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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.

This study investigates the tail risk connectedness between financial technology (FinTech) and artificial intelligence (AI) stocks using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The asymmetric slope Conditional Autoregressive Value-at-Risk (CAViaR) approach was employed to quantify tail risk. Our study period spans from June 13, 2018, to September 15, 2023, inclusive of pre- and post-COVID-19 pandemic periods. The results indicate a significant increase in total tail risk spillovers during the initial wave of the COVID-19 pandemic, with spillovers being more pronounced at the 5% level, followed by the 10% and 2.5% levels. Predominantly, AI stocks emerged as persistent net transmitters of shocks, while FinTech stocks acted as shock receivers. The gold volatility and geopolitical risk (VIX and EPU) decrease (increase) the total system connectedness. The findings of this study advocate that investors and policymakers should consider incorporating FinTech and AI stocks in portfolios for enhanced risk diversification during periods of crisis. These nascent assets exhibit substantial growth potential, offering investors the opportunity for elevated returns, thus promoting household financial inclusion and technology adoption. [ABSTRACT FROM AUTHOR]

Pada investasi agregat aset finansial, setiap aset tunggal dapat memunculkan potensi risiko kerugian yang harus ditanggung oleh investor. Pada kondisi ini, untuk memprediksi nilai risiko kerugian dapat digunakan konsep risiko agregat. Prediksi nilai risiko dapat diukur melalui suatu ukuran risiko, salah satunya adalah Value at Risk (VaR). Namun, VaR tidak selalu memenuhi sifat subaditif, sehingga VaR bukan merupakan ukuran risiko yang koheren. Ukuran risiko lain sebagai alternatif pengganti VaR adalah Expected Shortfall (ES). Kelebihan utama ES dibandingkan VaR adalah ES telah memenuhi sifat subaditif, sehingga ES adalah ukuran risiko yang koheren. Untuk memprediksi nilai risiko agregat menggunakan VaR maupun ES, dibutuhkan fungsi distribusi bersama dari risiko agregat tersebut. Akan tetap cukup sulit untuk menentukan fungsi distribusi bersama risiko agregat yang disusun oleh beberapa risiko tunggal yang tidak saling bebas. Alternatif yang dapat digunakan apabila fungsi distribusi bersama risiko agregat sulit diperoleh adalah dengan menghitung batas atas risiko agregat dengan memanfaatkan sifat komonotonik dan convex order. Penelitian ini bertujuan untuk mengukur nilai batas risiko agregat menggunakan ukuran risiko ES untuk investasi agregat pada saham PT. Indofood Sukses Makmur Tbk (INDF.JK) dan PT Indofood CBP Sukses Makmur Tbk (ICBP.JK). Berdasarkan hasil analisis menggunakan data return saham INDF.JK dan ICBP.JK periode 02/01/21 – 17/09/21, nilai batas atas ukuran risiko aregat VaR dan ES pada portofolio saham untuk tingkat kepercayaan 95% dan holding period 1 hari masing-masing adalah -0,05231 dan -0,07731.

基于广义自回归得分（generalized autoregressive score，GAS）和已实现波动率异质自回归（heterogeneous autoregressive of realized volatility，HAR-RV）模型，引入投资者情绪因素，构建了HAR-RV GAS和HAR-RV-SENT GAS波动率模型，旨在预测沪深300指数波动率和风险价值（value at risk，VaR）度量。用自相关函数曲线和高级预测能力（superior predictive ability，SPA）检验方法，分别解析了模型样本内拟合和样本外波动率的预测能力。运用VaR预测序列图和平均分位数损失函数，实证分析了VaR预测效果。结果表明，HAR-RV-SENT GAS模型在波动率和VaR预测能力上效果较佳，HAR-RV GAS模型次之。研究可为金融投资者和风险管理者提供理论参考。

This paper seeks to answer the effects of monetary policy on family consumption using the National Household Survey (ENAHO) as a database. To do this, a panel data model is estimated for the period 2016 - 2019; as well as the use of the VAR panel methodology with a quarterly periodicity between 2013 and 2020. In both it is observed that there is a degree of significance of these shocks on the consumption of families, also verifying that under the inclusion of certain variables of control, as well as of the main macroeconomic variables, greater robustness of the monetary policy is generated.

The technique (VaR) is a statistical measure of the risk. It is associated with financial risks related to the high volatility in prices, interest rates, or exchange rates. It is used massively by entities because of the necessity to measure risk in constantly traded portfolios.

The relationship between economic growth and renewable energy (RE) consumption has received enormous attention in the literature. However, there are diverse views about the Granger causality and nature of this relationship. The purpose of this empirical study is to investigate how RE consumption on electricity generation is affected by economic growth and electricity prices using data from 1990 to 2021 with the vector auto regression (VAR) approach. This is done by using case study countries (Turkey, and Kazakhstan), which have different levels of economic development, are located in diverse regions in the world, but with common effort of encouragement in term of policy implementations in RE use recently. Findings in this paper show RE consumption, for the period under consideration, is significantly determined by income and electricity price in the long run.

Economic growth and energy consumption are interrelated for an economy and these variables are expected to indicate a positive correlation. This relationship has been probed in the study in the context of the United Arab Emirates for the time period 1996-2020. The study uses basic regression and Vector Auto Regression techniques for analysis. The study found that energy consumption and non-renewable energy consumption are positively correlated, are dependent on the country’s economic growth and this relationship is optimum at a lag of 7 years.

Airports, as integral components of the global aviation industry, experience dynamic changes in air passenger traffic load, which are also related to the trends of local urban development and airspace restrictions of nearby airports. Using time-series data from 2006 to 2019, this study comprehensively applied the autoregressive distributed lag and vector auto-regression mode approaches to identify causal relationships between the urban development factors, including GDP, population, tourism industry, industrial structure, etc., of Tianjin city (China); the Tianjin airport passenger flow; and the Beijing Capital airport's airspace restriction factor, namely, airport aircraft sorties. The results show that the growth of Tianjin city's GDP, primary industry and disposable income per capita was accompanied by a long-term decline in the passenger flow at the Tianjin airport. In addition, increased aircraft sorties of Tianjin airport, as well as the growth of primary, secondary and tertiary industries in Tianjin city, led to a short-term decline in passenger flow at the Tianjin airport. In general, there is variability in the long- and short-term impacts of urban economic structure on airport passenger flow, and this variability applies to other airports. The increased aircraft sorties at the Beijing Capital airport had a short-term positive impact on passenger flow at the Tianjin airport but resulted in a long-term decline of the latter's aircraft sorties. This phenomenon indicates that there is interaction between airports and that this influence varies depending on the competition and cooperation mechanisms between airports. The findings of this study are considered instrumental in guiding the competitive and cooperative strategies of nearby airports and predicting the coupled trends of the airport and urban development. [ABSTRACT FROM AUTHOR]

In actuarial statistics, distributions with heavy tails are of great interest to actuaries, as they represent a better description of risk exposure through a type of indicator with a certain probability. These risk indicators are used to determine companies' exposure to a particular risk. In this paper, we present a distribution with heavy right tail, studying its properties and the behaviour of the tail. We estimate the parameters using the maximum likelihood method and evaluate the performance of these estimators using Monte Carlo. We analyse one set of simulated data and another set of real data, showing that the distribution studied can be used to model income data. [ABSTRACT FROM AUTHOR]

This paper aims to measure the impact of COVID‐19 and changes in economic policy uncertainty (EPU) on the US stock market volatility using Event Study, Stepwise Regression, and Vector Autoregression (VAR) models. The impact has been examined under three contexts namely inside the US, outside the US and the whole world. This study finds negative cumulative abnormal returns of the market in response to the announcement of new cases and death records whereas the market reacts positively to the announcement of the financial bailout. The negative and significant impact of reported deaths and cases on the volatility of stock indices indicates that the US stock market is highly sensitive to the COVID‐19. EPU has a significant and positive impact on the volatility of the US stock market. The COVID‐19, EPU, and volatility variables are cointegrated and move in a unidirectional way. This study considers the presence of a long‐run relationship among the variables and concludes that the US stock market is more exposed to COVID‐19 compared with the rest of the world. [ABSTRACT FROM AUTHOR]

The study investigates bank fraud and its implications on the Nigerian economy. The study specifically examined how clearing fraud, financial forgeries, and financial embezzlement affect Nigeria's GDP. Secondary series data were obtained from the Central Bank of Nigeria's statistical database without bias from 1986 to 2020. Stationarity, cointegration, VAR Granger Causality, and VAR tests were estimated at a 95% confidence level. The stationarity test reveals that all variables were stationary at the first difference, resulting in the use of Johansen co-integration, which demonstrates the absence of longrun form. As a result, we used the VAR technique and discovered that clearing fraud and financial forgeries significantly slow economic expansion, whereas financial embezzlement reduces GDP but not significantly. According to the VAR Granger Causality, clearing fraud and financial forgeries influence GDP while financial embezzlement does not support GDP. We conclude that bank fraud reduces Nigeria's economic growth. In light of this, we recommend that commercial banks strengthen their internal control systems in order to detect and prevent fraud and fraudulent activities, as well as protect their assets, in order to more efficiently support economic growth. Bank regulatory and supervisory bodies in Nigeria must improve their supervision by utilising all available tools to appropriately check and curtain the incidence of fraud and fraudulent practises in the Nigerian banking industry. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between the financial market and the real business cycle in the US from February 1987 to March 2016. Using different monthly time-series as proxies for the financial and macroeconomic cycles, we first specify the determinants and then build two indicators to measure the financial and real business cycles based on principal component analysis. We identify not only the main different cycles for each indicator but also measure the duration of the phases for each indicator. Second, we study the relationship between economic and financial indicators per cycle and per phase using a vector autoregressive model. Our findings show that the economic indicator is useful to forecast the financial, and that there exists a significant relationship between the financial and economic cycles that is actively stronger during the "expansion–growth" phase. This result enables investors and policymakers to better forecast the future dynamics of financial sector using the information provided by the analysis of the real business cycle. [ABSTRACT FROM AUTHOR]

In this paper, we test the existence of regime changes by using Markov-Switching GARCH model for modeling volatility of the three country petroleum productions, i.e., namely India, Japan, and South Korea. We tested around 32 Markov-Switching GARCH (MSGARCH) models with different regimes (k =1,2,3) for the log- returns of each petroleum productions to test the in-sample analysis of volatility. We compare 32 models for in-sample estimations by using AIC and BIC and select the best fit model for all petroleum production series. Also, the results indicate that the one-day ahead VaR prediction was conducted by the best MSGARCH model. In addition, we test the transition matrix and stable probabilities for MCMC estimation with three regimes. Our results suggest that in most of the cases analysed there are regime changes in the GARCH process. We also find that the MSGARCH model outperforms single regime GARCH model specifications when forecasting the VaR. The results suggest that the use of MSGARCH models may provide accurate VaR forecasts, and hence useful in portfolio optimization and risk management etc. [ABSTRACT FROM AUTHOR]

Studying the property of the combination of renewable energy sources in the existing power systems is of great importance, and especially in the case of deregulated systems. The uncertainty of renewable sources is the largest barrier to integrating renewable-energy-producing units into the existing electrical infrastructure. Due to its uncertainty, integrating wind power into an existing power system requires extra consideration. In this work, the impacts of wind farm (WF) integration and a pumped hydroelectric storage system (PHES) on the electric losses, voltage profiles, generation costs, and system economy in a deregulated power market were studied. A comparative study was performed to determine the impact of wind farm integration on regulated and deregulated environments. Four locations in India were chosen at random for this work, and we used the real-time statistics for the actual wind speeds (AWSs) and forecasted wind speeds (FWSs) for each chosen location. To determine the system economy, surplus charge rates and deficit charge rates were developed to evaluate the imbalance cost resulting from the mismatch between the predicted and actual wind speeds. Considering the effect of the imbalance cost, the system profit/day varies by an average of 1.6% for the locations studied. Because of the reorganization of the power system, consumers constantly look for reliable and affordable power that is also efficient. As a result, the system security limit may be breached, or the system may run in a dangerous state. Lastly, in this paper, an economic risk analysis is presented with the help of heuristic algorithms (i.e., artificial bee colony algorithm (ABC) and moth–flame optimization algorithm (MFO)), along with sequential quadratic programming (SQP), and the way in which the PHES is used to compensate for the deviation in the WF integration in the real-time electricity market is also presented. The value at risk (VaR) and conditional value at risk (CVaR) were used as the economic risk analysis tools. According to the work, with the increase in the wind generation, the system risk improves. The results show that, as the wind generation increases by three times, there is an improvement in the risk coefficient values by 1%. A modified IEEE 14-bus test system was used for the validation of the entire work. [ABSTRACT FROM AUTHOR]

In this paper, we test cointegration between GDP and Public consumption of the Republic of North Macedonia, for quarterly data of twenty years' time series (2000Q1-2019Q4). We present results of two methods for cointegration test: first, residual regression test table and second, Engle & Granger cointegration test and Philips Ouliaris test. Both methods provides same conclusions. We did not find the presence of spurious regression. ADF Unit Root Test on residuals confirms that residuals are not stationary and that series are not cointegrated. Engle-Granger cointegration test and Phillips Ouliaris cointegration test results confirms that GDP and Public consumption of the Republic of North Macedonia are not cointegrated and can be used for further analyze using VAR(p) model.. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to evaluate the health and economic impact of the varicella vaccination program on varicella disease in the United States (US), 1996-2020.Methods: Analysis was conducted using the Centers for Disease Control and Prevention or published annual population-based varicella incidence, and varicella-associated hospitalization, outpatient visit, and mortality rates in the US population aged 0-49 years during 1996-2020 (range, 199.5-214.2 million persons) compared to before vaccination (1990-1994). Disease costs were estimated using the societal perspective. Vaccination program costs included costs of vaccine, administration, postvaccination adverse events, and travel and work time lost to obtain vaccination. All costs were adjusted to 2020 US dollars using a 3% annual discount rate. The main outcome measures were the number of varicella-associated cases, hospitalizations, hospitalization days, and premature deaths prevented; life-years saved; and net societal savings from the US varicella vaccination program.Results: Among US persons aged 0-49 years, during 1996-2020, it is estimated that more than 91 million varicella cases, 238 000 hospitalizations, 1.1 million hospitalization days, and almost 2000 deaths were prevented and 118 000 life-years were saved by the varicella vaccination program, at net societal savings of $23.4 billion.Conclusions: Varicella vaccination has resulted in substantial disease prevention and societal savings for the US over 25 years of program implementation. [ABSTRACT FROM AUTHOR]

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management. [ABSTRACT FROM AUTHOR]

The problem of reliability minimization is considered. One of the available approaches to solving this problem, namely bPOE, is analyzed. The advantages and disadvantages of this approach are determined. It is noted that the results of minimizing the probability of failures in the classical setting and minimizing the bPOE may differ. A new method of reliability optimization in the classical formulation of the problem is proposed. A comparative analysis of the results of reliability minimization using the bPOE and the results obtained by the proposed method is carried out. [ABSTRACT FROM AUTHOR]