scholarly journals Multivariate Heavy-Tailed Models for Value-at-Risk Estimation

2011 ◽  
Author(s):  
Carlo Marinelli ◽  
Stefano d'Addona ◽  
Svetlozar Rachev
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Heavy Tailed

scholarly journals MULTIVARIATE HEAVY-TAILED MODELS FOR VALUE-AT-RISK ESTIMATION

2012 ◽  
Vol 15 (04) ◽  
pp. 1250029 ◽  
Author(s):  
CARLO MARINELLI ◽  
STEFANO D'ADDONA ◽  
SVETLOZAR T. RACHEV
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Price ◽  
Risk Estimation ◽  
Price Data ◽  
Heavy Tailed Distributions ◽  
Derivative Instruments ◽  
Heavy Tailed ◽  
Student’S T

For purposes of Value-at-Risk estimation, we consider several multivariate families of heavy-tailed distributions, which can be seen as multidimensional versions of Paretian stable and Student's t distributions allowing different marginals to have different indices of tail thickness. After a discussion of relevant estimation and simulation issues, we conduct a backtesting study on a set of portfolios containing derivative instruments, using historical US stock price data.

scholarly journals Value at Risk estimation using GAS models with heavy tailed distributions for cryptocurrencies

2021 ◽  
Vol 10 (4) ◽  
pp. 40-54
Author(s):  
Stephanie Danielle Subramoney ◽  
Knowledge Chinhamu ◽  
Retius Chifurira
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Ratio Test ◽  
Student’S T Distribution ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Student’S T ◽  
Student's T Distribution ◽  
T Distribution

  Risk management and prediction of market losses of cryptocurrencies are of notable value to risk managers, portfolio managers, financial market researchers and academics. One of the most common measures of an asset’s risk is Value-at-Risk (VaR). This paper evaluates and compares the performance of generalized autoregressive score (GAS) combined with heavy-tailed distributions, in estimating the VaR of two well-known cryptocurrencies’ returns, namely Bitcoin returns and Ethereum returns. In this paper, we proposed a VaR model for Bitcoin and Ethereum returns, namely the GAS model combined with the generalized lambda distribution (GLD), referred to as the GAS-GLD model. The relative performance of the GAS-GLD models was compared to the models proposed by Troster et al. (2018), in other words, GAS models combined with asymmetric Laplace distribution (ALD), the asymmetric Student’s t-distribution (AST) and the skew Student’s t-distribution (SSTD). The Kupiec likelihood ratio test was used to assess the adequacy of the proposed models. The principal findings suggest that the GAS models with heavy-tailed innovation distributions are, in fact, appropriate for modelling cryptocurrency returns, with the GAS-GLD being the most adequate for the Bitcoin returns at various VaR levels, and both GAS-SSTD, GAS-ALD and GAS-GLD models being the most appropriate for the Ethereum returns at the VaR levels used in this study.    

Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation

2013 ◽  
Vol 40 (6) ◽  
pp. 2233-2243 ◽  
Author(s):  
Nikolay Y. Nikolaev ◽  
Georgi N. Boshnakov ◽  
Robert Zimmer
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Volatility Modeling ◽  
Heavy Tailed

scholarly journals Comparison of certain value-at-risk estimation methods for the two-parameter Weibull loss distribution

2011 ◽  
Vol 235 (11) ◽  
pp. 3304-3314 ◽  
Author(s):  
Omer L. Gebizlioglu ◽  
Birdal Şenoğlu ◽  
Yeliz Mert Kantar
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Estimation Methods ◽  
Loss Distribution ◽  
Two Parameter

scholarly journals Improving Value-at-Risk Estimation from the Normal EGARCH Model

2017 ◽  
Vol 11 (1) ◽  
pp. 91-106 ◽  
Author(s):  
Mahsa Gorji ◽  
Rasoul Sajjad
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Risk Estimation ◽  
Egarch Model

scholarly journals Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

2017 ◽  
Vol 65 (5) ◽  
pp. 1687-1694
Author(s):  
Tomáš Konderla ◽  
Václav Klepáč
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stock Exchange ◽  
Risk Estimation ◽  
Listed Company ◽  
Mixture Distribution ◽  
Normal Mixture ◽  
Development Characteristic ◽  
The Common

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA‑GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal‑mixture distribution against previously used GARCH with many types of non‑normal innovations.

Value-at-risk performance in emerging and developed countries

2018 ◽  
Vol 14 (5) ◽  
pp. 591-612
Author(s):  
Luiz Eduardo Gaio ◽  
Tabajara Pimenta Júnior ◽  
Fabiano Guasti Lima ◽  
Ivan Carlin Passos ◽  
Nelson Oliveira Stefanelli
Keyword(s):  
At Risk ◽  
Financial Crises ◽  
Value At Risk ◽  
Risk Estimation ◽  
Developed Countries ◽  
Emerging Countries ◽  
Copula Functions ◽  
Predictive Capacity ◽  
Arch Models ◽  
Content Type

Purpose The purpose of this paper is to evaluate the predictive capacity of market risk estimation models in times of financial crises. Design/methodology/approach For this, value-at-risk (VaR) valuation models applied to the daily returns of portfolios composed of stock indexes of developed and emerging countries were tested. The Historical Simulation VaR model, multivariate ARCH models (BEKK, VECH and constant conditional correlation), artificial neural networks and copula functions were tested. The data sample refers to the periods of two international financial crises, the Asian Crisis of 1997, and the US Sub Prime Crisis of 2008. Findings The results pointed out that the multivariate ARCH models (VECH and BEKK) and Copula-Clayton had similar performance, with good adjustments in 100 percent of the tests. It was not possible to perceive significant differences between the adjustments for developed and emerging countries and of the crisis and normal periods, which was different to what was expected. Originality/value Previous studies focus on the estimation of VaR by a group of models. One of the contributions of this paper is to use several forms of estimation.

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