Search

Este artigo tem o propósito de criar um índice capaz de mensurar o grau de incerteza da política econômica no Brasil. Esse índice será construído a partir da estimação do sentimento textual contido nas atas de reuniões do Copom, considerando o período de janeiro de 2000 a dezembro de 2018. Posteriormente, analisa-se como um choque de incerteza afeta a dinâmica de um conjunto de variáveis macroeconômicas por meio de um Modelo de Vetores Autorregressivos com restrição de sinais. Foi possível verificar que o aumento da incerteza tem efeitos contracionistas típicos, promovendo redução no consumo e na atividade econômica.

This study investigates the mixed impact of fiscal-monetary policies on economic growth in Vietnam, an emerging economy in the Asia-Pacific region. The Vector autoregressive method (VAR), a quantitative technique, is employed on a quarterly database collected in 2004–2018. The cointegration test indicates a long-term cointegration relationship between these macroeconomic policies and the growth of gross output. The variance decomposition and impulse response function conclude that the impacts of these policies on economic growth are quite weak and faint. However, our results indicate that monetary policy is more significant than fiscal policy in supporting economic growth. The results imply that these economic policies may give priority to other macroeconomic objectives instead of promoting economic growth in the studied period. Hence, policymakers need to have more solutions to improve the efficiency of these policies in Vietnam in the future.

Purpose: The aim of this study is to assess the potential maximum loss in more concentrated investment portfolios and more diversified portfolios using the VaR calculation as a tool for controlling and managing market risk. For this, the study proposes to answer the following research question: "Do more diversified equity funds present less risk?" Methodology: The historical simulation model was applied, considering seven portfolios of equity investment funds (FIAs) and 493 daily returns, under the 95% confidence level. Results: The results indicated that the maximum expected loss is higher in more concentrated portfolios. Therefore, the diversification strategy helped to reduce risk and is an important instrument to be considered in a stock portfolio. Contributions of the Study: The main contribution of the study is to provide subsidies for investors and asset managers, while providing a simulation and practical application of VaR in the analysis of portfolio diversification in equity investment funds.

In emerging economies, examining the linkage between different markets has become crucial. We have examined the linkage between crude oil and Indian oil exploration companies' equity prices. The augmented Dickey-Fuller method is used to test the stationarity of the series. The Granger causality test, Vector autoregression (VAR), and correlation methodologies are used to examine the causality between the markets. The p-values of Granger causality tests are less than 0.05, which confirms that the crude oil price causes the price movements of Indian oil exploration equities. The VAR (2) model confirmed that the prices of HOCE, OIL, and ONGC follow the first and second lag, Reliance and PETRONET equities follow the first lag of International crude price. The impulse response function shows a positive response of Indian oil exploration equity returns for the positive shocks of crude oil return. The findings of this study may help the traders and investors in the equity market, energy equity investors.

Este artigo apresenta uma discussão sobre parte das descobertas encontradas em uma pesquisa de iniciação cientí­fica, que teve como objetivo analisar as opiniões e repercussões nas redes sociais, sobre a modernização do futebol brasileiro através do uso da tecnologia. Mais precisamente, analisou-se o campeonato brasileiro masculino da série A, realizado no ano de 2019, através de apontamentos realizados sobre a arbitragem de vídeo publicadas no Twitter. Para atingir o proposto, inicialmente foram coletados e catalogados comentários da rede social delimitada, que serviram de subsí­dio para a realização de um estudo netnográfico. Desta feita, foram coletados e a partir da representação sócio digital dos membros frequentadores deste meio de comunicação interativo, obteve-se como resultado a manifestação de opinião destes de forma a concordar e a discordar da utilização do método V.A.R durantes as partidas de futebol do campeonato brasileiro de 2019. Neste sentido, foram encontrados os porcentuais de 66% como sendo de opiniões contrárias a utilização da modernidade e 34% favoráveis a esta utilização de um total de tweets analisados de 604. Logo, norteia-se este artigo para finalidade de demonstrar no que se refere a aplicação de novas tecnologias ao esporte citado, os principais aspectos positivos e negativos apresentados pelos torcedores sobre a modernização no futebol atual.

A busca pelo aperfeiçoamento no que se refere à justiça no futebol fez com que as instituições organizadoras de competições revissem seus métodos. Uma tecnologia utilizada em outros esportes, o sistema de ví­deo, passou a figurar as partidas junto com a implementação do auxiliar de ârbitro de ví­deo (VAR). O objetivo do estudo foi analisar se a interferência do VAR nos jogos modificou o resultado final do Campeonato Brasileiro de 2019, onde foi quantificado e qualificado os lances. Os dados foram compostos por 380 jogos no decorrer de 38 rodadas. A coleta de dados foi realizada a partir de um site esportivo e contabilizadas em uma planilha. Ocorreu interferência do VAR em 81 jogos, com 44 gols foram anulados, 18 gols validados, 37 pênaltis marcados e convertidos. Após a análise, concluiu-se que a intervenção do auxiliar de árbitro de ví­deo foi determinante para a classificação do campeonato. Métodos de aprimoramento no uso da tecnologia devem ser estudados, visando enfatizar o propósito central da ideia.

In this paper, shape memory alloys (SMAs) (NiTi-based) have been manufactured by casting with a different atomic percentage of a silver element (0, 1, 2 and 3 at. % Ag) using a Vacuum Arc Remelting (VAR) furnace. The silver element is added to the binary alloys due to its excellent properties such as (anti-corrosion, anti-bacterial and high electrical conductivity), which make these alloys using in wider applications. These alloys with different atomic percentages (Ni55Ti45Ag0, Ni55Ti44Ag1, Ni55Ti43Ag2 and Ni55Ti42Ag3) have been manufactured. The successful manufacturing process has been achieved and proved via examinations and tests. The FESEM microscopic examinations show that the silver element has been distributed uniformly and homogeneously in the NiTi matrix. Moreover, the emergence of austenite phase, martensite phase and little amount impurities. Regarding the XRD examination, showed that there is an increase in the number of peaks of Ag phase with an increase in the atomic percentage of the silver element, as well to emergence of phase (Ti2Ni) upon heating, phase (Ti 002) upon cooling, and phase (Ni4Ti3) is not desired. The starting and finishing of the phase transformations have been determined for all samples by the DSC test. The Shape Memory Effect (SME) for the alloy (Ni50Ti42Ag3) is measured to be about 89.99%.

While a great deal of effort over the past few decades has been devoted to the study of housing spillovers, much of the work has failed to grasp the critical role played by monetary policy. This study aims to investigate the full version of housing spillovers across monetary policy and local housing markets by introducing a hybrid structural vector autoregression (VAR) followed by a "two-sector" spillover account. Using monthly data for national M2 and city-level housing prices in China, we find that monetary policy has since 2019 played a key role in combating housing systemic risk by lowering inter-city ripple effects. At the same time, the pivotal position of Shenzhen in coordinating monetary policy with residential markets deserves explicit emphasis. • Housing spillovers stem from monetary policy and housing market itself. • Our new VAR model is to introduce monetary policy into housing spillovers. • A two-sector spillover account can show relative importance of monetary policy to local housing markets. • Our results indicate that China's monetary policy is effective against housing systemic risk since 2019. • Shenzhen is a focal city to maximize the influence of monetary policy in housing markets. [ABSTRACT FROM AUTHOR]

• The study presents a new methodology to decompose VaR ESG , a measure of VaR that integrates ESG risks, to obtain the correspondent Component VaR ESG. • This bottom-up methodology identifies the risk contribution of each security, by considering both its traditional financial risk and its exposure to ESG risk. • Component VaR ESG reduces unexpected losses, especially under stressful conditions. • Component VaR ESG can be used for defining specific risk limits in terms of individual security, geographical area, economic sector, and asset type. • Results are relevant for portfolio managers who intend to implement risk management practices compliant with sustainable finance legislation. This study investigates the contribution of different asset classes to investment portfolio risk by integrating environmental, social, and governance (ESG) factors into traditional financial risk measures. We propose a new methodology for decomposing VaR ESG by measuring the Component VaR ESG (CVaR ESG) of a multi-asset financial portfolio. A pilot empirical application's results provide evidence of the reliability of CVaR ESG to define the maximum contribution of the risk accepted for securities or parts of the financial portfolio. This study contributes to the debate on how ESG factors can have quantifiable long-term financial impacts and clarifies the risk contribution of each security included in a financial portfolio. [ABSTRACT FROM AUTHOR]

This study aims to measure the risk dependence between the two most important energy markets, oil and gas, to analyze their risk spillovers. To this end, we apply different extreme risk measures (the value at risk, conditional value at risk, delta conditional value at risk, and copula) to high-frequency energy data to capture the intraday dynamic dependence between oil and gas prices (using, in particular, a 5-min intraday sample data from November 2014 to October 2017). Our analysis shows two interesting findings. First, while we highlight an extreme risk dependence between oil and gas markets, the risk spillover from the oil to the gas market is higher than that from the gas to the oil market. Second, the upward and downward risk spillovers exhibit asymmetry, as extreme negative shocks produce a stronger spillover effect than do extreme positive shocks. The exploration of these systemic risk forms provides significant insights for policymakers and investors in terms of risk management and portfolio diversification. [ABSTRACT FROM AUTHOR]

Investment in Green energy is becoming a popular alternative asset class for investors, primarily due to its environment-friendly attributes. However, there is a dire need for subjective evaluation of this emerging asset class based on the risk-return dynamics to which investors are exposed. To respond to this call, in this study, we conduct this evaluation utilizing a unique and rich data set consisting of daily prices of exchange-traded funds (ETFs) established on different asset classes. We use Vector autoregression and Baba-Engle-Kraft-Kroner parameterization of multivariate GARCH models and assess the relative strength of return and volatility spillovers from the Green and Grey energy markets. Our results reveal the return shocks originated in the Green energy market and transmitted to other markets are more pronounced. It is also observed that the potential to earn high returns and the weak correlation of Green energy ETFs with the traditional asset classes are the crucial factors helpful in inviting attention and investment of investors after 2015. Although our results further suggest that the role of Grey energy is diminishing, as shown by the Impulse response functions and the coefficients of multivariate ARCH and GARCH. Nonetheless, for some asset classes, e.g., Bonds, the volatility spillovers that originated in the Grey energy market are still prominent and robust. [ABSTRACT FROM AUTHOR]

Forecasting the spread of COVID-19 infection is an important aspect of public health management. In this paper, we propose an approach to forecasting the spread of the pandemic based on the vector autoregressive model. Concretely, we combine the time series for the number of new cases and the number of new deaths to obtain a joint forecasting model. We apply the proposed model to forecast the number of new cases and deaths in the UAE, Saudi Arabia, and Kuwait. Test results based on out-of-sample forecast show that the proposed model achieves a high level of accuracy that is superior to many existing methods. Concretely, our model achieves mean absolute percentage error (MAPE) of 0.35%, 2.03%, and 3.75% in predicting the number of daily new cases for the three countries, respectively. Furthermore, interpolating our predictions to forecast the cumulative number of cases, we obtain MAPE of 0.0017%, 0.002%, and 0.024%, respectively. The strong performance of the proposed approach indicates that it could be a valuable tool in managing the pandemic. [ABSTRACT FROM AUTHOR]

This paper examines the relationship and related causality patterns of oil price volatility and exchange rate volatility of a group of oil-dependent economies before and after the 2008–2009 global financial crisis. We employed weekly time-series data of oil price and exchange rates for 2000–2007 (pre-crisis) and 2010–2016 (post-crisis). United States dollar exchange rates are for Ghanaian cedi, Nigerian naira, Russian ruble, Indian rupee, South African rand, and the Euro. To investigate the volatility impacts that exist between oil price and exchange rates during both sub-sample periods, we merged Vector Autoregressive (VAR) with GARCH and EGARCH models in the form of Bivariate VAR-GARCH and VAR-EGARCH. We further adopted the Toda-Yamamoto causality test to investigate related causality patterns. Empirical findings revealed both bidirectional and unidirectional relationship between oil price volatility and the exchange rates volatility of four out of the six oil-dependent economies considered for the study. These findings were more prevalent in the post-crisis period than the pre-crisis period. We also confirmed both bidirectional and unidirectional causality pattern between oil price volatility and exchange rate volatility of the same four currencies as observed with the VAR results in both sub-sample periods. [ABSTRACT FROM AUTHOR]

We present a polynomial expansion of the standardized Student-t distribution. Our density, obtained through the polynomial adjusted method in Bagnato, Potí, and Zoia (2015. "The Role of Orthogonal Polynomials in Adjusting Hyperbolic Secant and Logistic Distributions to Analyse Financial Asset Returns." Statistical Papers 56 (4): 1205–12340), is an extension of the Gram–Charlier density in Jondeau and Rockinger (2001. "Gram-Charlier Densities." Journal of Economic Dynamics and Control 25 (10): 1457–1483). We derive the closed-form expressions of the moments, the distribution function and the skewness–kurtosis frontier for a well-defined density. An empirical application is also implemented for modeling heavy-tailed and skewed distributions for daily asset returns. Both in-sample and backtesting analysis show that this new density can be a good candidate for risk management. [ABSTRACT FROM AUTHOR]

This article was prepared for the Special Issue "Celebrated Econometricians: Katarina Juselius and Søren Johansen" of Econometrics. It is based on material recorded on 30 October 2018 in Copenhagen. It explores Søren Johansen's research, and discusses inter alia the following issues: estimation and inference for nonstationary time series of the I(1), I(2) and fractional cointegration types; survival analysis; statistical modelling; likelihood; econometric methodology; the teaching and practice of Statistics and Econometrics. [ABSTRACT FROM AUTHOR]

This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of investing.com. The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R2 are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model's goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Indonesian Agribusiness / Jurnal Agribisnis Indonesia is the property of IPB University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Al-Quds Open University for Administrative & Economic Research is the property of Al-Quds Open University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The near‐universal practice of inflation targeting has strengthened the belief of central banks that all that is needed to control inflation is to anchor expectations with a credible inflation target. The use of Taylor rule augmented DSGE models for policy analysis and forecasting adds credence to the view that, provided inflation expectations remain stable, actual inflation will be driven by expectations. The ultimate drivers of inflation are subsidiary to the central bank operation of acting on inflation expectations for the control of inflation. This article poses the question whether inflation is caused by deteriorating inflation expectations or excessive monetary growth. It takes as its theoretical inspiration Friedman's theory of nominal income determination. We use quarterly data of one‐year‐ahead inflation expectations produced by the Bank of England, and medium‐run inflation expectations backed out of five‐year bond yields as measures of long‐term inflation expectations. To this we add actual inflation, nominal GDP, and M4 to define a four‐variable VAR. The results reveal that M4 Granger causes inflation and inflation expectations, and a variance decomposition of inflation shows that while inflation expectations help to drive inflation, after a period of between five and eight quarters money supply dominates the variance decomposition. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose a single-phase UPC (universal power compensator) system to extensively tackle power quality issues (voltage and current) with an equal VAR (volt-ampere reactive) sharing approach between the series and shunt APF (active power filter) of a UPC system. The equal VAR sharing feature facilitates the series and shunt APF inverters to be of an equal rating. An SRF (synchronous reference frame)-based direct PA (power angle) calculation technique is implemented to realize equal VAR sharing between the APFs of the UPC. This PA estimation utilizes d and q axis current parameters derived for the reference signal generation of the shunt APF. An SRF-based method is highly useful for power estimations in distorted supply voltage conditions compared with other conventional methods, i.e., the PQ method. It comprises a reduced complexity and estimations with an easiness to retain two APF inverters of equal rating. A rigorous simulation analysis is performed with MATLAB/SIMULINK and a real-time digital simulator (OPAL-RT) for addressing different power quality-disturbing elements such as current harmonics, voltage harmonics, voltage sag/swell and load VAR demand with the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

How can we construct portfolios that perform well in the face of systemic events? The global financial crisis of 2007–2008 and the coronavirus disease 2019 pandemic have highlighted the importance of accounting for extreme form of risks. In "Systemic Risk-Driven Portfolio Selection," Capponi and Rubtsov investigate the design of portfolios that trade off tail risk and expected growth of the investment. The authors show how two well-known risk measures, the value-at-risk and the conditional value-at-risk, can be used to construct portfolios that perform well in the face of systemic events. The paper uses U.S. stock data from the S&P500 Financials Index and Canadian stock data from the S&P/TSX Capped Financial Index, and it demonstrates that portfolios accounting for systemic risk attain higher risk-adjusted expected returns, compared with well-known benchmark portfolio criteria, during times of market downturn. We consider an investor who trades off tail risk and expected growth of the investment. We measure tail risk through the portfolio's expected losses conditioned on the occurrence of a systemic event: financial market loss being exactly at, or at least at, its value-at-risk (VaR) level and investor's portfolio losses being above their conditional value-at-risk (CoVaR) level. We decompose the solution to the investment problem in terms of the Markowitz mean-variance portfolio and an adjustment for systemic risk. We show that VaR and CoVaR confidence levels control the relative sensitivity of the investor's objective function to portfolio-market correlation and portfolio variance, respectively. Our empirical analysis demonstrates that the investor attains higher risk-adjusted returns, compared with well-known benchmark portfolio criteria, during times of market downturn. Portfolios that perform best under adverse market conditions are less diversified and invest on a few stocks that have low correlation with the market. [ABSTRACT FROM AUTHOR]

The past decades witness the impressive development of China. Along with the huge demand of China for commodities, the Chinese shipping market becomes more and more important. This paper investigates the determinants and the interactions of the Chinese shipping market with the international shipping market. We utilize the relatively large-dimensional Chinese commodities data as part of the controlled variables. The principal component analysis is applied to the commodities data to reduce the size of the data dimension. It is found that the controlled variables, including fuel cost, financial markets, and prices of large commodities, have the explanatory capability for the Chinese and the international shipping markets. There are mutual influences between the two shipping markets, with the Chinese market more sensitive to the movements of the international market. The Chinese shipping market has integrated into the international shipping market to some extent, instead of being isolated. [ABSTRACT FROM AUTHOR]

Power system networks are becoming more complex and decentralized with the foreword of deregulation in the global power sector. In this scenario, an independent system operator (ISO) is responsible for determining the appropriate actions to deliver stable and quality power to the customers connected to the network at the lowest cost without violating the system security limits. Violations of any security limit may result in system risk. The unstable and non-reliable system always has some drawbacks and is not desirable from the consumer's point of view. A deregulated power market always keeps the consumer on the advantage side by giving stable, reliable, and less costly power. By using risk assessment tools, we identify the fault conditions and we try to minimize the risk by various uses of sequential programming methods. In this paper, a novel power system risk analysis and congestion management approach are introduced with considering meta-heuristic algorithms i.e., Slime Mould Algorithm (SMA) and Artificial Bee Colony Algorithm (ABC) in renewable energy integrated electricity market. The proposed power system risk analysis is constructed with the help of two risk valuation tools named Conditional-Value-at-risk (CVaR) and Value-at-risk (VaR). The higher negative value of VaR and CVaR represents the higher risk system and lower negative value or towards a positive value of VaR and CVaR denotes the less risk or stable system. The projected method has been experienced on the IEEE 14-bus test system and IEEE 30-bus test system to examine the usefulness of the meta-heuristic algorithm in system risk analysis under the deregulated environment. The importance of renewable energy integration in system risk curtailment has also been depicted in this work: basically, to measure the system's risk, hence enhancing the system's reliability and societal welfare. As a result, it will benefit both supply and demand-side participants. [ABSTRACT FROM AUTHOR]

In the field of financial risk measurement, Asymmetric Laplace (AL) laws are used. The assumption of normalcy is used in traditional approaches for calculating financial risk. Asymmetric Laplace distribution, on the other hand, reveals the properties of empirical financial data sets much better than the normal model by leptokurtosis and skewness. According to recent financial data research, the regularity assumption is frequently broken. As a result, Asymmetric Laplace laws offer a simple, creative, and useful option to normal distributions when it comes to modeling financial data. We here engage AL distribution to explore specific formulas for the two commonly used risk measures, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). The currency exchange rates data are used to and worked out to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

In this study, we test whether uncertainty shocks can affect the choice between direct investments and international trade as competitive modes of operating on foreign markets. In order to verify this, we built simple investments-to-trade ratios and used them with selected uncertainty measures in VAR models. Our analysis shows that in the case of the Polish manufacturing sector, different types of uncertainty not only affect the choice between direct investments abroad and exports but also affect the choice between foreign direct investment and imports as different modes of supplying the Polish market by foreign entities. [ABSTRACT FROM AUTHOR]

Indonesia, which is included in a small open economy, will be easily affected by the turmoil in a large country such as the United States. Any form of pressure, either appreciation or depreciation of the exchange rate, is necessary because it could lead to a crisis that has a negative impact on the economy. This study analyzes the Exchange Market Pressure (EMP) based on the condition of foreign exchange reserves, the rupiah exchange rate, inflation and the consumer price index using the Vector Autoregressive (VAR) approach. The data used is quarterly secondary data in the first quarter of 2010 to the fourth quarter of 2020. The results show that there is a long-term effect on the variables of foreign exchange reserves, rupiah exchange rate, inflation and the consumer price index on the value of EMP. In the causality analysis, it is known that there is no two-way relationship between all these variables. What happens is only a one-way relationship between foreign exchange reserves and EMP, rupiah exchange rate with EMP, foreign exchange reserves with rupiah exchange rate, and EMP with consumer price index. [ABSTRACT FROM AUTHOR]

To estimate state‐dependent US output multipliers of government spending, we build a Markov‐switching fiscal VAR assuming that the economy is subject to unobservable regime shifts. We combine variables in the VAR and external qualitative indicators to infer the regimes and find that they match well with the business cycle. To account for the possibility of regime shifts after a shock, we compute generalized impulse responses using a novel recursive method. We find the multipliers smaller than 1 in both regimes, but statistically larger in recessions than in expansions. We reconcile our results with the main findings in the previous studies. [ABSTRACT FROM AUTHOR]

In this paper, we define a new multivariate conditional Value-at-Risk (MCVaR) risk measure. This MCVaR considers both individual risks and the aggregate risk of a portfolio, but prioritizes the aggregate risk. The new MCVaR risk measure is based on the minimization of the expectation of a multivariate loss function, which balances the shortfall and surplus risks of the aggregate risk and the individual risks in an overall risk of a portfolio. It is shown that the MCVaR risk measure holds the properties of positive homogeneity, translation invariance, subadditivity, and monotonicity under certain conditions. Numerical examples of the MCVaR risk measure are presented to illustrate the effect of dependence among individual risks on the MCVaR. [ABSTRACT FROM AUTHOR]

Introduction: Football is the most practiced sport in the world and one of the fundamental elements for the sport are the referees and assistants. Recently, the video assistant referee (VAR) was introduced in football in order to assist referees in the decision-making process during the game. In this way, VAR is used to minimize referee errors and promote fairer competition between teams. Objective: The objective of the present study was to analyze the influence of VAR in the 2019 Men's First Division Brazilian Football Championship, compared to the 2018 edition, in which VAR was not present. Methods: A descriptive analysis of the refereeing performance of all matches of the Brazilian Men's Football Championship, editions 2018 and 2019, was carried out, analyzing the number of penalties, fouls, yellow and red cards, offsides and goals per match. In addition, VAR actions were taken into account in the 2019 edition, including penalties, offsides, red cards and player recognition. It is noted that most of the analyzed plays concern penalties, followed by offsides, red cards and player recognition. The data was collected through SofaScore football statistics website (www.sofascore.com), this data is accessible free of charge. Results: It was found that most of the analyzed plays are related to penalties, followed by offsides, red cards and player recognition. There was a reduction in the average number of fouls, yellow cards and impediments per game in the 2019 Brazilian Men's Football Championship, compared to 2018. Conclusion: The use of VAR can influence arbitration sanctions in the Brazilian Football Championship and, consequently, the dynamics about the game. [ABSTRACT FROM AUTHOR]

Ekonomik aktivitede zaman içerisinde yaşanan inişli çıkışlı hareketler iktisadi dalgalanmalar olarak tanımlanmakta iken çıktı düzeyindeki dalgalanmaların ileriki dönemlerdeki seyri hakkında bilgiler içeren göstergelere ekonomik göstergeler denilmektedir. İktisadi dalgalanmaların kısa dönemli öngörüsü, yaklaşan resesyonların tespiti ve buna yönelik gerekli önlemlerin alınması açısından önemli görülmektedir. Bu çalışmada, 2002Q1-2016Q3 dönemleri arası aylık ve üç aylık toplam 29 göstergeden yararlanılarak GSYH tahmin modeli oluşturma denemesi yapılmıştır. Bu amaçla, biri bağımlı değişken olarak GSYH, diğeri bağımsız değişken olarak bir ekonomik gösterge olmak üzere iki değişkenli VAR tahmin modelleri tahmin edilmiştir. Sonra, elde edilen gösterge bazlı bireysel tahminlerden aritmetik ortalama ve medyan alma yöntemleriyle tahmin birleştirme teknikleri uygulanmıştır. Son olarak, elde edilen tahminlerin ortalama hataları referans bir AR tahmininden elde edilen hataların ortalamasıyla karşılaştırılarak tahmin performanslarına bakılmıştır. Sonuç olarak, BIST100 endeksi, döviz kuru ve tüketici fiyat endeksi gibi verilerin, GSYH’nin kısa dönemli konjonktürel hareketlerinin tahmininde yararlı bilgiler içerdiği görülmüştür. Ayrıca, tahmin birleştirme yöntemlerinin pek çok göstergeden elde edilen bireysel tahminlerden daha başarılı sonuçlar verdiği de tespit edilmiştir.

At the beginning of the 21st century, with an unprecedented increase in the prices of basic commodities exportable in Latin America, the role of China’s demand has increased in the interest of economic literature, since the end of this so-called “super cycle”, occurred in 2014. This article discusses the effects of this cyclical variation in the prices of minerals and food products on macroeconomic variables (GDP, investment, income and international reserves) in the Brazilian economy, with exposure to the international market – and, accordingly, the demand from China – is significant. The investigation of possible channels of contamination by varying prices, through cointegration problems and the causality of Engle-Granger and VAR/VEC models, suggested different intensities of the impacts of the Super Cycle through similar channels. There was a broadly balanced relationship between fiscal income and GDP in the Brazilian economy in relation to price changes, as well as in the Chilean economy. In Venezuela, in turn, the effects of the cycle were much more intense in their inflows and economic growth.

The aim of this paper is to investigate if Islamic finance can solve the funding problem of SMEs and facilitate access to finance for SMEs, and how can these last participate in job creation in Turkey in order to face the problem of unemployment. The paper adopts a Vector Autoregressive model (VAR) based on monthly data (2009-2017). The results revealed that Islamic finance is a golden opportunity and a sufficient alternative financial source for SMEs. In addition, as SMEs can reduce unemployment by contributing to the labor market. This study contributes to the existing literature by presenting a promising financing tool to the prospective borrowers of SMEs (farmers, underserved groups, and small entrepreneurs) which is Islamic financing. This alternative can be an effective tool for both muslim and non-muslim countries in regard that conventional finance is more expensive and SMEs are unable to pay high interest rates. It would also help in establishing low unemployment levels. This paper suggested that Islamic banks and Islamic financial institutions should converge more on the SMEs by providing funding to SMEs which would lead to boosting production and economic development.

The paper realizes inclusion of probabilistic measure for risk, VaR (Value at Risk), into a portfolio optimization problem. The formal analysis of the portfolio problem illustrates the evolution of the portfolio theory in sequentially inclusion of different market characteristics into the problem. They make modifications and complications of the portfolio problem by adding various constraints to consider requirements for taxes, boundaries for assets, cardinality constraints, and allocation of the investment resources. All these characteristics and parameters of the investment participate in the portfolio problem by analytical algebraic relations. The VaR definition of the portfolio risk is formalized in a probabilistic manner. The paper applies approximation of such probabilistic constraint in algebraic form. Geometrical interpretation is given for explaining the influence of the VaR constraint to the portfolio solution. Numerical simulation with data of the Bulgarian Stock Exchange illustrates the influence of the VaR constraint into the portfolio optimization problem.

Background: Plasmodium falciparum erythrocyte membrane protein is encoded by a highly variable multicopy var gene family known to play a key role in malaria pathogenicity. Therefore, we investigated sequence variation, expression profile and immune response of the Duffy binding-like domain (DBLα) region of the var gene. Methods: Blood samples were collected from patients with cerebral, severe and mild malaria in Chhattisgarh, India, a region with endemic malaria. Polymerase chain reaction amplicons were cloned and sequenced to determine sequence variation. The expression level was analyzed targeting the upstream region of var gene using the Delta-Delta-Ct method. Immunoglobulin G (IgG) level was determined against the 6 synthetic peptides of the DBLα region. Results: The study identified that group 1 and group 5 sequences (cysteine/position of limited variability (cys/PoLV) classification) along with cys2/cys4 and MFK*/REY motifs and short amino acid length were significantly associated with malaria severity. The specific PoLV (MFKS, LREA, PTNL) were restricted to cerebral malaria. The expression level of var group A was higher than var groups B and C, demonstrating its prognostic characteristic. All peptides showed high-quality IgG response, while VAR P5 appeared to be a good marker for severity. Conclusions: The present study illustrates the presence of specific sequences of DBLα tags involved in severe malaria that could be targeted in future interventions for malaria control and elimination.

Objectives: The COVID-19 pandemic (caused by SARS-CoV-2) has introduced significant challenges for accurate prediction of population morbidity and mortality by traditional variable-based methods of estimation. Challenges to modelling include inadequate viral physiology comprehension and fluctuating definitions of positivity between national-to-international data. This paper proposes that accurate forecasting of COVID-19 caseload may be best preformed non-parametrically, by vector autoregression (VAR) of verifiable data regionally. Methods: A non-linear VAR model across 7 major demographically representative New York City (NYC) metropolitan region counties was constructed using verifiable daily COVID-19 caseload data March 12–July 23, 2020. Through association of observed case trends with a series of (county-specific) data-driven dynamic interdependencies (lagged values), a systematically non-assumptive approximation of VAR representation for COVID-19 patterns to-date and prospective upcoming trends was produced. Results: Modified VAR regression of NYC area COVID-19 caseload trends proves highly significant modelling capacity of observed patterns in longitudinal disease incidence (county R2 range: 0.9221–0.9751, all p < 0.001). Predictively, VAR regression of daily caseload results at a county-wide level demonstrates considerable short-term forecasting fidelity (p < 0.001 at one-step ahead) with concurrent capacity for longer-term (tested 11-week period) inferences of consistent, reasonable upcoming patterns from latest (model data update) disease epidemiology. Conclusions: In contrast to macroscopic variable-assumption projections, regionally-founded VAR modelling may substantially improve projection of short-term community disease burden, reduce potential for biostatistical error, as well as better model epidemiological effects resultant from intervention. Predictive VAR extrapolation of existing public health data at an interdependent regional scale may improve accuracy of current pandemic burden prognoses.

Recently, there has been renewed interest in modelling risks especially in insurance market, there has been substantial research undertaken on the importance of copula in financial risk management field. This study aim to estimate risk measures Value-at-Risk (VaR) and Expected Shortfall (ES) based on the dependence structure between investment channels (Government bonds, Securities available for trade, Securities available for sale and Securities held to maturity) by using elliptical Gaussian copula on data collected from Egyptian insurance market from 2007/2008 to 2018/2019. The principal finding of this research is to measure the financial risk according to the copula estimation. The findings of this paper illustrate that the copula parameter ρ is simmilar to the parameter in the whole market, that is because the life insurance sector has long term conrtacts while the nonlife sector has short term contracts. Moreover, VaR and CVaR in all sectors are equevlint that reflects the stability in insurance sector.

The primary purpose of investors is maximizing the utility that is characterized by two essential criteria include risk and return. Regarding investors' uncertainty about the future, one of the main ways to reduce risk is to diversify the investment portfolio. In this research, we proposed an index conducted by Euclidean distance for assessing portfolio diversity. Besides, we designed a multi-objective model to select optimal stock portfolios with considering value at risk (VaR), which is one of the critical indicators of unacceptable risk, portfolio Beta as systematic risk, and portfolio variance as unsystematic risk simultaneously. The model presented in this paper aims to maximize diversification while minimizing value at risk and stock risks. Furthermore, maximizing returns are considered as a limitation of this model. Since the proposed model is nonlinear and concerning computational complexity, it is NP-hard; therefore, we utilized the PSO and the GE metaheuristic algorithms that are improved for solving multi-objective problems to solve the model. The results of the model implementation in multiple iterations showed that the average yield of selected portfolios by the model is higher than the desirable condition. The evaluation of stock performance indicators also shows the satisfactory performance of the multi-objective model.

Landslides are one type of destructive and recurring natural calamities in the mountainous regions. The landslide occurrences often lead to immense damage to local infrastructure and loss of land, human lives and livestock. Data-driven risk assessment of landslide risk plays a crucial role in preventing the incoming landslide occurrences and recurrences. In this research, we developed a human-centric framework using information-granules to perform risk assessment of a group of landslides. First, the density-based spatial clustering of applications with noise (DBSCAN) has been selected as the backbone unsupervised learning method to subclusters for landslide risk indication. The clustering outcomes are visualized via t-distributed stochastic neighbor embedding (t-SNE) in the 2-D embedding space. Second, the prototype points within the subclusters produced by DBSCAN are computed for granular construction. Third, interval-based information-granules are constructed and measured via coverage, specificity and area under the coverage-specificity curve (AUC). Last, with the optimal information-granules constructed, two risk measures namely Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) are computed to interpret the rule-based information-granules with respect to the key attributes. Comparative experiments have also been performed against other benchmarking clustering approaches. Computational results indicate that the information-granules constructed from DBSCAN subclusters offered enhanced performance in reveal meaningful information-granules and provide promising results. The proposed approach can capture the main essence of landslide pattern with higher interpretability and help to reduce the computing overhead.

Traffic volume forecasting is a key objective in Intelligent Transportation Systems (ITS) since its importance for both the general public and authorities in decision making, optimizing navigation strategies and avoid traffic congestions. Various research projects have been conducted for identifying the best approach to solve that issue. This paper proposes a comparison of statistical learning models, Vector Auto Regression, ARIMAX and a deep learning model, LSTM neural network, in the context of multivariate short-term (24 hours) time series forecasting using traffic volume, speed, and average waiting time, integrating weather attributes in Austin city, Texas. Models were evaluated using rolling forecast origin method for three main feature sets generated utilizing feature selection. VAR model produced the best performance with an accuracy of 91.459% and proved to be used successfully in short term traffic forecasting in ITS applications.