Variance-covariance Method Research Articles

Implementing the Variance-Covariance Method for Assessing Market Transaction Risks in Raw Material Sector Stocks

The capital market plays a crucial role in supporting a country's economic growth. Besides being a funding source, the capital market also serves as an investment avenue for investors, particularly through stocks. Every investor must be willing to bear risks in line with their targeted returns. Risk is defined as the uncertainty of future outcomes due to market condition changes, and VaR (Value at Risk) is used to determine the tolerated loss at a certain confidence level. This study discusses the application of the Value at Risk (VaR) method using the Variance-Covariance approach to mitigate market risks in the portfolio of raw material sector stocks. The study focuses on two raw material sector stocks in Indonesia, assuming a normal distribution of asset price changes. The measurement results indicate that with an investment of Rp. 100,000,000.00, a 95% confidence level, and a 1-day period, the VaR of the portfolio of these five stocks is Rp. 2,769,750.00. This research provides critical insights to assist investors in understanding and managing portfolio risks, making VaR a key indicator to measure potential future risks and laying the foundation for decision-making in risk management.

Low-pressure measurements of energy depositions in nanometre objects with a single ionisation chamber in a 60Co beam using the variance–covariance method

Dose-mean lineal energies were measured using the variance–covariance method and commercial ionisation chambers in a therapy level 60Co field at several different air pressures. The results confirmed the trend from previous studies and the experimental data was extended down to a simulated object diameter of 4.2 nm using a low-noise electrometer. The variance–covariance method was adapted to consecutive charge integrations from a single ionisation chamber and used to correct for signal variations due to pressure drifts in the ion chamber. Monte Carlo simulations using MCNP 6.2 were performed, and the results follow the trend of the experimental values in the nanometric region.

Financial Risks of Business Management of Cryptocurrency Operations

Bitcoin is an asset with high risks, and a significant part of its volatility can be explained by the speculative component. Parametric variance-covariance (VaR) methods are not applicable for assessing the risks of bitcoin investment, since log returns are not distributed according to the normal law. Autoregressive risk assessment models (such as ARIMA-GARCH) for bitcoin volatility overestimate risks at times of sharp exchange rate changes and they underestimate them at times of less significant rate changes compared to historical volatility. The grid search for the smoothing parameter in the exponentially weighted moving average method is potentially interesting for modeling the risks of bitcoin investment. This makes it possible to fully take into account the autocorrelation of the bitcoin rate to the levels of previous periods and the volatility of the asset. As a conclusion, there are currently no econometric models that can explain and forecast the volatility of bitcoin in the medium and short term, considering the available factors in the market.

Analysis of Value at Risk Measurement Using The Variance-Covariance Method in The Securities Portfolio

Bonds are one of the investment instruments with fixed income. One of the most popular bonds is government bonds, which are considered safer than corporate bonds or other investment instruments. Every financial investment instrument that provides a rate of return (return) will have risks. Choosing bonds with the smallest loss rate is one of the priorities of an investor. One of the steps that investors can take is to determine the value of Value at Risk (VaR) first to determine the maximum loss obtained when investing. This study will discuss the risks in Bank Jatim's FVTOCI portfolio by measuring the Value at Risk value of each selected bond sample and several portfolio combinations of these bonds. The bonds used as samples in this research are FR0068, FR0080, FR0096 and FR0098. The measurement of this VaR value uses the Varian-Covariance method, where the assumption of the data used is data with a normal distribution. The resulting VaR value can be used as a consideration for the company to choose bonds for its investment portfolio.

BACK-TESTING APPROACHES FOR VALIDATING VAR MODELS

Value at risk (VaR) is one of the important market risk measures. It measures the possible potential loss on given investment in terms of value, with certain probability for certain time horizon. In this paper, our aim is to discuss different back-testing approaches to validate VaR models, and also test it the real market data. We back tested VaR of Nifty 50 index obtained by Variance Co-variance method, Historical simulation method, Monte-Carlo simulation, and cubic polynomial regression method. We have used Total exceptions by binary back-testing over entire population. we have also used Basel Traffic Light Zone Test, Kupiec POF-test, Kupiec TUFF-test, and Haas’ Mixed-Kupiec test and analyzed the above methods.

Design of Quantitative Investment Strategy Based on Domestic Chip Industry

Select 59 concept stocks in the chip industry for quantitative analysis, use principal component analysis method and k-means clustering to process our financial indicators; use historical simulation method, variance-covariance method and Monte Carlo simulation method to calculate the VaR value of each stock for effective risk management. Based on Markowitz theory, the effective frontier of five stocks is constructed, combined with the utility of investors in different risk aversion levels, the best portfolio is selected to obtain the best portfolio of concept stocks in the chip field, using the Sharp ratio and CV ratio for performance evaluation, and the corresponding investment suggestions are given.

Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio

In investing, all investors must be faced with risk that must be borne. Therefore, to determine the best strategy in investing, every investor must calculate the risk. One statistical approach that can be used to measure the risk is Value at Risk (VaR). VaR is defined as a tolerable loss with a certain level of confidence. The purpose of this research is to estimate VaR using Variance Covariance and Historical Simulation methods on banking stock portfolio consisting of three stocks for the period 11 September 2020-30 September 2021. Both methods will then be evaluated using backtesting to determine the accuracy of VaR and to obtain the best method. From the research results, if the holding period is 1 day, then the VaR calculation for banking stock portfolio using both methods can be used to estimate the risk at 99% and 95% confidence levels, except for the VaR value using the Variance Covariance method for banking stock portfolio at 95% confidence level. The results show that Variance Covariance method is the best method for 99% confidence level. As for the 95% confidence level, Historical Simulation method is the best method.

Range-shifter effects on the stray field in proton therapy measured with the variance-covariance method.

Measurements in the stray radiation field from a proton therapy pencil beam at energies 70 and 146 MeV were performed using microdosimetric tissue-equivalent proportional counters (TEPCs). The detector volumes were filled with a propane-based tissue-equivalent gas at low pressure simulating a mean chord length of 2 μm in tissue. Investigations were performed with and without a beam range shifter, and with different air gaps between the range shifter and a solid water phantom. The absorbed dose, the dose-mean lineal energy, and the dose equivalent were determined for different detector positions using the variance–covariance method. The influence from beam energy, detector- and range-shifter positions on absorbed dose, LET, and dose equivalent were investigated. Monte Carlo simulations of the fluence, detector response, and absorbed dose contribution from different particles were performed with MCNP 6.2. The simulated dose response for protons, neutrons, and photons were compared with, and showed good agreement with, previously published experimental data. The simulations also showed that the TEPC absorbed dose agrees well with the ambient absorbed dose for neutron energies above 20 MeV. The results illustrate that changes in both dose and LET variations in the stray radiation field can be identified from TEPC measurements using the variance–covariance method. The results are in line with the changes seen in the simulated relative dose contributions from different particles associated with different proton energies and range-shifter settings. It is shown that the proton contribution scattered directly from the range shifter dominates in some situations, and although the LET of the radiation is decreased, the ambient dose equivalent is increased up to a factor of 3.

Value at risk as a market risk measurement: An application on financial investment intruments

Risk çoğunlukla “zarara uğrama tehlikesi’’ olarak tanımlanmaktadır. Bu tehlikenin risk yönetimi ile önceden belirlenmesi, ölçülmesi ve gerekli önlemlerin alınarak olası kayıpların engellenmesi amaçlanmaktadır. Riske maruz değer daha anlaşılabilir olması ve muhtemel kayıp için tek bir rakam tahmini vermesinden dolayı risk yönetimi alanında en fazla tercih edilen yöntemlerden olmuştur. Riske maruz değer, düzenleyici otoritelerin önerileri ve kurumların risklere karşı duyarlılığının artması ile en çok kullanılan risk ölçütlerinden olmuştur. Bu çalışmada, Dolar, Euro, Sterlin, altın, BİST50, BİST100, Bitcoin finansal araçlarında piyasa riskinin ölçülmesinde kullanılan varyans- kovaryans yöntemi ile hangi yıllarda ve hangi finansal araçlarda riskin daha fazla olabileceğinin ortaya konulması amaçlanmıştır. Çalışmada Ocak 2009 – Aralık 2018 dönemlerine ait günlük kapanış verileri kullanılmıştır. Çalışma sonucunda, Dolar, Euro ve Sterlin için maksimum riske maruz değer 2018 yılında, BİST50 ve BİST100 endekslerinde en yüksek riske maruz değer 2013 yılında Altın ve Bitcoin için ise en yüksek riske maruz değer 2014 yılında gerçekleştiği tespit edilmiştir. Ayrıca riske maruz değer hesaplaması yapılan finansal yatırım araçları arasında en yüksek riske maruz değer Bitcoin’ de, en düşük riske maruz değer ise Dolar’ da olduğu görülmüştür.

Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process

A new computational approach based on the pointwise regularity exponent of the price time series is proposed to estimate Value at Risk. The forecasts obtained are compared with those of two largely used methodologies: the variance-covariance method and the exponentially weighted moving average method. Our findings show that in two very turbulent periods of financial markets the forecasts obtained using our algorithm decidedly outperform the two benchmarks, providing more accurate estimates in terms of both unconditional coverage and independence and magnitude of losses.

Mahalanobis distances for ecological niche modelling and outlier detection: implications of sample size, error, and bias for selecting and parameterising a multivariate location and scatter method.

The Mahalanobis distance is a statistical technique that has been used in statistics and data science for data classification and outlier detection, and in ecology to quantify species-environment relationships in habitat and ecological niche models. Mahalanobis distances are based on the location and scatter of a multivariate normal distribution, and can measure how distant any point in space is from the centre of this kind of distribution. Three different methods for calculating the multivariate location and scatter are commonly used: the sample mean and variance-covariance, the minimum covariance determinant, and the minimum volume ellipsoid. The minimum covariance determinant and minimum volume ellipsoid were developed to be robust to outliers by minimising the multivariate location and scatter for a subset of the full sample, with the proportion of the full sample forming the subset being controlled by a user-defined parameter. This outlier robustness means the minimum covariance determinant and the minimum volume ellipsoid are highly relevant for ecological niche analyses, which are usually based on natural history observations that are likely to contain errors. However, natural history observations will also contain extreme bias, to which the minimum covariance determinant and the minimum volume ellipsoid will also be sensitive. To provide guidance for selecting and parameterising a multivariate location and scatter method, a series of virtual ecological niche modelling experiments were conducted to demonstrate the performance of each multivariate location and scatter method under different levels of sample size, errors, and bias. The results show that there is no optimal modelling approach, and that choices need to be made based on the individual data and question. The sample mean and variance-covariance method will perform best on very small sample sizes if the data are free of error and bias. At larger sample sizes the minimum covariance determinant and minimum volume ellipsoid methods perform as well or better, but only if they are appropriately parameterised. Modellers who are more concerned about the prevalence of errors should retain a smaller proportion of the full data set, while modellers more concerned about the prevalence of bias should retain a larger proportion of the full data set. I conclude that Mahalanobis distances are a useful niche modelling technique, but only for questions relating to the fundamental niche of a species where the assumption of multivariate normality is reasonable. Users of the minimum covariance determinant and minimum volume ellipsoid methods must also clearly report their parameterisations so that the results can be interpreted correctly.

PENGUKURAN VALUE AT-RISK PADA PORTOFOLIO OBLIGASI DENGAN METODE VARIAN-KOVARIAN

A bond is investment instrument that is basically a debt investment. The profit gained in investing will be comparable with the risk. An investor must pay attention to the size of the risk in choosing bonds. Value at-Risk (VaR) is a risk measurement instruments for measure the maximum loss of asset or portfolio over a spesicif time interval for a given confidence level under normal market conditions. The purpose of this paper is to explain VaR measurement on bond portfolio using variance-covariance method and prove that method is valid to estimate VaR’s model using likelihood ratio. Variance covariance method was chosen because giving lower estimate potential volatility of asset or portfolio than historical simulation and Monte-Carlo simulation. This article use goverment bonds with code FR0053, FR0061, FR0073, FR0074 and portfolio combination. Normality test of return asset and portfolio are required before calculating VaR values. The result of this paper for confidence level 95% showed that bond portfolio FR0053 with FR0061 have a smaller value with VaR values 2,28% of the total market value. It was concluded that VaR bond portfolio are smaller than VaR single asset. Verification test estimate that VaR values using variance-covariance is valid at confidence level 95%.

An Empirical Assessment of Alternative Methods of Variance-Covariance Matrix

The current study aims at the estimation of a group of variance-covariance methods using the data set of the non-financial sector of the Pakistan stock exchange. The study compares nine covariance estimators using two assessment criteria of root mean square error and standard deviation of minimum variance portfolios to gauge on accuracy and effectiveness of estimators. The findings of the study based on RMSE and risk behaviour of MVPs suggest that portfolio managers receive no additional benefit for using more sophisticated measures against equally weighted variance-covariance estimators in the construction of portfolios. Keywords: Variance-Covariance Estimators, Portfolio Construction, Mean-Variance Optimization.

Calculation of Value-at-Risk Variance-Covariance with the Approach of Simple Cash Portfolio, Factor Models and Cash Flow

One way to calculate Value-at-Risk (VaR) is the variation-covariance method. The calculation of VaR covariance assumes stock data is normally distributed. The data needed to calculate VaR by the variance-covariance method is the covariance matrix of Bank Danamon and Bank Mandiri stock data. The main topics discussed in this paper are calculating VaR covariance with a simple cash portfolio approach, factor models and cash flow. For comparison of the use of the three approaches Backtesting, the backtest results indicate that the factor model is the best method.

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.

PREVALENCE AND HERITABILITY OF PERCEIVED MENTAL FATIGABILITY IN THE LONG LIFE FAMILY STUDY

We examined the prevalence and heritability of perceived mental fatigability among older adults enrolled in the Long Life Family Study. Participants (N=2342; 55% female) self-administered the Pittsburgh Fatigability Scale (PFS; scores range 0-50; higher score=greater fatigability). Using the PFS mental subscale, we evaluated differences across age strata (adjusted for family structure and field center) and estimated genetic heritability using the variance covariance methods implemented in SOLAR to determine genetic heritability (adjusted for age, sex, and field center). PFS mental score (mean±SD) and prevalence of higher mental fatigability (PFS ≥13) was greater across age strata: 60-69 (N=996, 5.9± 6.5, 14.5%), 70-79 (N=830, 6.8 ±7.6, 18.7%), 80-89 (N=251, 11.7±10.8, 41.8%), and ≥90 (N=265, 20.2±13.6, 67.2%), p<0.0001. Only among those ≥90, females (21.7±13.5) had greater mental fatigability than males (18.0±13.5), p=0.03. Residual heritability of mental fatigability was 0.17, p<0.0001. Future analyses will evaluate correlates of mental fatigability to identify potential avenues for intervention.

Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM

Short-term wind speed prediction is of cardinal significance for maximization of wind power utilization. However, the strong intermittency and volatility of wind speed pose a challenge to the wind speed prediction model. To improve the accuracy of wind speed prediction, a novel model using the ensemble empirical mode decomposition (EEMD) method and the combination forecasting method for Gaussian process regression (GPR) and the long short-term memory (LSTM) neural network based on the variance-covariance method is proposed. In the proposed model, the EEMD method is employed to decompose the original data of wind speed series into several intrinsic mode functions (IMFs). Then, the LSTM neural network and the GPR method are utilized to predict the IMFs, respectively. Lastly, based on the IMFs’ prediction results with the two forecasting methods, the variance-covariance method can determine the weight of the two forecasting methods and offer a combination forecasting result. The experimental results from two forecasting cases in Zhangjiakou, China, indicate that the proposed approach outperforms other compared wind speed forecasting methods.

Forecast and analysis of the ratio of electric energy to terminal energy consumption for global energy internet

In the background of building global energy internet, it has both theoretical and realistic significance for forecasting and analysing the ratio of electric energy to terminal energy consumption. This paper firstly analysed the influencing factors of the ratio of electric energy to terminal energy and then used combination method to forecast and analyse the global proportion of electric energy. And then, construct the cointegration model for the proportion of electric energy by using influence factor such as electricity price index, GDP, economic structure, energy use efficiency and total population level. At last, this paper got prediction map of the proportion of electric energy by using the combination-forecasting model based on multiple linear regression method, trend analysis method, and variance-covariance method. This map describes the development trend of the proportion of electric energy in 2017-2050 and the proportion of electric energy in 2050 was analysed in detail using scenario analysis.

Value at Risk on Composite Price Share Index Stock Data

The financial servicest authority was declared Let’s Save Campaign on n commemoration of the World Savings Day that falls on this day, October 31, 2016. The activity was greeted enthusiastically by Indonesia Stock Exchange by taking out the slogan Let’s Save The Stocks. Stock is a form of investment that is expected to benefit in the future despite has risks. Value at Risk (VaR) is a method that can measure how much the risk of a financial investment. Composite Stock Price Indeks is the stock price index used by Indonesia Stock Exchange as stock volatility benchmarks in Indonesia. This study aimed to estimate Value at Risk (VaR) on closing price Composite Price Share Index Stock data on the period 20 September 2016 until 20 September 2017. Box-Pierce test results p value=0.9528 which is greater than a, that shows homoskedasticity. Value at Risk (VaR) with Variance Covariance Method is Rp.3.054.916,07 which means with 99% confindence interval someone who invests Rp.100.000.000,00 will get Rp.3.054.916,07 as a maximum loss.

Comparison of value at risk methods: application of ISE 30

Purpose- The main purpose of this paper is to measure the potential losses of the portfolio obtained from ISE-30 using three different methods with VaR methods. Methodology- Historical Simulation, Variance-Covariance Method and Monte Carlo Simulation are used for the calculation of VaR. These three methods are examined regarding their results on the portfolios created according to different criteria. The price series of ISE 30 are used to create different three portfolios and their VaR results are compared. The performance of VaR results are checked by backtesting process after calculating VaR. VaR results are discussed by examining the performance of the methods for each portfolio. Findings- When the VaR of the portfolios are examined, the lowest VaR result of three portfolios is obtained in Portfolio 2 which is formed according to volatility criterion. One of the remarkable results of this study is that, as mentioned above, V-C and MCS methods give similar results. On the other hand, VaR results of the Historical Simulation Method are higher, and emerge in the green area in test process. Conclusion- It may be advisable for banks or other investors in the financial sector to move to the top of the order of preference according to the retrospective test results of TS method under high confidence level conditions. On the other hand, the results of the V-K and MCS method should be tested with the Backtesting by extending the observation period.