Value At Risk Method Research Articles

Forecasting value-at-risk and expected shortfall in emerging market: does forecast combination help?

PurposeThis paper investigates how various strategies for combining forecasts, both simple and optimised approaches, are compared with popular individual risk models in estimating value-at-risk (VaR) and expected shortfall (ES) in emerging market at alternative risk levels.Design/methodology/approachUsing the case study of the Vietnamese stock market, the author produced one-day-ahead VaR and ES forecast from seven individual risk models and ten alternative forecast combinations. Next, the author employed a battery of backtesting procedures and alternative loss functions to evaluate the global predictive accuracy of the different methods. Finally, the author investigated the relative performance over time of VaR and ES forecasts using fluctuation test.FindingsThe empirical results indicate that, although combined forecasts have reasonable predictive abilities, they are often outperformed by one individual risk model. Furthermore, the author showed that the complex combining methods with optimised weighting functions do not perform better than simple combining methods. The fluctuation test suggests that the poor performance of combined forecasts is mainly due to their inability to cope with periods of instability.Research limitations/implicationsThis study reveals the limitation of combining strategies in the one-day-ahead VaR and ES forecasts in emerging markets. A possible direction for further research is to investigate whether this finding holds for multi-day ahead forecasts. Moreover, the inferior performance of combined forecasts during periods of instability motivates further research on the combining strategies that take into account for potential structure breaks in the performance of individual risk models. A potential approach is to improve the individual risk models with macroeconomic variables using a mixed-data sampling approach.Originality/valueFirst, the authors contribute to the literature on the forecasting combinations for VaR and ES measures. Second, the author explored a wide range of alternative risk models to forecast both VaR and ES with recent data including periods of the COVID-19 pandemic. Although forecast combination strategies have been providing several good results in several fields, the literature of forecast combination in the VaR and ES context is surprisingly limited, especially for emerging market returns. To the best of the author’s knowledge, this is the first study investigating predictive power of combining methods for VaR and ES in an emerging market.

VaR calculation by binary response models

<p>The original Risk-Metrics method is underpinned by the assumption that daily asset returns are conditional Gaussian independently identically distributed (iid) random variables with a mean of zero. In this paper, a new method to calculate Value at Risk (VaR) was suggested to overcome the shortcoming of Risk-Metrics by employing binary response models to compute probability forecasts of the portfolio return by exceeding a grid of candidate quantile values. From those values, the VaR quantile value was selected. The proposed model was called BRV (Binary Response VaR method). Consistent application of BRV to the Dow Jones Industrial Average (INDEXDJX: DJI) and Dow Jones U.S. Marine Transportation Index (DJUSMT) time series proved that it was more accurate than the Risk-Metric system. This method not only worked similar to quantile regression but had the advantage that conventional maximum likelihood methods could be used for parameter estimation and inference. The BRV method was the best performing method for computing the daily VaR at both the 95% and 99% confidence levels over the period 02/01/06–31/12/08. The BRV and the QR (quantile regression) methods performed similarly, but the BRV method had the practical advantage that conventional maximum likelihood (ML) technique could be used for parameter estimation and robust inference.</p>

MEASUREMENT OF THE RISK LEVEL AND INDIVIDUAL SHARE PROFIT BY USING A HISTORICAL APPROACH TO THE VALUE AT RISK (VaR) METHOD ON THE TOP 10 BEST COMPANY STOCK IN RTI PERIOD 2023

Research has been conducted to measure individual risk with profit level using the Value at Risk (VaR) approach. The research method used to analyze the data with refers to the Historical Simulation Model approach. The data used is the daily asset return data obtained from the daily closing price of shares during the January 2022 period. The results of the VaR method approach are Historical Simulation. The model shows NICL (PT Pam Mineral Tbk)stock with a risk of 30 million to 40 million. The measurement results show that if the funds invested are Rp. 500,000,000.00 with a 95% confidence level, the period is 1 year 248 days from published stock data of each company.

A risk measurement study evaluating the impact of COVID-19 on China's financial market using the QR-SGED-EGARCH model.

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis.

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies.

Value-at-Risk forecasting: A hybrid ensemble learning GARCH-LSTM based approach

This study proposes a new hybrid model that combines LSTM and BiLSTM neural networks with GARCH type model forecasts using an ensemble approach to forecast volatility for one-day ahead 95% and 99% Value-at-Risk (VaR) estimates using the Parametric (PAR) and Filtered Historical Simulation (FHS) method. The forecasting abilities of the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models are combined with the LSTM networks to capture different characteristics of the underlying volatility. We evaluate the model using log returns on Crude Oil during two periods of extreme volatility: the 2007-09 Financial Crisis and the Covid Recession of 2020–21. The performance of hybrid models is compared against several traditional VaR methods like the Historical Simulation, Bootstrap, Age weighted method, and the volatility-based VaR models using the GARCH, LSTM, and BiLSTM model forecasts. The unconditional and conditional coverage tests and a combination of regulator and firm loss functions are used to evaluate the quality of VaR forecasts. We find a significant improvement in the quality and accuracy of the VaR forecasts of the hybrid models over all the other models across all loss functions and coverage tests. The FHS-BiLSTM-HYBRID, a proposed FHS-based hybrid model, combining the BiLSTM model with three GARCH-type models, is the best performing, with the lowest values for both loss functions. The traditional and GARCH-type models do not efficiently model volatility during the crisis periods resulting in poor VaR forecasts. The FHS consistently performs as the best method for generating VaR compared to all other approaches.

Backtesting the evaluation of Value-at-Risk methods for exchange rates

PurposeSmall- and medium-sized enterprises can be highly affected by losses caused by exchange rate changes. The aim of this paper was to find the optimal Value-at-Risk (VaR) method for estimating future exchange rate losses within one year.Design/methodology/approachThe analysis focuses on five VaR methods, some of them traditional and some of them more up to date with integrated EVT or GARCH. The analysis of VaR methods was concentrated on a time horizon (1–12 months), overestimation predictions and six scenarios based on trends and variability of exchange rates. This study used three currency pairs EUR/CZK, EUR/USD and EUR/JPY for backtesting.FindingsIn compliance with the backtesting results, the parametric VaR with random walk has been chosen, despite its shortcomings, as the most accurate for estimating future losses in a medium-term period. The Nonparametric VaR confirmed insensitivity to the current exchange rate development. The EVT-based methods showed overconservatism (overestimation predictions). Every parametric or semiparametric method revealed a severe increase of liberality with increasing time.Research limitations/implicationsThis research is limited to the analysis of suitable VaR models in a long- and short-run period without using artificial intelligence.Practical implicationsThe result of this paper is the choice of a proper VaR method for the online application for estimating the future exchange rate for enterprises.Originality/valueThe orientation of medium-term period makes the research original and useful for small- and medium-sized enterprises.

PENGUKURAN TINGKAT RISIKO DAN KEUNTUNGAN SAHAM INDIVIDUAL DENGAN MENGGUNAKAN PENDEKATAN HISTORIS PADA METODE VALUE AT RISK (VaR) (STUDI KASUS TOP 10 SAHAM TERBAIK JANUARI 2022)

In research has been conducted to measure individual daily in January 2022 which is included in the Top 10 best stocks version of kontan.co.id risk and profit level using the Value at Risk (VaR) approach. The research method used to analyze the data refers to the Historical Simulation Model approach. The data used is the daily asset return data obtained from the daily closing price of shares during the January 2021 period. The results of the VaR method approach are Historical Simulation. The model shows CMPP stock with a risk of 91 million. The measurement results show that if the funds invested are Rp. 500,000,000.00 with a 95% confidence level, the period is 21 days.

Value-at-risk (VAR) estimation and backtesting during COVID-19: Empirical analysis based on BRICS and US stock markets

Value-at-risk (VaR) is the most common and widely used risk measure that enterprises, particularly major banking corporations and investment bank firms employ in their risk mitigation processes. The purpose of this study is to investigate the value-at-risk (VaR) estimation models and their predictive performance by applying a series of backtesting methods on BRICS (Brazil, Russia, India, China, South Africa) and US stock market indices. The study employs three different VaR estimation models, namely normal (N), historical (HS), exponential weighted moving average (EMWA) procedures, and eight backtesting models. The empirical analysis is conducted during three different periods: overall period (2006–2021), global financial crisis (GFC) period (2008–2009), and COVID-19 period (2020–2021). The results show that the EMWA model performs better compared to N and HS estimation models for all the six stock market indices during overall and crisis sample periods. The results found that VaR models perform poorly during crisis periods like GFC and COVID-19 compared to the overall sample period. Furthermore, the study result shows that the predictive accuracy of VaR methods is weak during the COVID-19 era when compared to the GFC period.

ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR. The purpose of this study is to demonstrate control variates method as a variance reduction method that can be applied to estimate VaR. Moreover, it is to compare the results with the normal VaR method or analytical VaR calculation. Control variates method was used to find new returns from all stocks which are used as estimators of the control variates. The new returns were then used to define parameters needed to generate N random numbers. Furthermore, the generated numbers were used to find the VaR value. The method was then applied to estimate a portfolio of the game and esports company stocks that are EA, TTWO, AESE, TCEHY, and ATVI . The results show Monte Carlo simulation gives VaR of US$41.6428 within 1000 simulation, while the analytical VaR calculation or normal VaR method gives US$30.0949.

Risk measurement for conditionally heteroscedastic location-scale time series models with ASTD and AEPD innovations

This study examines the asymptotic properties of a class of conditionally heteroscedastic location-scale time series models with innovations following a generalized asymmetric Student-t distribution (ASTD) or an asymmetric exponential power distribution (AEPD). We first show the consistency and asymptotic normality of the conditional maximum likelihood estimator of the model parameters under certain regularity conditions. Then, based on the maximum likelihood estimator, we estimate conditional value-at-risk (VaR) and expected shortfall (ES) by using their closed forms induced from the model. Their performance is finally compared with that of conditional autoregressive VaR and expectile methods. A simulation study and real data analysis are provided as an illustration.

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

Covid-19 Death Risk Estimation Using VaR Method

Purpose: The purpose of this paper is to show that the Value at Risk (VaR) method can be used to estimate the death rate from Covid-19 infection. Design/Methodology/Approach: The VaR method allows for risk measurements and estimations of the highest expected loss on a portfolio at an assumed confidence level over a specified time horizon. The most important assumption affecting the calculation method is that price changes in financial markets follow a normal distribution. Findings: It appears that by appropriately re-defining the concepts of assets and portfolio rates of return, we can describe the volatility in the numbers of deaths caused by Covid-19. We also confirmed using the Shapiro-Wilk and Skewness and Kurtosis tests that the rates of return distribution for the death numbers follow a normal distribution. Practical Implications: The VaR method allows to estimate the number of deaths based on current trends which can be utilised to better manage available resources in order to reduce casualties. We use the data regarding the number of deaths in the Visegrad Group (V4) countries as a case study to test the effectiveness and accuracy of the VaR method in a different, non-financial domain. Originality/Value: The theory we used in this paper is currently mainly applied to financial investments. We use this theory to describe social phenomenon which is the number of deaths, our approach has not been seen in the literature so far.

Tail dependence risk and spillovers between oil and food prices

We examine the nonlinear dependence dynamics and downside and upside risk spillovers between oil prices and world food prices captured by a world food price index and its subcategories of dairy, cereals, vegetable oil, and sugar. We draw our empirical results using static and dynamic bivariate copulas, Value-at-Risk (VaR) and conditional VaR (CoVaR) methods. Our empirical findings reveal that oil prices and aggregate food prices, as measured by the world food price index, independently move during market upturns and downturns. However, lower and upper tail dependence is observed between oil prices and cereals, vegetable oil, and sugar prices. We also identify upside and downside asymmetric risk spillovers from individual food commodities to oil and from oil to food commodities. Oil prices most strongly affect sugar and vegetable oil prices (downside and upside) whereas oil prices are most strongly impacted in the downside and upside by vegetable oil and sugar prices, respectively. The implications of the results are discussed.

Revisiting the accuracy of standard VaR methods for risk assessment: Using the Copula–EVT multidimensional approach for stock markets in the MENA region

The aim of this study is twofold. First, it aims to show how to overcome some of the shortcomings of the standard risk measurement methods using value-at-risk (VaR) in the context of extreme events and propose an alternative empirical method. Second, it aims to fill the research gap in analysis of risk in the Middle East and North Africa (MENA) region. In this regard, for six daily stock indices in the MENA region, from January 3, 2005 to December 31, 2014, we employ a vine copula-based generalized autoregressive conditional heteroskedastic (GARCH) method and the extreme value theory (EVT) to model the dependence between the marginal distributions of returns and forecast the VaR. Based on backtesting, we assess the efficiency of the standard risk measurement models from the following families—the exponentially weighted moving average, the historical simulation, and the GARCH. By implementing the GARCH-EVT-C-vine method in the MENA region, we find that, empirically, standard methods overestimate (underestimate) the violation ratio, implying an underestimation (overestimation) of the risk, and therefore a misallocation of the capital covering the risk. The method also provides better VaR estimates for the MENA stock markets than that of the standard methods. We also verify that the greater the openness of the capital accounts and the flexibility of the exchange rate regimes, the greater will be the conditional dependence of the MENA countries on the developed markets. Finally, the empirical method we propose has an important implication for the MENA countries in that it can be adopted by these countries to forecast VaR effectively.

Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states

Numerous advances in the modeling techniques of value-at-risk (VaR) have provided financial institutions with a wide range of market risk approaches. However, which model to use depends on the state of volatility. We present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory (EVT), conditional autoregressive VaR (CAViaR) and filtered historical simulation (FHS) with multiple sets of parameters. The backtesting procedure is based on the excess ratio, Kupiec and Christoffersen tests for multiple thresholds and cost functions. The main contribution of this paper is that we compared the models in four different scenarios, with different states of volatility in the training and testing samples. The results indicate that the best of the models, ie, the least affected by changes in the volatility, is GARCH(1,1) with a standardized Student t distribution. Nonparametric techniques (eg, CAViaR with GARCH setup or FHS with a skewed normal distribution) have very prominent results in testing periods with low volatility, but they are worse in turbulent periods. We also discuss an automatic method to set an extreme distribution threshold for EVT models as well as several ensembling methods for VaR, of which the minimum VaR estimate from the best models – in particular, a minimum of GARCH(1,1) with a standardized Student t distribution and either the EVT or the CAViaR model – has been proven to give very good results.

PERHITUNGAN VALUE AT RISK KUNJUNGAN WISATAWAN ASING KE BALI

The development of the tourism industry in Bali is very fast compared to other regions in Indonesia. This is due to the fascination of Bali which fascinates tourists, such as culture, customs and natural beauty. The rapid development of tourism in Bali requires tourism risk management. The purpose of this study is to calculate the Value At Risk (VaR) of Chinese, British and American tourists visiting Bali. The study was conducted using the VaR method with the GARCH (1,1) and GJR (1,1) models. Chinese tourist visit data is homocedasticity so it cannot proceed to GARCH (1.1) and GJR (1.1) modeling. VaR value of British and American tourist visits using the GARCH (1.1) and GJR (1.1) models at 95% confidence levels respectively -69.2% and -43.6 with an average VaR value of -56, 4%, and -69.3% and -44.7% with an average VaR of -57%. This means that if the Bali Government targets the number of tourist visits to be 7,100,000 people with a tourism promotion cost of Rp.134.1 per person, then there will be at least 4,004,400 people visiting Bali. So the investment costs incurred by the Provincial Government of Bali for tourism promotion of Rp. 536,990,040.

Comparative analysis between global sukuk and bond indices: value-at-risk approach

PurposeSukuk is an innovative financial instrument with a flexible structure based on Islamic financial contracts, unlike a bond which is based on the structure of a loan imposed with interest. With the notion that sukuk differs considerably from the conventional bonds in terms of risks related to investment, this study aims to examine whether the sukuk market is different from conventional bond markets based on the value-at-risk (VaR) approach.Design/methodology/approachThe VaR of a portfolio consists of sukuk and bond indices and is undertaken to determine whether there is any reduction in the VaR amount through the inclusion of the sukuk index in the portfolio. The analysis is undertaken based on the developed and emerging market bond and sukuk indices from January 2010 to December 2015.FindingsThis paper examines whether the VaR of sukuk market differs from conventional bond markets by using fundamental techniques. It was observed that the VaR amount of sukuk indices is comparatively much lower than the VaR of bond indices in all the cases. Including the sukuk index with each bond index can reduce the VaR of the portfolio by around 30 to 50 per cent for all the developed and emerging market bond indices.Research limitations/implicationsThis research is limited to covering six years of data. Nonetheless, it is able to provide findings which are believed to be useful for the market players.Practical implicationsThis study unveils attractive opportunities in terms of diversification benefits of sukuk indices for international fixed-income portfolios.Originality/valueThe VaR method is a useful risk management tool. This study uses this method to emphasise the significant reduction of risks and diversification benefits that sukuk investment could offer by including it in the investment portfolio.

Discussion on the Effectiveness of the Copula-GARCH Method to Detect Risk of a Portfolio Containing Bitcoin

Since it was invented by Satoshi Nakamoto in 2008, Bitcoin has drawn considerable attention both from the financial industry and government supervisory departments, and there is no unanimity on Bitcoin’s nature in the academic field. Some people may think Bitcoin is more like an asset than a currency. And investors’ motivations for incorporating Bitcoin into their portfolios may vary. Might there be a better way to deal with the risk-detection issue associated with such a unique and ambiguous object? The copula-GARCH method has been proven in much of the literature to be a better way than the traditional ways to estimate the value at risk (VaR) of portfolios. When it comes to a portfolio containing Bitcoin, can it still maintain its superiority? In this study, gold and Ethereum were each used to construct a portfolio with Bitcoin. We collected a total of 2,246 daily adjusted closing prices from July 23, 2010, to March 12, 2019. As for the copula-GARCH model, we selected four constant and two time-varying copula models combined with GARCH Student-t residuals to fit the joint distribution of the two assets in the portfolios. The traditional methods refer to the historical simulation, the variance-covariance method, the EWMA method, and the univariate GARCH VaR method. We adopted each method to compute corresponding one-day VaRs. Our results indicated that for the portfolios containing Bitcoin and Ethereum, the copula-GARCH method performed better than traditional methods, while for the portfolio consisting of Bitcoin and gold, traditional methods performed better. Our results may suggest that the copula-GARCH method may not be suitable in the extremely low correlation case.

Reducing Variation of Risk Estimation by Using Importance Sampling

In today's world, risk measurement and risk management are of great importance for various economic reasons. Especially in the crisis periods, the tail risk becomes very important in risk estimation. Many methods have been developed for accurate measurement of risk. The easiest of these methods is the Value at Risk (VaR) method. However, standard VaR methods are not very effective in tail risks. This study aims to demonstrate the usage of delta normal method, historical simulation method, Monte Carlo simulation, and importance sampling to calculate the value at risk and to show which method is more effective by applying them to the S&P index between 1993 and 2003.