full text Add to dashboard Cite

The subject of the research is to analyse and evaluate methods of investment risk modelling in dynamic, changing market circumstances, with a special focus on the assessment success of the expected effects of investment activities in ’extreme’ return points. In that sense, different Value at Risk models were used: the Historical Simulation (HS VaR), the Delta Normal VaR (D VaR) and the Extreme Value Theory model (EVT). The research objective is to test the performance of these models in specific, volatile, market circumstances, in terms of estimating the maximum possible losses from these activities. The basic hypothesis of the research is that it is possible to successfully anticipate the maximum possible losses from the investment activities in the extreme points of the return function by applying different methods of investment risk modelling in volatile market circumstances. The analysed financial data comprise daily stock returns of the BELEX15 (Serbia), BUX (Hungary), CROBEX (Croatia) and SBITOP (Slovenia) stock exchange indices in the period 2012-2019, which is relatively long time period suitable for the sound analyses. The main findings of the research point to the superior application adequacy of the Extreme Value Theory model (EVT) for successful risk modelling, i.e. for making optimal investment decisions. The research results represent innovated, concrete knowledge in the field of understanding the behaviour of the return function in its extremes, and consequently are of great importance to both the academic and professional public in the process of generating decisions on investment activities in volatile market conditions.

show abstract

Value-at-Risk dynamics: a copula-VAR approach

Cited by 9 publications

References 18 publications

Quantifying Foreign Exchange Risk in the Selected Listed Sectors of the Johannesburg Stock Exchange: An SV-EVT Pairwise Copula Approach

Quantifying Foreign Exchange Risk in the Selected Listed Sectors of the Johannesburg Stock Exchange: An SV-EVT Pairwise Copula Approach

CoVaR with volatility clustering, heavy tails and non-linear dependence

Modelling Risk under Volatile Conditions: Tail Index Estimation and Validation

Contact Info

Product

Resources

About