scholarly journals Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?

2015 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Jying-Nan Wang ◽  
Lu-Jui Chen ◽  
Hung-Chun Liu ◽  
Yuan-Teng Hsu
Keyword(s):  
Value At Risk ◽  
Garch Model ◽  
Downside Risk ◽  
Exchange Traded Funds ◽  
Explanatory Variables ◽  
Additional Information ◽  
The Face ◽  
Variance Equation ◽  
Fear Index ◽  
Risk Managers

This paper aims to propose the augmented GJR-GARCH (GJR-GARCH<sub>M</sub>) model that extends the GJR-GARCH model by comprising overnight returns volatility (ONV), daily high-low prices range (PK), and fear index (VIX) as explanatory variables for the GJR’s variance equation, respectively. The proposed models are used to estimate the daily value-at-risk values and evaluate their downside risk management performance for the SPDRs covering the period from 2009 to 2014. Empirical results show that the GJR-GARCH<sub>M</sub> model outperforms the GJR-GARCH model for most cases, suggesting that the GJR-GARCH-based VaR forecasts can be moderately improved with the additional information embodied in the ONV, PK and VIX volatility estimators. In addition, daily high-low prices range and VIX are far more informative than the overnight volatility estimator for improving the GJR-GARCH-based VaR forecasts. Risk managers can employ the proposed models for estimating and controling the potential loss of ETFs in the face of financial catastrophes.

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

scholarly journals Measuring parametric and semiparametric downside risks of selected agricultural commodities

2021 ◽  
Vol 67 (No. 8) ◽  
pp. 305-315
Author(s):  
Dejan Živkov ◽  
Marijana Joksimović ◽  
Suzana Balaban
Keyword(s):  
At Risk ◽  
Soybean Oil ◽  
Soybean Meal ◽  
Value At Risk ◽  
Downside Risk ◽  
Conditional Value At Risk ◽  
Agricultural Commodities ◽  
Second Best ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Very High

In this paper, we evaluate the downside risk of six major agricultural commodities – corn, wheat, soybeans, soybean meal, soybean oil and oats. For research purposes, we first use an optimal generalised autoregressive conditional heteroscedasticity (GARCH) model to create residuals, which we later use for measuring downside risks via parametric and semiparametric approaches. Modified value-at-risk (mVaR) and modified conditional value-at-risk (mCVaR) provide more accurate downside risk results than do ordinary value-at-risk (VaR) and conditional value-at-risk (CVaR). We report that soybean oil has the lowest mVaR and mCVaR because it has two very favourable features – skewness around zero and low kurtosis. The second-best commodity is soybeans. The worst-performing downside risk results are in wheat and oats, primarily because of their very high kurtosis values. On the basis of the results, we propose to investors and various agents involved with these agricultural assets that they reduce the risk of loss by combining these assets with other financial or commodity assets that have low risk.

scholarly journals CONSTRUCTION AND APPLICATION OF THE CLASSIFICATION SCHEME OF DYNAMIC RISK MEASURES ESTIMATING

2016 ◽  
Vol 5 ◽  
pp. 67-80 ◽  
Author(s):  
Nataly Zrazhevska
Keyword(s):  
Value At Risk ◽  
Classification Scheme ◽  
Risk Measures ◽  
Estimation Method ◽  
Daily Basis ◽  
Stock Index ◽  
Dynamic Risk Measures ◽  
Dynamic Risk ◽  
Risk Managers

The most popular methods for dynamic risk measures – Value-at-Risk (VaR) and Conditional VaR (CVaR) estimating were analyzed, description and comparative analysis of the methods were fulfilled, recommendations on the use were given. Results of the research were presented in the form of a classification scheme of dynamic risk measures estimating that facilitates the choice of an estimation method. The GARCH-based models of dynamic risk measures VaR and CVaR evaluation for artificially generated series and two time series of log return on a daily basis of the most well-known Asian stock indexes Nikkey225 Stock Index and CSI30 were constructed to illustrate the effectiveness of the proposed scheme. A qualitative analysis of the proposed models was conducted. To analyze the quality of the dynamic VaR estimations the Cupets test and the Cristoffersen test were used. For CVaR estimations the V-test was used as quality test. The tests results confirm the high quality of obtained estimations. The proposed classification scheme of dynamic risk measures VaR and CVaR estimating may be useful for risk managers of different financial institutions.

Challenges for Diadromous Fishes in a Dynamic Global Environment

2009 ◽  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Generalized Additive Models ◽  
Habitat Characteristics ◽  
Explanatory Variables ◽  
Temperature And Precipitation ◽  
Management Plans ◽  
The Face ◽  
Uncertain Future ◽  
Diadromous Species

<em>Abstract</em>.-Climate change can have an effect on species distributions. The 1900 distribution and potential future distribution of diadromous fish in Europe, North Africa, and the Middle East were explored using generalized additive models (GAMs) and selected habitat characteristics of 196 basins. Robust presence-absence models were built for 20 of the 28 diadromous species in the study area using longitude, annual temperature, drainage surface area, annual precipitation, and source elevation as explanatory variables. Inspection of the relationship between each variable and species presence-absence revealed that the GAMs were generally interpretable and plausible. Given the predicted rise in annual temperature in climate models ranging between 1°C and 7°C by 2100, the fish species were classified according to those losing suitable basins, those gaining suitable basins, and those showing little or no change. It was found that the climate envelopes based on temperature and precipitation for diadromous species would, in general, be shifted farther northeastwards by 2100, and these shifting ranges were comparable with those assessed in other studies. The uncertain future of some species was highlighted, and it was concluded that conservation policy and management plans will need to be revised in the face of climate change.

scholarly journals Risk Model Validation: An Intraday VaR and ES Approach Using the Multiplicative Component GARCH

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10
Author(s):  
Ravi Summinga-Sonagadu ◽  
Jason Narsoo
Keyword(s):  
Loss Function ◽  
Value At Risk ◽  
Garch Model ◽  
Innovation Process ◽  
Expected Shortfall ◽  
Error Distribution ◽  
Garch Models ◽  
Intraday Data ◽  
Distributional Assumptions ◽  
T Distribution

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five distributional assumptions for the innovation process are used to analyse their effects on the modelling and forecasting performance. The high-frequency volatility models were validated in terms of in-sample fit based on various statistical and graphical tests. A more rigorous validation procedure involves testing the predictive power of the models. Therefore, three backtesting procedures were used for the VaR, namely, the Kupiec’s test, a duration-based backtest, and an asymmetric VaR loss function. Similarly, three backtests were employed for the ES: a regression-based backtesting procedure, the Exceedance Residual backtest and the V-Tests. The validation results show that non-normal distributions are best suited for both model fitting and forecasting. The MC-GARCH(1,1) model under the Generalised Error Distribution (GED) innovation assumption gave the best fit to the intraday data and gave the best results for the ES forecasts. However, the asymmetric Skewed Student’s-t distribution for the innovation process provided the best results for the VaR forecasts. This paper presents the results of the first empirical study (to the best of the authors’ knowledge) in: (1) forecasting the intraday Expected Shortfall (ES) under different distributional assumptions for the MC-GARCH model; (2) assessing the MC-GARCH model under the Generalised Error Distribution (GED) innovation; (3) evaluating and ranking the VaR predictability of the MC-GARCH models using an asymmetric loss function.

Forecasting multivariate VaR and ES using MC-GARCH-Copula model

2020 ◽  
Vol 21 (5) ◽  
pp. 493-516 ◽  
Author(s):  
Hemant Kumar Badaye ◽  
Jason Narsoo
Keyword(s):  
Value At Risk ◽  
Garch Model ◽  
Dependence Structure ◽  
Copula Model ◽  
Copula Models ◽  
Content Type ◽  
Significance Level ◽  
Foreign Currencies ◽  
Out Of Sample ◽  
Weighted Portfolio

Purpose This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY. Design/methodology/approach By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation. Findings In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure. Originality/value To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.

scholarly journals The Threshold GARCH Model: Estimation and Density Forecasting for Financial Returns*

2019 ◽  
Vol 18 (2) ◽  
pp. 395-424 ◽  
Author(s):  
Yuzhi Cai ◽  
Julian Stander
Keyword(s):  
Value At Risk ◽  
Empirical Work ◽  
Garch Model ◽  
Likelihood Estimation ◽  
Threshold Value ◽  
Financial Data ◽  
Financial Returns ◽  
Density Forecasting ◽  
Heavy Tailed ◽  
Density Forecasts

AbstractWe consider multiple threshold value-at-risk (VaRt) estimation and density forecasting for financial data following a threshold GARCH model. We develop an α-quantile quasi-maximum likelihood estimation (QMLE) method for VaRt by showing that the associated density function is an α-quantile density and belongs to the tick-exponential family. This establishes that our estimator is consistent for the parameters of VaRt. We propose a density forecasting method for quantile models based on VaRt at a single nonextreme level, which overcomes some limitations of existing forecasting methods with quantile models. We find that for heavy-tailed financial data our α-quantile QMLE method for VaRt outperforms the Gaussian QMLE method for volatility. We also find that density forecasts based on VaRt outperform those based on the volatility of financial data. Empirical work on market returns shows that our approach also outperforms some benchmark models for density forecasting of financial returns.

scholarly journals Temporal branch of facial nerve: a normative study of nerve conduction

2010 ◽  
Vol 68 (4) ◽  
pp. 619-622 ◽  
Author(s):  
Paula Fabiana Sobral da Silva ◽  
Maria Carolina Martins de Lima ◽  
José Natal Figueiroa ◽  
Otávio Gomes Lins
Keyword(s):  
Facial Nerve ◽  
Conduction Velocity ◽  
Normative Values ◽  
Normative Study ◽  
Traumatic Injuries ◽  
Additional Information ◽  
The Face ◽  
Electrophysiological Technique ◽  
Clinical Conditions ◽  
The Temple

The temporal branch of the facial nerve is particularly vulnerable to traumatic injuries during surgical procedures. It may also be affected in clinical conditions. Electrodiagnostic studies may add additional information about the type and severity of injuries, thus allowing prognostic inferences. The objective of the present study was to develop and standardize an electrophysiological technique to specifically evaluate the temporal branch of the facial nerve. METHOD: Healthy volunteers (n=115) underwent stimulation of two points along the nerve trajectory, on both sides of the face. The stimulated points were distal (on the temple, over the temporal branch) and proximal (in retro-auricular region). Activities were recorded on the ipsilateral frontalis muscle. The following variables were studied: amplitude (A), distal motor latency (DML) and conduction velocity (NCV). RESULTS: Differences between the sides were not significant. The proposed reference values were: A >0.4 mV, DML <3.9 ms and NCV >40 m/s. Variation between hemifaces should account for less than 60% for amplitudes and latency, and should be inferior to 20% for conduction velocity. CONCLUSION: These measurements are an adequate way for proposing normative values for the electrophysiological evaluation of the temporal branch.

Accurate value-at-risk forecasting based on the normal-GARCH model

2006 ◽  
Vol 51 (4) ◽  
pp. 2295-2312 ◽  
Author(s):  
Christoph Hartz ◽  
Stefan Mittnik ◽  
Marc Paolella
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Model ◽  
Risk Forecasting
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