Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19

Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast the direction of stock markets, the inclusion of leading indicators to volatility models is highly important;however, such series are generally at different frequencies. The paper proposes the GARCH-MIDAS-LSTM model, a hybrid method that benefits from LSTM deep neural networks for forecast accuracy, and the GARCH-MIDAS model for the integration of effects of low-frequency variables in high-frequency stock market volatility modeling. The models are being tested for a forecast sample including the COVID-19 shut-down after the first official case period and the economic reopening period in in Borsa Istanbul stock market in Türkiye. For this sample, significant uncertainty existed regarding future economic expectations, and the period provided an interesting laboratory to test the forecast effectiveness of the proposed LSTM augmented model in addition to GARCH-MIDAS models, which included geopolitical risk, future economic expectations, trends, and cycle industrial production indices as low-frequency variables. The evidence suggests that stock market volatility is most effectively modeled with geopolitical risk, followed by industrial production, and a relatively lower performance is achieved by future economic expectations. These findings imply that increases in geopolitical risk enhance stock market volatility further, and that industrial production and future economic expectations work in the opposite direction. Most importantly, the forecast results suggest suitability of both the GARCH-MIDAS and GARCH-MIDAS-LSTM models, and with good forecasting capabilities. However, a comparison shows significant root mean squared error reduction with the novel GARCH-MIDAS-LSTM model over GARCH-MIDAS models. Percentage decline in root mean squared errors for forecasts are between 39% to 95% in LSTM augmented models depending on the type of economic indicator used. The proposed approach offers a key tool for investors and policymakers.

VAR, ARIMAX and ARIMA models for nowcasting unemployment rate in Ghana using Google trends

The analysis of the high volume of data spawned by web search engines on a daily basis allows scholars to scrutinize the relation between the user's search preferences and impending facts. This study can be used in a variety of economics contexts. The purpose of this study is to determine whether it is possible to anticipate the unemployment rate by examining behavior. The method uses a cross-correlation technique to combine data from Google Trends with the World Bank's unemployment rate. The Autoregressive Integrated Moving Average (ARIMA), Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) and Vector Autoregression (VAR) models for unemployment rate prediction are fit using the analyzed data. The models were assessed with the various evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute error (MedAE), and maximum error (ME). The average outcome of the various evaluation metrics proved the significant performance of the models. The ARIMA (MSE = 0.26, RMSE = 0.38, MAE = 0.30, MAPE = 7.07, MedAE = 0.25, ME = 0.77), ARIMAX (MSE = 0.22, RMSE = 0.25, MAE = 0.29, MAPE = 6.94, MedAE = 0.25, ME = 0.75), and VAR (MSE = 0.09, RMSE = 0.09, MAE = 0.20, MAPE = 4.65, MedAE = 0.20, ME = 0.42) achieved significant error margins. The outcome demonstrates that Google Trends estimators improved error reduction across the board when compared to model without them.

Construction of SCUIR Propagation Model Based on Time-Varying Parameters

The novel coronavirus is a new type of virus, and its transmission characteristics are different from the previous virus. Based on the SEIR transmission model, this paper redefines the latent state as close contacts state, introduces an asymptomatic infection state, and considers the influence of time on the state transition parameters in the model, proposing a new transmission model. The experimental results show that the fitting accuracy of the model has significantly improved. Compared with the traditional model, the fitting error was reduced by 8.3%-47.6%. Also, this study uses the US epidemic data as the training set to predict the development of the US epidemic, and the forecast results show that the US epidemic cannot be quickly controlled in a short time. However, the number of active cases will usher in a rapid decline after August 2021.

Accounting the factor of randomity of social processes in prediction of demand for electric energy

Мета. Урахування фактору випадковосл сощальних процесш при прогнозуванш попиту на електричну енерпю для зменшення похибки. Методика. Апарат математично! статистики, методш лшшного програмування, теорп нечггких множин i методiв експертного оцшювання, теорй' шкал, Байесовський п1дх1д до моделей прогнозування, комп'ютерне моделювання. Результаты. Проаналiзована динамiка споживання електрично! енергп за рiзнi перiоди часу, встановлено вплив фактору пандемп на процес формування попиту на електричну енерпю. Розроблена вербально-числова шкала для комплексного оцшювання впливу на попит на електричну енерпю такого складного сощального явища, як пандемш. Сформована модель прогнозування попиту на електричну енерпю з використанням Байесовського подходу та ощнки експерта, що дозволила використати ретроспективш данi споживання електрично! енергп та врахувати невизначенiсть соцiального фактору впливу пандемп. Наукова новизна. Набула подальшого розвитку модель прогнозування попиту на електричну енерпю, яка, на вщмшу в1д iнших, ураховуе фактор випадковостi соцiальних процеив i вербально-числову шкалу, що дозволяе зменшити похибку прогнозування споживання електрично! енергп. Практична значимтсть. Результата дослщження кориснi для пщприемств, що спецiалiзуються на генерацй', передачi й розподшу електрично! енергп споживачам. Представленi результата надають можливють зменшити похибку прогнозування попиту на електричну енерпю при врахуванш фактору випадковосл сощальних процешв.Alternate :Purpose. Taking into account the factor of randomness of social processes when forecasting the demand for electric energy to reduce the error. Methodology. Apparatus of mathematical statistics, linear programming methods, fuzzy set theory and expert assessment methods, scale theory, Bayesian approach to forecasting models, computer modeling. Findings. The dynamics of consumption of electric energy for different periods of time is analyzed, the influence of the pandemic factor on the process of formation of demand for electric energy is established. A verbal-numerical scale has been developed for a comprehensive assessment of the impact on the demand for electric energy of such a complex social phenomenon as a pandemic. A model for forecasting the demand for electrical energy was formed using the Bayesian approach and an expert's assessment, which made it possible to use retrospective data on electrical energy consumption and take into account the uncertainty of the social factor influencing the pandemic. Originality. The model for forecasting the demand for electrical energy has been further developed, which, unlike others, takes into account the factor of randomness of social processes and a verbal-numerical scale, which makes it possible to reduce the error in predicting the consumption of electrical energy. Practic l value. The research results are useful for enterprises specializing in the generation, transmission and distribution of electrical energy to consumers. The presented results make it possible to reduce the error in forecasting the demand for electric energy, taking into account the factor of randomness of social processes.

Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications

The grid operation and communication network are essential for smart grids (SG). Wi-SUN channel modelling is used to evaluate the performance of Wi-SUN smart grid networks, especially in the last-mile communication. In this article, the distribution approximation of the received signal strength for IEEE 802.15.4g Wi-SUN smart grid networks was investigated by using the Rician distribution curve fitting with the accuracy improvement by the biased approximation methodology. Specifically, the Rician distribution curve fitting was applied to the received signal strength indicator (RSSI) measurement data. With the biased approximation method, the Rician K-factor, a non-centrality parameter (rs), and a scale parameter (σ) are optimized such that the lower value of the root-mean squared error (RMSE) is acheived. The environments for data collection are selected for representing the location of the data concentrator unit (DCU) and the smart meter installation in the residential area. In summary, the experimental results with the channel model parameters are expanded to the whole range of Wi-SUN’s frequency bands and data rates, including 433.92, 443, 448, 923, and 2440 MHz, which are essential for the successful data communication in multiple frequency bands. The biased distribution approximation models have improved the accuracy of the conventional model, by which the root mean-squared error (RMSE) is reduced in the percentage range of 0.47–3.827%. The proposed channel models could be applied to the Wi-SUN channel simulation, smart meter installation, and planning in smart grid networks.