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With political and economic scenarios changing at an ever faster pace, it is necessary to understand the potential effects on asset prices. Today, the topic of rising inflation in the US as well as in the Eurozone, although still considered temporary by central banks, confronts us with the "unexpected risk" of a deviation from the baseline scenario. This implies the risk of having an aggressive monetary policy in the US, in a restrictive direction, therefore harmful to the financial markets. In this context, the question arises: is it possible to contemplate these events beforehand and act in good time? The answer is Yes and good risk management practices are important, using stress testing / scenario analysis techniques to accompany risk measures such as VaR and Expected Shortfall. Implementing this concept, through the implementation of stress test / scenario analysis - Bloomberg Economics Forecast Models® and Bloomberg Factor Models® - the present work seeks to consider plausible adverse scenarios that may arise and to assess the related impacts in terms of portfolio. The final aim is to improve the information set for the investor, allowing him to avoid potential market falls, as far as possible, that could prevent him from achieving his investment objectives.

Britain’s decision to exit the EU lead to disruptions in global markets. This study investigates the change in the return and volatility spillover pattern due to the repercussions of the Brexit vote between the US, France, the UK, Germany, and India’s 10-year government bond yields by applying the VAR and GARCH-BEKK models. The findings demonstrate a substantial rise in the return spillover to India and USA 10-year government bond yields following the Brexit vote compared to the pre-Brexit vote era. In addition, the results showed evidence of unidirectional volatility spillover from India to France, bidirectional volatility spillover between the USA and India, and unidirectional volatility spillover from the UK to India 10-year government bond market post-Brexit vote. However, there was no interconnection between these markets before the Brexit vote. Therefore, the Brexit vote did affect and significantly increased the linkage between the US, France, the UK, and India’s 10-year government bond market. The increase in correlation in India-US, India-UK, and India-France’s 10-year government bond markets will help predict and have an important implication for hedgers, decision-makers, and portfolio managers if similar political events occur in the future.

Objetivo: o objetivo do presente estudo e? avaliar a potencial perda máxima em carteiras de investimentos mais concentradas e carteiras mais diversificadas utilizando como ferramenta para o controle e gerenciamento do risco de mercado, o cálculo do VaR. Para isso, o estudo se propôs a responder a seguinte questão de pesquisa: “Os fundos de ações mais diversificados apresentam menor risco?”. Metodologia: o modelo de simulação histórica foi aplicado, considerando sete carteiras de fundos de investimentos em ações (FIAs) e 493 retornos diários, sob o nível de confiança de 95%. Resultados: os resultados indicaram que a perda máxima esperada é superior em carteiras mais concentradas. Portanto, a estratégia de diversificação auxiliou na redução do risco e é um instrumento importante a ser considerado em um portfólio de ações. Contribuições do Estudo: a contribuição central do estudo é a de fornecer subsídios para investidores e gestores de recursos ao passo em que traz uma simulação e uma aplicação prática do VaR na análise da diversificação das carteiras dos fundos de investimentos em ações.

Bài viết này với mục đích đánh giá cấu trúc thị trường tài chính ở Việt Nam, nghiên cứu sử dụng mô hình VAR và kiểm định Granger để kiểm tra mối quan hệ giữa “Tăng trưởng kinh tế”, “Sự phát triển của hệ thống ngân hàng” và “Sự phát triển của thị trường chứng khoán”, bên cạnh đó bài viết cũng trình bày và so sánh cấu trúc thị trường tài chính của Việt Nam với cấu trúc thị trường tài chính của các nước trong ASEAN như Singapore, Malaysia và Philippines. Kết quả nghiên cứu cho thấy hệ thống tài chính của Việt Nam phụ thuộc phần lớn vào hệ thống ngân hàng, và hệ thống ngân hàng phát triển có tác động đến tăng trưởng kinh tế.

Ozone (O3) pollution in the atmosphere is getting worse in many cities. In order to improve the accuracy of O3 prediction and obtain the spatial distribution of O3 concentration over a continuous period of time, this paper proposes a VAR-XGBoost model based on Vector autoregression (VAR), Kriging method and XGBoost (Extreme Gradient Boosting). China is used as an example and its spatial distribution of O3 is simulated. In this paper, the O3 concentration data of the monitoring sites in China are obtained, and then a spatial prediction method of O3 mass concentration based on the VAR-XGBoost model is established, and finnally its influencing factors are analyzed. This paper concludes that O3 features the highest correlation with PM2.5 and the lowest correlation with SO2. Among the measurement factors, wind speed and temperature are the most important factors affecting O3 pollution, which are positively correlated to O3 pollution. In addition, precipitation is negatively correlated with 8-hour ozone concentration. In this paper, the performance of the VAR-XGBoost model is evaluated based on the ten-fold cross-validation method of sample, site and time, and a comparison with the results of XGBoost, CatBoost (categorical boosting), ExtraTrees, GBDT (gradient boosting decision tree), AdaBoost (adaptive boosting), RF (random forest), Decision tree, and LightGBM (light gradient boosting machine) models is conducted. The result shows that the prediction accuracy of the VAR-XGBoost model is better than other models. The seasonal and annual average R2 reaches 0.94 (spring), 0.93 (summer), 0.92 (autumn), 0.93 (winter), and 0.95 (average from 2016 to 2021). The data show that the applicability of the VAR-XGBoost model in simulating the spatial distribution of O3 concentrations in China performs well. The spatial distribution of O3 concentrations in the Chinese region shows an obvious feature of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The mean concentration is clearly low in winter and high in summer within a season. The results of this study can provide a scientific basis for the prevention and control of regional O3 pollution in China, and can also provide new ideas for the acquisition of data on the spatial distribution of O3 concentrations within cities.

This study revisits the relationship between the exchange rate and interest rate differential in Ghana with a focus on the period in which the country adopted the inflation targeting regime. Using macro-data spanning 2002 to 2019 for Ghana and the United States, we show the nonexistence of the relationship in both the short-run and long-run. Further, we show a positive but slow responsiveness of the exchange rate to interest rate differential shocks from the short-run to the medium term. The long-run results, however, shows a case of a strong and significant response of exchange rate to interest rate differential shocks. We recommend that the Bank of Ghana (BoG) addresses perennial macroeconomic instability, especially on inflation, which has been shown to fuel investment uncertainty and investment insensitivity to interest rate.

Energy stocks are potentially a hedge against inflation and have a number of advantages over other forms of energy investing. This motivates us to study on portfolio management of energy stocks. We compare the performance of proposed GARCH-EVT-vine copula models under three different dimensions with other competing models using energy stocks from the U.S. market. In our proposed model, we use static C- and D-vine copulas. We compare the accuracy and efficiency of different models in forecasting portfolio VaR and CVaR. We also examine whether the proposed models yield greater economic and statistical performances than the competing models in a tactical asset allocation framework. Our findings indicate that the proposed models perform best overall. In fact, the relatively better performance of the proposed model is even more prominent when the portfolio size increases. Further, the comparative analysis between GARCHEVT- static vine and GARCH-EVT-dynamic vine copula models produces mixed results. [ABSTRACT FROM AUTHOR]

Carbon emission reduction is a necessary task for each country to address the severe challenges arising from global warming. The government of the USA formally launched legal proceedings to withdraw from the Paris Agreement on climate change in November 2019, which has posed a significant challenge for the international community to tackle climate change together. While with the deepening of globalization, we should re-examine traditional understanding of factors influencing carbon emissions. This research has a theoretical analysis and establishes the motion trajectory of carbon emissions. The motion trajectory model is original design, and derivation takes the innovation and scientific contributions in the logic understand of the factors affecting carbon emissions. Meanwhile, taking an outward-looking, major economic province in China (Zhejiang Province) as an example, we empirically study the influencing factors of carbon emissions. Based on time series data of Zhejiang province from 1990 through 2020, the methods such as linear regression, ARDL model analysis, vector autoregression (VAR) analysis, and variance decomposition were used to check the motion trajectory model theories. The empirical results show that there is a long-run relationship between AGDP, TECH, TRADE, KL, and AC, while in the short-run, the effect of the TRADE is elastic and positively significant, the effect of the KL is elastic but nonsignificant, and AC has hysteresis effect on its own with negatively significant. It is concluded that the upgrading of the industrial structure is essential to break the solidified energy consumption mode. On this basis, Zhejiang Province is suggested to strengthen her development, popularization, and utilization of new energies, vigorously promote the development of the digital economy, and be fully involved in the regional integration of the Yangtze River Delta. [ABSTRACT FROM AUTHOR]

This research analyzes the effect of the government bond yield curve spread on economic growth performance in Indonesia using the indicators of exchange rate, inflation, BI rate, foreign investment, portfolio investment, current account, and government accounts. Furthermore, it aims to prove the accuracy of the vector autoregression (VAR) or vector autoregression model in predicting economic growth from Q1 2010 to Q3 2020. The results showed that the yield curve spread has a significant effect on economic growth. Meanwhile, the exchange rate, inflation, and the BI rate have a negative effect on economic growth. Capital inflows such as foreign direct investment, portfolio investment, as well as the current account balance and government balance have a positive effect on economic growth. These results are useful to government policymakers, fund managers, and investors, as they provide further evidence of the potential use of yield curves as an indicator of future economic activity. [ABSTRACT FROM AUTHOR]

Conventional explanations of monetary policy decisions in the United States assume that the longer-run Federal funds rate is determined by a representative central banker (i.e., the Fed) using longer-term forecasts of economic activity and unemployment. This assumption is inconsistent with the federalist structure of the Federal Reserve in which the Federal funds rate is determined by a committee made up of the Federal Reserve Board and the Federal Reserve Banks. This inconsistency would be irrelevant if differences in the Fed participants' longer-run projections were small or constant, but they are not: disparities in these longer-run projections are large and volatile. This finding raises several questions: Are FOMC participants relying on the same forecasting framework (i.e., model or rules of thumb) but using different values for the forecast drivers? Or are these participants using the same forecast drivers but relying on different frameworks? [ABSTRACT FROM AUTHOR]

Ethiopia has one of the largest livestock populations in Africa. In 2016-2017, the share of live animals, leather, and meat in the total export of the country reached 9.6%. This paper aims to identify the determinants of the export of Ethiopian livestock products by means of vector autoregressive and vector error correction models. Multivariate time series is used to model the association between the products of the Ethiopian livestock export included in the study. Vector autoregressive and vector error correction models are used for modelling and inference. The results indicated the existence of a long term correlation between the volume of live animals, meat and leather exports. The volume of meat export is significantly affected by a lag occurring in the export of live animals in the short-run. Therefore, 3.7% of the shortrun imbalance in the volume of leather export is adjusted each quarter. It is suggested that the exporters of livestock products should properly utilise the Ethiopian livestock resources. On the other hand, the government should offer different forms of support to exporters, especially those focusing on exporting value-added products. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been an extraordinary event, the type of event that rarely occurs but that has major impacts on the stock market. The pandemic has created high volatility and caused extreme fluctuations in the stock market. The stock market can be characterized as either linear or nonlinear. One method that can detect extreme fluctuations is extreme value theory (EVT). This study employed a semisystematic literature review on the use of the EVT method to estimate investment risk in the stock market. The literature used was selected by applying the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines, sourced from the ScienceDirect.com, ProQuest, and Scopus databases. A bibliometric analysis was conducted to determine the study characteristics and identify any research gaps. The results of the analysis show that studies on this topic are rarely carried out. Research in this field is generally performed only in univariate cases and is very complicated in multivariate cases. Given these limitations, further research could focus on developing a conceptual model that is dynamic and sensitive to extreme fluctuations, with multivariable inputs, in order to predict investment risk. The model developed here considered the variables that affect stock price fluctuations as the input data. The combination of VaR–EVT and machine-learning methods is effective in increasing model accuracy because it combines linear and nonlinear models. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic volatility connectivity of important environmental, social, and governance (ESG) stock indexes from May 2010 to March 2021. The empirical research is focused on five major S&P ESG stock indexes from the US, Latin America, Europe, the Middle East and Africa, and Asia Pacific regions. The study reveals that ESG stock indexes in the Middle East Africa, and Latin America are net shock transmitters, whereas the United States and Asia Pacific are net volatility receivers. Furthermore, the study finds that bilateral intercorrelations are higher among US, Latin America, and Europe region group pairs and weaker in relation to Middle East Africa and Asia Pacific region group pairs, indicating the presence of contagion within developed and/or emerging regions, which has relevance for portfolio and risk management. [ABSTRACT FROM AUTHOR]

This paper studies the nexus between asset returns volatility in six major segments of Indian financial markets (viz. money, equity, gsec, forex, equity and banking stocks) and macro-economic shocks (viz. GDP, Inflation, Current Account Deficit, market capitalisation to GDP ratio, US Treasury Yield and Foreign Portfolio Investment). The period of study is from April 2002 to March 2021, a period covering four instances of significant economic and financial market stress. Findings of the study are generally aligned to economic theory, except for the case of gsec market. Besides, macro-variables were found to be exerting greater impact when they are in their weaker/unstable state and the behaviour of US treasury yield and FPI flows were found be more significant factors during stress periods and recovery immediately thereafter. Therefore, there is a need to focus on maintaining macroeconomic stability as a policy to foster financial market stability. Besides, there is a need to monitor a customized and dynamic list of macroeconomic variables in respect of each of the financial market segments to decide on the timing, type and quantum of policy and regulatory responses from time to time. This study contributed towards financial markets public policy, particularly during periods of uncertainties. [ABSTRACT FROM AUTHOR]

This paper is concerned with estimating the risk measure, Value-at-Risk (VaR), without considering the usual hypothesis used in parametric methods. A non-parametric method is used to fit severity and frequency loss distributions in collective risk models. In addition, an optimum bandwidth is estimated. The model is then applied to insurance claims data from a particular insurance company. As a result of the new model, the outcomes show better accuracy, for both light-tailed and heavy-tailed distributions [ABSTRACT FROM AUTHOR]

The standard linear Granger causality test, based on the vector autoregressive model (VAR), requires stationarity of the time series. A VAR model is fitted to the first-differences of the time series, when they exhibit trends and are not co-integrated. In the case of co-integration, the vector error-correction model (VECM) is used instead. Alternatively, a nonlinear information causality measure is suggested, called partial transfer entropy on rank vectors (PTERV), which uses locally ranked observations. It is model-free and of a more general purpose, as it can be directly applied to the original time series without pre-testing for stationarity or co-integration. The significance test of the PTERV detects effectively the connectivity structure of complex multivariate systems. In particular, the size and power of this test are comparable to that of the standard linear Granger causality approach (VAR or VECM) when applied to systems with only linear causal effects, while the PTERV test outperforms the linear causality test when nonlinear causal effects exist, as long as the sample size is large enough. The application of PTERV to stock market data and interest rates illustrates that it can be a useful tool in the causality analysis of financial time series. [ABSTRACT FROM AUTHOR]

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration's deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project. [ABSTRACT FROM AUTHOR]

At the 26th annual United Nations climate change conference (COP26) in Glasgow, Scotland, 197 countries succeeded in reaching an agreement to address the "Phase-down of Unabated Coal Power", i.e. the gradual reduction of coal-fired power generation and to ends fossil fuel subsidies that are not in efficient. Its going with the policy of Indonesia Government, in the General Draft of National Energy (RUEN), they will limit coal production up to 400 million tons per year and the exports will be reduced gradually from year to year and will be stopped on 2046. In another hand, China and India's dependence on coal energy for power generation and industry is still very high. Both countries demanding will affect Indonesia's trade balance considering the proportion of Indonesia's coal exports is 71% compared to domestic consumption. To determine this effect, this study uses the Autoregression Vector (VAR) model with annual data from 2000 to 2021. The test results show that Indonesia's coal exports have an effect on the trade balance by 13.17% in terms of the total export value. International coal price will have a positive impact on the Indonesia's trade balance by 17.91%. However, the price of coal is very influential on the volume of Indonesia's coal export. Momentum of Phase-down Coal is a golden opportunity to maximize economic benefits while preparing renewable energy as a substitute for coal. [ABSTRACT FROM AUTHOR]

The primary objective of this article is to present an adaptive parameter VAR-KF technique (APVAR-KF) to forecast stock market performance and macroeconomic factors. The method exploits a vector autoregressive model as a system identification technique, and the Kalman filter is served as a recursive state parameter estimation tool. A further development was designed by incorporating the GARCH model to quantify an automatic observation covariance matrix in the Kalman filter step. To verify the efficiency of our proposed method, we conducted an experimental simulation applied to the main stock exchange index, real effective exchange rate and consumer price index of Thailand and Indonesia from January 1997 to May 2021. The APVAR-KF method is generally shown to have a superior performance relative to the conventional VAR(1) model and the VAR-KF model with constant parameters. [ABSTRACT FROM AUTHOR]

We analyse the predictive and the forecasting ability of various Generalized Autoregressive Score (GAS) and GARCH frameworks for European Union Allowances (EUAs) daily returns (EUAs returns) for the period 22/04/2005–28/02/2019. We further examine the impact of different distributional assumptions on risk prediction. The Model Confidence Set (MCS) is employed to compare and select a superior predictive model of Value-at-Risk (VaR) thresholds. We find that GAS under skewed t-student error distribution and gjr-GARCH under general error distribution deliver excellent results for the Value-at-Risk (VaR) prediction for EUA at 1% and 5% levels, respectively. These results are robust with respect to three back-testing procedures (i.e., Unconditional Coverage, Conditional Coverage, and Dynamic Quantile tests). These results are of particular importance for the development of EUA pricing policies and risk management strategies. [ABSTRACT FROM AUTHOR]

The aim of this study is to econometrically investigate the determinants of inflation in the Turkish economy. In the study that analyses the period of 2006-2021, the effect of exchange rate, interest rate, real money supply, budget deficit and current account deficit on the inflation rate was estimated under three different time series techniques: Stepwise regression model, Vector Autoregressive (VAR) model and Autoregressive Distributed Lag (ARDL) model. According to the findings, the exchange rate has a positive effect on the inflation rate in both the short and long-run. The change in real money supply affects negatively inflation in both periods. Interest rate has a positive effect on the inflation rate. However, this effect is marginally low. Although the budget deficit affects inflation in the short-run, it is not a determinant on inflation in the long-run. In the study, strong findings that the current account deficit is determinant on inflation were not obtained. In terms of the variables discussed in the period of 2006-2021, the most statistically effective factor on inflation in the long run in the Turkish economy is the changes in the exchange rate. [ABSTRACT FROM AUTHOR]

We consider a multiplicative decomposition of the financial returns to improve the density forecasts of financial returns. The multiplicative decomposition is based on the identity that financial return is the product of its absolute value and its sign. Advantages of modeling the two components are discussed. To reduce the effect of the estimation error due to the multiplicative decomposition in estimation of the density forecast model, we impose a moment constraint that the conditional mean forecast is set to match with the sample mean. Imposing such a moment constraint operates a shrinkage and tilts the density forecast of the decomposition model to produce the improved maximum entropy density forecast. An empirical application to forecasting density of the daily stock returns demonstrates the benefits of using the decomposition and imposing the moment constraint to obtain the improved density forecast. We evaluate the density forecast by comparing the logarithmic score (LS), the quantile score (QS), and the continuous ranked probability score (CRPS). We contribute to the literature on the density forecast and the decomposition models by showing that the density forecast of the decomposition model can be improved by imposing a sensible constraint in the maximum entropy framework. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ciencias Economicas: Investigacion y Reflexion is the property of Revista Facultad de Ciencias Economicas 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 Bartin University Journal of Faculty of Economics & Administrative Sciences / Bartın Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi is the property of Bartin University, Faculty of Economics & Administrative Sciences 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.)

External influences or behavioral biases can affect the way risk is perceived. This paper studies the prediction of VaR (Value at Risk) as a measure of the risk of loss for investments on financial products. Our aim is to predict the percentage of loss that a financial product would have in the future to assess the risks and determine the potential loss of a security in the stock market, thus reducing reasoning influenced by feelings for bank and financial firms seeking to deploy AI and advanced automation. We used the IFM (inference function for margins) method in different market scenarios, with particular emphasis on the strengths and weaknesses of it. The study is assessed on single product level with the skewed studen-t GARCH(1,1) model and portfolio level with t-copulas for the inter-dependencies. It has been shown that under normal market conditions the risk is predicted properly for both levels. However, when an unexpected market event occurs, the prediction fails. To address this limitation, a combined model with sentiment analysis and regression is proposed for further investigation as a future work. [ABSTRACT FROM AUTHOR]

Based on a Structural VAR approach, we estimated fiscal multipliers for social benefits in Brazil for 1997–2018. Our results suggest that social benefits have relatively large multiplier effects, even when compared to public investment. The multipliers are also larger in the full sample, which includes the country's 2014–16 economic crisis than in the period 1997–2014. In particular, our results show that spending one unit on social expenditures generates a final change in GDP of almost three after two years. The higher estimated multipliers in the full sample appear in the response of household consumption and private investment to shocks in total social expenditures and for different types of social benefits (e.g. cash transfers, unemployment insurance, and pensions). In a context in which the expansion of social protection became prominent as a response to structural changes in the labor market and the Covid-19 pandemic, our paper reinforces its potential role in the short-run economic recovery. [ABSTRACT FROM AUTHOR]

Assessing the impact of exchange rate shocks on domestic prices (Exchange Rate Pass-through) has a crucial role, particularly for the inflation targeting countries. This paper aims to clarify some aspects of ERPT to domestic prices in Turkey, an emerging country. Specifically, this study investigates if ERPT in Turkey is complete and how the behavior of ERPT has changed after the adoption of a flexible exchange rate regime in 2001. A Vector Auto Regression (VAR) framework is used to analyze the impact of exchange rate shocks on CPI Inflation. This study provides empirical evidence on the incompleteness and the pattern of ERPT on Turkey’s CPI inflation based on the VAR Model. The findings demonstrate that ERPT is not only incomplete but also has a different pattern during the floating exchange rate period. The pass-through has decreased from 2001 to 2017, and it has increased dramatically after 2017 to a large extent because of very high inflation and high exchange rate volatility in this period.