scholarly journals Previsão de value-at-risk para o mercado de criptomoedas usando modelos EGARCH com regimes markovianos

2020 ◽  
Vol 18 (3) ◽  
pp. 80
Author(s):  
Paulo Fernando Marschner ◽  
Paulo Sergio Ceretta
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Regime Change ◽  
Change Model ◽  
Sample Analysis ◽  
Predictive Capacity ◽  
Statistical Probability ◽  
Regime Changes ◽  
Arch Effect ◽  
Out Of Sample

<p>This study aims to understand the volatile behavior of six highly representative cryptocurrencies. To do so, EGARCH and Markov-switching EGARCH models were estimated, combined with different distributions of statistical probability. The predictive capacity of the best models resulting from these combinations were tested by predicting the value-at-risk. The daily returns of the cryptocurrencies clearly show regime changes in their volatility dynamics. In the in-sample analysis, the regime change model confirms the existence of two states: the first characterized by a greater ARCH effect and less affected by asymmetries, while the second reveals a greater effect of the arrival of information, that is, it is more sensitive to asymmetric shocks. In the out-of-sample analysis, the value-at-risk predictions of the regime change model clearly exceed the single-regime model by the extreme quantile of 1%.</p>

scholarly journals The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245904
Author(s):  
Viviane Naimy ◽  
Omar Haddad ◽  
Gema Fernández-Avilés ◽  
Rim El Khoury
Keyword(s):  
Value At Risk ◽  
Risk Measure ◽  
Canadian Dollar ◽  
Swiss Franc ◽  
Garch Models ◽  
Predictive Capacity ◽  
Australian Dollar ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Japanese Yen

This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies’ volatility and revealed persistence and “intensifying” levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.

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.

scholarly journals Risk-based constraints with correlated uncertainties for the optimal operation of an energy community

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Data ◽  
Real Life ◽  
Optimal Operation ◽  
Conditional Value At Risk ◽  
Forecast Errors ◽  
Worst Case ◽  
Life Measurement ◽  
Out Of Sample

<div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. </div><div>First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.</div><div>To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.</div>

scholarly journals Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility

2019 ◽  
Vol 12 (1) ◽  
pp. 79-88
Author(s):  
T. Bazhenov ◽  
D. Fantazzini
Keyword(s):  
At Risk ◽  
Empirical Evidence ◽  
Value At Risk ◽  
Implied Volatility ◽  
Realized Volatility ◽  
Google Trends ◽  
Probability Level ◽  
Sample Analysis ◽  
Search Activity ◽  
Additional Information

This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The outof-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1 % probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.

scholarly journals The Value-At-Risk Estimate of Stock and Currency-Stock Portfolios’ Returns

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 133 ◽  
Author(s):  
Jung-Bin Su ◽  
Jui-Cheng Hung
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Suitable Model ◽  
Conditional Correlation ◽  
Risk Capital ◽  
Forecast Performance ◽  
Stock Portfolios ◽  
Out Of Sample ◽  
Efficiency Test ◽  
Risk Managers

This study utilizes the seven bivariate generalized autoregressive conditional heteroskedasticity (GARCH) models to forecast the out-of-sample value-at-risk (VaR) of 21 stock portfolios and seven currency-stock portfolios with three weight combinations, and then employs three accuracy tests and one efficiency test to evaluate the VaR forecast performance for the above models. The seven models are constructed by four types of bivariate variance-covariance specifications and two approaches of parameters estimates. The four types of bivariate variance-covariance specifications are the constant conditional correlation (CCC), asymmetric and symmetric dynamic conditional correlation (ADCC and DCC), and the BEKK, whereas the two types of approach include the standard and non-standard approaches. Empirical results show that, regarding the accuracy tests, the VaR forecast performance of stock portfolios varies with the variance-covariance specifications and the approaches of parameters estimate, whereas it does not vary with the weight combinations of portfolios. Conversely, the VaR forecast performance of currency-stock portfolios is almost the same for all models and still does not vary with the weight combinations of portfolios. Regarding the efficiency test via market risk capital, the NS-BEKK model is the most suitable model to be used in the stock and currency-stock portfolios for bank risk managers irrespective of the weight combination of portfolios.

Several Extended CAViaR Models and Their Applications to the VaR Forecasting of the Security Markets

2016 ◽  
Vol 20 (4) ◽  
pp. 590-596
Author(s):  
Xiaorong Yang ◽  
◽  
Chun He ◽  
Jie Chen
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Security Market ◽  
Conditional Heteroskedasticity ◽  
Model Stability ◽  
Threshold Autoregressive ◽  
Security Markets ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Out Of Sample ◽  
Conditional Autoregressive

The conditional autoregressive Value-at-Risk (CAViaR) model, as a conditional autoregressive specification for calculating the Value-at-Risk (VaR) of the security market, has been receiving more and more attentions in recent years. As asymmetry may have a significant influence on the markets and the returns may have an autoregressive mean, this study proposes some extended CAViaR models, including asymmetric indirect threshold autoregressive conditional heteroskedasticity (TARCH) model and indirect generalized autoregressive conditional heteroskedasticity (GARCH) model with an autoregressive mean. We also present two types of CAViaR-Volatility models by adding the volatility term as an exogenous explanatory variable. Our empirical results indicate that extended models perform more effectively on out-of-sample predictions, as both forecasting effect and model stability have been improved. In addition, we find that the forecasting effect is better at the lower quantile (1%) than at the higher quantile (5%); a possible explanation is that extreme market information has more impact on VaR. In addition, there is negative correlation between volatility and VaR; VaR decreases as volatility increases.

Dynamic backtesting of value-at-risk models under regime change

2008 ◽  
Vol 1 (4) ◽  
pp. 95-110
Author(s):  
Victor de la Pena ◽  
Ricardo Rivera
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Regime Change ◽  
Risk Models

scholarly journals Out of sample value-at-risk and backtesting with the standardized pearson type-IV skewed distribution

2013 ◽  
Vol 60 (2) ◽  
pp. 231-247 ◽  
Author(s):  
Stavros Stavroyiannis ◽  
Leonidas Zarangas
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Skewed Distribution ◽  
Financial Analyst ◽  
Type Iv ◽  
Pearson Type ◽  
Out Of Sample ◽  
Sample Testing ◽  
T Distribution ◽  
Pearson Type Iv

This paper studies the efficiency of an econometric model where the volatility is modeled by a GARCH (1,1) process, and the innovations follow a standardized form of the Pearson type-IV distribution. The performance of the model is examined by in sample and out of sample testing, and the accuracy is explored by a variety of Value-at-Risk methods, the success/failure ratio, the Kupiec-LR test, the independence and conditional coverage tests of Christoffersen, the expected shortfall measures, and the dynamic quantile test of Engle and Manganelli. Overall, the proposed model is a valid and accurate model performing better than the skewed Student-t distribution, providing the financial analyst with a good candidate as an alternative distributional scheme.

scholarly journals Forecasting Value at Risk: Evidence from Emerging Economies in Asia

2018 ◽  
Vol 2 (1) ◽  
pp. 77-90
Author(s):  
Le Trung Thanh ◽  
Nguyen Thi Ngan ◽  
Hoang Trung Nghia
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Stock Returns ◽  
Emerging Economies ◽  
Value At Risk ◽  
Stock Index ◽  
Historical Simulation ◽  
Out Of Sample ◽  
Student’S T ◽  
T Distribution

In this paper, various Value-at-Risk techniques are applied to stock indices of 9 Asian emerging financial markets. The results from our selected models are then backtested by Unconditional Coverage, Independence, Joint Tests of Unconditional Coverage and Independence and Basel tests to ensure the quality of Value-at-Risk (VaR) estimates. The main conclusions are: (1) Timevarying volatility is the most important characteristic of stock returns when modelling VaR; (2) Financial data is not normally distributed, indicating that the normality assumption of VaR is not relevant; (3) Among VAR forecasting approaches, the backtesting based on in- and out-of-sample evaluations confirms its superiority in the class of GARCH models; Historical Simulation (HS), Filtered Historical Simulation (FHS), RiskMetrics and Monte Carlo were rejected because of its underestimation (for HS and RiskMetrics) or overestimation (for the FHS and Monte Carlo); (4) Models under student’s t and skew student’s t distribution are better in taking into account financial data’s characters; and (5) Forecasting VaR for futures index is harder than for stock index. Moreover, results show that there is no evidence to recommend the use of GARCH (1,1) to estimate VaR for all markets. In practice, the HS and RiskMetrics are popularly used by banks for large portfolios, despite of its serious underestimations of actual losses. These findings would be helpful for financial managers, investors and regulators dealing with stock markets in Asian emerging economies.  

CoVaR

2016 ◽  
Vol 106 (7) ◽  
pp. 1705-1741 ◽  
Author(s):  
Tobias Adrian ◽  
Markus K. Brunnermeier
Keyword(s):  
At Risk ◽  
Financial Crisis ◽  
Systemic Risk ◽  
Value At Risk ◽  
Asset Price ◽  
Financial System ◽  
Maturity Mismatch ◽  
Out Of Sample ◽  
Forward Looking

We propose a measure of systemic risk, Δ CoVaR, defined as the change in the value at risk of the financial system conditional on an institution being under distress relative to its median state. Our estimates show that characteristics such as leverage, size, maturity mismatch, and asset price booms significantly predict Δ CoVaR. We also provide out-of-sample forecasts of a countercyclical, forward-looking measure of systemic risk, and show that the 2006:IV value of this measure would have predicted more than one-third of realized Δ CoVaR during the 2007–2009 financial crisis. (JEL C58, E32, G01, G12, G17, G20, G32)

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