Forecast Values Research Articles

Singular-spectrum time series analysis for oil and gas industry production forecasting

The article considers theoretical aspects of using the model of singular-spectral analysis of time series with the use of decision trees, and also provides justification of the feasibility of using this model for forecasting the production of products for both oil and gas industry and dual-use products. To reduce the risks of distortion of aggregated forecast values when working with large data sets, the expert should carry out preliminary manual selection and exclusion of products, the release of which has been completed or suspended.

МАКРОЕКОНОМІЧНА СТАБІЛЬНІСТЬ УКРАЇНИ В УМОВАХ ВОЄННОГО СТАНУ

The article is devoted to the analysis of the level of macroeconomic stability of Ukraine based on a set of indicators, the values of which are characterized by unusual trends against the background of the war. The analysis is mainly built on the basis of determining the current and forecast values of GDP, the discount rate, the average exchange rate of the hryvnia, the consumer price index, customer funds among the banking sector, the credit portfolio of the banking sector, the ratio of reserves to the credit portfolio of the banking sector. The stability of macroeconomic indicators determines the possibility of preserving or losing potential for development, which is the basis for the formation of strategic guidelines. The quality and timeliness of the analysis, the validity and accuracy of the interpretation of the results increases significantly in the conditions of crisis phenomena and insurmountable circumstances, in particular, in the conditions of martial law in Ukraine, which is the basis for planning at the level of various economic systems and a condition for the purposeful use of resources, the increase or reduction of business activity. The purpose of this article is to analyze the set of macroeconomic indicators of Ukraine in the context of determining the prospects of its economic dynamics in the conditions of martial law. For this purpose, a study of key macroeconomic indicators of Ukraine was conducted using the methodology of forecasting with the application of regression studies of time series and the autoregressive moving average (ARIMA) model. The dynamics of the main indicators of macroeconomic stability in the period before and after the start of the full-scale invasion were determined. The impact of economic crises caused by exogenous factors on indicators of macroeconomic stability was evaluated, the degree of their volatility was analyzed, which made it possible to adjust the obtained results and obtain forecast indicators (a decrease in GDP for a period of 5 years.

Управление качеством углей по критерию их опасности с использованием цифровых моделей полезного ископаемого на Кутинском буроугольном месторождении

The purpose of the research is to substantiate the need for preliminary planning with the use of block models in the extraction of TPI as an important element of designing an economically and environmentally efficient enterprise for the development of a deposit of solid minerals. The object of the study is the Kuta brown coal deposit, which has areas of coal with increased concentrations of natural radionuclides. The method of planning the quality of coals, taking into account the hazard criterion and products of their processing with the use of Micromine Alastri software (software) is considered. The objectives of the study are to test the author’s hypothesis regarding the use of BM in the process of predicting the quality of the extracted PI according to the obtained characteristics. Expected results of the scientific study: it is expected that the specific activity in the samples of dust and slag will be recorded several times; when conducting field studies on the content of carbon in coal seams and coal dust with the inclusion of rock, it is necessary to establish the regularity of increasing the value of Aeff in coal dust with the inclusion of rock particles relative to coal samples. The results of the interpolation of the contents of the ORE into the blocks of the digital block model of the coal seam I of the Kuta brown coal deposit are presented. In conclusion, the author highlights the methodology for determining the parameters of mined coal and coal products, taking into account the hazard criterion, which has the following logic: burning of an experimental batch of coal; analysis of the correlation regularity between the average content of OER in coal by block and the average content of OER in coal combustion products, determination of the dependence of the oxygen content in coals and combustion products; assignment by reverse interpolation of the obtained forecast values to each three-dimensional block of the block model included in the mining blocks for each horizon and their transfer to the maps of horizon-by-horizon mining planning. Thus, obtaining a safety map of the extracted PI with the predicted values of the content of N in the combustion products.

ПРОГНОЗИРОВАНИЕ ИСПАРЕНИЯ С ВОДНОЙ ПОВЕРХНОСТИ С ПОМОЩЬЮ МАШИННОГО ОБУЧЕНИЯ

In studies carried out at the Krasnodar Rice Institute, as well as according to data from world rice cultivation centers, it has been shown that evaporation has the greatest effect on rice yield from climatic factors. Evaporation without any significant errors can be taken as evaporation from the waters of water bodies, while the value of rice transpiration most often has a fairly significant correlation or functional relationship with evaporation from water bodies. From the short-term weather forecasts issued by regional subjects of Roshydromet and other organizations, it is possible to obtain forecast values of temperature, wind speed and precipitation with a five-day (pentad) advance. It was on their basis that a method for predicting evaporation for the future pentad was developed. In this paper, the hypothesis of the possibility of predicting the values of evaporation from the water surface of rice checks by the main predicted meteorological elements using neuro-machine learning technologies is tested. In the course of the work, a machine learning algorithm was compiled aimed at approximating the amount of evaporation from the water surface based on forecast data of meteorological parameters. This technology can be used as a new technique for medium-term forecasting of evaporation for the pentad period. In order to obtain the amount of evaporation, it is quite possible to obtain a forecast dependence on the values of air temperature and wind speed, provided there is no precipitation, when such a forecast is most important.

Use of modern management mechanisms in digitalization of housing construction study status

Underlying all the reforms implemented in our country is the noble goal of ensuring human interests. In this regard, the fundamental changes implemented in life are aimed at increasing the welfare of our people. In particular, issues such as improving the quality of housing and communal services, improving the housing stock management system are being solved step by step. Since there is no single system in this regard, all countries carry out this activity in their own way or based on the experiences of advanced countries. Today there are 32,000 contracting organizations in Uzbekistan. By September 1, 2020, the task was set to launch a single platform covering information such as financial indicators of construction enterprises, solvency, state procurement, and employee qualifications. This platform will be introduced in Tashkent until November 1, and throughout the republic from the beginning of next year. The electronic labor record system will also be integrated into it. This article describes housing, housing construction, digitization, modern management system, use of management mechanisms, application of IT technologies in housing management mechanisms, correlation analysis, regression analysis, and forecast values of essential indicators related to housing stock management.

Cointegration and Causality Relationship of the USA Stock Market with Selected World Markets

This article, based on data from 1995 to 2022, considers the commercial and economic processes between the USA, Japan and China and stock indexes indicators of these countries. In the course of research, the authors have created multiply regression model, adequacy of the model was determined using the Fisher’s F-test, Student’s t-test, and the stability of the parameters of the regression model was checked using the CUSUM test. Stationarity of the time series was checked by the Dickey-Fuller test. As a result, econometrically sound recommendations are developed, which allows to conduct dynamic analyses to effectively regulate economic processes, integration and international trade, foreign investment operations between the three countries. The Granger causality of the relationship was investigated. Johansen tests were implemented to find the cointegration space, after which a vector error correction model was constructed that describes the long-term equilibrium relationship between the studied indicators and the path to return to the equilibrium trajectory in case of deviation from it. During the modelling, all necessary statistical procedures were used to identify and evaluate the parameters of the model and check its adequacy, accuracy of short-term and long-term forecast values using Eviews 8 tools. The results show that not only for the countries studied here, but also for each country, conduction of monitoring by the governments by using the methodology of the vector model of error correction is very important in order to ensure effective regulation of foreign trade and to participate in regional and global integration processes.

AN EFFICIENT HYBRID METHOD TO PREDICT WIND SPEED BASED ON LINEAR REGRESSION AND VMD

To effectively improve the power dispatching, the prediction accuracy of wind power has been the concern of many scholars for many years. The wind power prediction problem is actually equivalent to the wind speed prediction problem. Based on linear regression (LR) and variational mode decomposition (VMD), in this paper, we proposed an efficient hybrid method to predict wind speed. In the proposed method, the VMD is used to decompose the signal of wind speed into several sub-signal. Compared with the original wind-speed series, each sub-signal is a more stable subsequence signal. Then, we used the LR method to predict each subsequence signal. Eventually, we obtain the final prediction results of the original wind speed series merged the forecasting values of all subsequences signal. We selected two data to test our proposed method in our experiment. Compared with several comparison methods, we found that our proposed methods has better prediction performance than other methods from the experimental results.

ВИКОРИСТАННЯ ПРОГНОЗУВАННЯ ЗА ДОПОМОГОЮ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ ПРИ ВИВЧЕННІ КОНКУРЕНТОСПРОМОЖНОСТІ ПРИМОРСЬКИХ РЕГІОНІВ

The use of the mechanism of regional competitiveness enhancement is to maintain and strengthen the region's resource, intellectual, social and human capital. The article is devoted to the study of the possibilities of using artificial neural networks for forecasting indicators of competitiveness, which have a direct impact on subsystems and elements of competitiveness of regions. The practical application of the method of learning artificial neural networks allows you to predict the scenarios of the development of the indicator, confidence intervals, and the amount of the forecasting error. The analysis data can be used in planning the strategic development of the region, studying trends in indicators. The use of software greatly facilitates and simplifies the process of calculations and allows you to obtain data in graphical and calculated form, which is a necessary condition for the interpretation and analysis of data by economists. Forecasting regional competitiveness indicators with the help of neural networks allows testing, obtaining several forecast results and evaluating the forecast results by calculating the average error. Comparison of the obtained values of the calculated average error allows you to choose the forecasting result that is the most acceptable and possible for further use. Taking into account the values of forecast indicators of the region's competitiveness is necessary when planning the region's development strategy, when choosing strategic priorities and initiatives, emphasis is placed on achieving the forecast values of the «markers» of the strategy. Using the example of the indicator of the gross regional product of the region, calculations of the forecast of changes in the indicator were carried out using the Statistica software by the method of artificial neural network modeling. The gross regional product is an indicator chosen for analysis due to certain of its strong positive aspects: universality of the comparative base, availability of statistical data, economic sense. The study of competitiveness requires an understanding of the prospects for the development of its indicators, since their management affects the overall indicator. Competitiveness forecasting makes it possible to create a prognostic scenario of the development of both competitiveness factors, its components, and the object of research in general. However, the use of forecasting tools requires knowledge of certain nuances that accompany forecasting: peculiarities of using software tools, checking the forecast by calculating the standard error, etc.

Simulation of the impact of foreign direct investment on the Ukraine’s economy in the conditions of post-war reconstruction

The article examines the dynamics of accumulated foreign direct investment in the economy of Ukraine in 2010–2021 and analyzes the reasons for the significant decrease in FDI inflows to the economy of Ukraine, the withdrawal of significant amounts of capital by foreign investors in periods of deepening economic crisis. The structural changes of foreign direct investment in the Ukrainian economy by types of economic activity and by industry for the period 2015–2021 are analyzed. With the help of regression analysis, modeling of the dependence of economic growth of Ukraine on changes in the volume of foreign direct investments was carried out under the condition that the war ends in 2023. To build a linear regression model, data on the GDP of Ukraine, GDP per capita, the volume of accumulated FDI in the economy and their annual revenues, the volume of FDI in the main sectors of the economy in 2010–2021 and the forecast values of these indicators in 2023–2025. The results of the study confirmed the importance of increasing both the total volume of FDI and the volume of investment involved in high-tech industries industry, to restore the economy of Ukraine after the end of the war. Regression analysis made it possible to reveal a direct connection between the economic growth of Ukraine and the volume of accumulated FDI in the economy and their annual inflow. Based on the model of linear dependence of Ukraine's economic growth on foreign direct investments, forecast scenarios of changes in Ukraine's GDP in actual prices and GDP per capita in 2023 were developed, provided that the volume of accumulated FDI in the economy increases. As a result of the study, no statistically significant dependence of the economic growth of Ukraine on the volume of accumulated FDI in the metallurgical industry and in the in the field of information and telecommunications sector was found. The regression analysis confirmed the significant direct dependence of Ukraine's economic growth on the volume of FDI involved in the machine-building industry, which proves their important role in the implementation of post-war economic recovery projects of Ukraine.

РОССИЙСКИЙ ИНСТИТУТ САМОЗАНЯТОСТИ: РАЗВИТИЕ В КОНТЕКСТЕ ПРОСТРАНСТВЕННЫХ ОСОБЕННОСТЕЙ

In his article, the author describes what he found when analyzing the formation and development of the institution of self-employment in the Russian Federation, both taking into account the diversity and specifics of Russian regions and identifying the reasons for this institution’s active introduction into the Russian economy and social sphere in modern conditions, as well as the positive results obtained. The study showed that in all Russian regions there is a significant increase in the number of self-employed, exceeding the planned and forecast values presented in the federal program “Creating favorable conditions for the activities of self-employed citizens” (2018). Statistics by federal subjects of Russia indicate that the spatial development of the institution of self-employment is combined with a significant decrease in the unemployment rate in the vast majority of Russian regions. However, it should be noted that after the introduction of this institution there were also negative effects, including the switch of skilled workers from SMEs and even from large enterprises to self-employment, which aggravated the problem of labor shortage in some regions. On the whole, it can be stated that in Russia this institution is now adequately fulfilling its functions in the economy and in the social sphere — bringing small business entities out of the shadows and fighting unemployment. In the context of modern challenges and strategic tasks, these two functions are essential for the Russian economy and society.

ОЦЕНКА РЫНКА БРОКЕРСКИХ УСЛУГ НА ОСНОВЕ СОВРЕМЕННЫХ ТЕХНОЛОГИЙ МАРКЕТИНГОВЫХ ИССЛЕДОВАНИЙ

It was determined on the basis of marketing research that according to official information of the customs authorities in 2017, customs procedures for customs transit in electronic form on the principle of a “single window” became mandatory and as a result of customs operations, 4,4 million units of customs declarations were issued by the customs authorities of the Russian Federation, which significantly increased the level of revenues to the state budget from customs duties and payments. The estimation of the volume of customs declaration processing in the Russian Federation, units for 2017-2022 is presented. It is assumed that in the period up to 2028, according to experts in the customs services market, there will be a certain transition period in the activities of customs brokers, during which the customs market will undergo a serious path of adaptation to tax and customs legislation and new or existing requirements for customs legislation will be introduced or improved (2019-2021), which indicates that outsourcing in the customs activity will be within the expected value of 1-2%, according to forecasts. It is indicated that in the period 2021-2028. it is planned to conduct and organize a post-adaptation period, during which it is expected to reduce outsourcing operations in a shared ratio, taking into account the statistical error with an annual rate of 5-7%. Summarizing the opinions of experts and the forecast values of the development of the customs services market and the activities of customs brokers, the trends in the development of the activities of customs brokers are determined. Customs brokers will always be in demand also because the demand for competent and highly specialized specialists is increasing, such specialists are being trained by educational organizations of higher education, the list of their professional competencies is being improved, requirements are increasing and indicators for checking the KPIs of customs brokers are expanding.

Forecasting the sectoral structure of population employment

All the labor market subjects that can influence the labor resources dynamics are interested in employment forecasts by labor market sectors. Such subjects are state employees and municipal employees, employers and workers. The statistical data aggregation degree affects the quality of the labor resources dynamics forecasting. Each labor market indicator combines a set of detailed indicators in a high degree of aggregation case. When building the trends it is impossibile to take into account information on the detailed indicators trends. The labor market indicators for each specific year don’t contain information about the interaction with each other. This fact also negatively affects the forecast quality. The article discusses the use of a balance mathematical model of the labor resources dynamics, which relates the labor market sectoral indicators, to define the intersectoral movements indicators. The authors consider a calculating labor market indicators method that uses only statistical data on sectoral employment and unemployment. Thus, the statistical data on the labor resources dynamics provided by the Federal State Statistics Service is a sufficient condition for the Russian Federation labor market detailing using intersectoral movements’ indicators. The paper shows how a set of intersectoral movements indicators allows building the forecast values of these indicators and using them to calculate the forecast values of labor market indicators. The article considers examples of building employment estimates by Russian Federation economy sectors for 2011–2016 and 2019. The entry into force of the All-Russian classifier of types of economic activity second edition in 2017 is the reason for choosing such research intervals. The purpose of these examples was to determine the impact of the detailed labor market indicators of the sectoral employment estimates reliability. The authors compared the forecast obtained directly from labor market indicators with the forecasts obtained from intersectoral movements indicators. Intersectoral movements indicators are the results of applying balance models with varying degrees of detail. The reliability tables presented in this work to assess the forecasting quality indicate that the detailing of the sectoral employment indicators by using the balance model can increase reliability of the forecast.

Forecast Value Added in Demand Planning

Forecast Value Added in Demand Planning

МЕТОДОЛОГІЧНІ ПІДХОДИ ДО УПРАВЛІННЯ ЕКОНОМІЧНИМ РОЗВИТКОМ ПІДПРИЄМСТВ ГОТЕЛЬНО-РЕСТОРАННОГО БІЗНЕСУ

Introduction. Economic development largely depends on the rate of economic growth, which is characterized by maximizing the level of efficiency and minimizing production costs. Solving these tasks causes the need to define methodological approaches to management of economic development of enterprises through optimization and rationalization of business processes. Purpose. The purpose of the article is the formation of the methodology of management of economic development of enterprises in the hotel and restaurant sphere on the basis of economic and mathematical modeling of their operational activities. Results. With the help of correlation and regression, statistical, and stochastic analysis, economic and mathematical models were developed, coefficients were calculated, which allow to establish differentiated and integrated parameters of resource potential development, trends of economic growth of hotel and restaurant business establishments. The model of dynamic economic development of reserves of the resource potential of operational activities was built. The methodical toolkit for the formation of the process of management of economic development of enterprises by establishing the patterns of mutual influence of the parameters of individual business processes, determining the order and structure of their interaction, calculating the forecast values of indicators of efficiency and resource intensity, coefficients for assessing the degree of influence of resource consumption factors on the profitability of operational activities was developed. Based on these approaches, the methodology of analysis, assessment and forecasting of dynamic economic growth and development of resource potential reserves of hotel and restaurant enterprises was formed. Conclusion. Correlation dependence models constructed with the help of the proposed methodical toolkit allow to establish and evaluate the influence of a factor characteristic on a criterion one; mathematically describe patterns of profit growth due to the most rational use of the potential of production resources; predict the quantitative ratio of resource costs and the level of activity efficiency; plan the results of business processes by determining the amount and structure of operating costs, and therefore to manage economic development of hotel and restaurant business enterprises.

Assessment of dynamic line rating forecasting methods

• Forecasting electric power transmission line ampacity is presented here to be pivotal to achieving an increased line rating. • Deep learning techniques and regression were used to identify patterns in the environmental data and forecast DLR at different lead times. • This article compared forecasting techniques and evaluated them with point and probabilistic error metrics. • Point Error metrics gave the discrepancy between the forecasted and actual values, while the probabilistic error metrics gave the confidence level of the forecast values of the techniques. Optimal transmission line rating use is guaranteed with dynamic line rating (DLR). It is a smart grid technology that foresees variations in meteorological conditions affecting line rating and deploys algorithms to effect changes to the line rating due to these conditions. Electric power system operators use forecasted DLR for system planning, operation, and delivery. This study reviewed DLR forecasting techniques, classified them, implemented them, and compared their outputs at different lead times. It used ensemble means forecasting, recurrent neural network (RNN), and convolution neural network (CNN). Ensemble forecasting technique deployed in this study involves a Monte-Carlo simulation that produces random, equally viable predicting solutions. Alternatively, a neural network layer's initial outcome is fed back into it to predict the output in RNN, while CNN learns to predict features that vary in time and space with marginal discrepancies. This study used quantile regression (QR), ensemble forecasting, RNN and CNN to forecast DLR at 12hrs, 24hrs and 48hrs. The tested forecasting approaches prove efficient, but ensemble forecasting seems less error-prone, more secure and conservative among all methods. On average, 75th percentile quantile regression and ensemble forecasting demonstrate better reliability and avail us the better choice of ampacity among the forecasting techniques.

MATHEMATICAL PARADIGM IN ASSESSING THE COMPETITIVENESS OF AN INDUSTRIAL ENTERPRISE

Objective. Research and build mathematical models of indicators of the competitive position of an industrial enterprise that can be used for forecasting and corresponding comparative analysis. Methods. Modeling and comparative analysis of the competitive position of an industrial enterprise on the basis of extrapolation (determining the predictive value of factors affecting the competitive position of the enterprise). Identification of discrete time series (formation of a mathematical model of a discrete time series of the competitive position of an industrial enterprise).. Results. Analytical dependences on the competitive position of the enterprise and relevant factors of influence for the enterprises of the Kryvyi Rih region were built. The main components of the impact on competitiveness are analyzed: the volume of implementation; Net profit; market share on the product market; intensity of competition in the industry; the ratio of the market share of the analyzed enterprise to the market leader. For the first time, the identification of discrete time series was obtained in order to determine the competitive position of an industrial enterprise. The application of the extrapolation method made it possible to determine the forecast values of factors affecting the competitive position of the enterprise. Based on the identification of discrete time series, mathematical models of discrete time series of the competitive position of an industrial enterprise were built. The obtained research results are recommended to be applied in practical activities regarding the strategic management of an industrial enterprise in order to make effective decisions. The use of the identification of discrete time series will allow an appropriate assessment of the competitive position of the industrial enterprise and the relevant influencing factors.

Assessment and Forecasting of Metallurgical Enterprises Carbon Footprint in the Sverdlovsk Region

The achievement of the targets of reducing greenhouse gas emissions is ensured, among other things, through direct price regulation measures, for the implementation of which a necessary condition is the calculation of the carbon footprint of products. At the same time, carbon footprint targets can be used as driving parameters to set industry targets for reducing greenhouse gas emissions, as well as to provide government support measures for enterprises. The aim of the work is to develop a methodological approach to the use of the «carbon footprint of products» indicator as a parameter for assessing the current and forecast values of greenhouse gas emissions from enterprises in industries that are subject to carbon regulation. The hypothesis of the study is to justify the use of target values of the indicator «carbon footprint of products» for strategic estimates of greenhouse gas emissions in the process of industrial decarbonization. For this purpose, the paper considers the organizational and economic mechanism of the European emissions trading system. A methodical approach to determining the carbon footprint of metallurgical products is described, and calculations of the carbon footprint of metallurgical enterprises in the Sverdlovsk region are performed. Calculations based on average indicators of the carbon intensity of products of Russian metallurgical enterprises showed that the total carbon footprint of the metallurgical sector of the Sverdlovsk region in 2021 is 21.8 million tons of CO2-eq; its target value may be 16.7 million tons of CO2-eq . Differences in values are due to the existing structure of manufactured products and applied technologies. The development of standard metrics for assessing greenhouse gas emissions at the enterprise level, regularly conducting such an assessment and its verification by an independent organization may constitute a preparatory stage that makes it possible to launch a mechanism for trading greenhouse gas emissions at the regional or country level. Additionally, the carbon footprint of products can act as a control parameter while establishing quantitative values for the volume of greenhouse gas emission reductions. The proposed methodological approach can be used by decision makers to shape regional decarbonization policies.

IFRS 도입 이후 경영자 예측치 발표에 따른 재무분석가 예측치 수정 기간의 변화

[Purpose] Management forecasts are recognized as more accurate than analysts forecasts under higher information uncertainty. The switch to IFRS is known as an increase in information uncertainty. We aim to find the impact of IFRS on the analyst’s use of management forecasts in revising their earnings forecasts.
 [Methodology] Focusing on the speed to reflect management forecasts into the analysts’ forecast revision, we use OLS regression to investigate the change in time spent in the revision before and after IFRS adoption. Further, we examine the moderating effect of the information amount in management forecast on the days it takes for an analyst to revise the forecast after the IFRS was adopted.
 [Findings] We find that analysts revise their earnings forecasts more quickly in the post－IFRS period than in the pre－IFRS period when the management forecast is announced. This analyst’s rapid response to management forecasts in the post－IFRS period weakened as the gap between the management forecast and the analyst’s most recent forecast is large. Further, as the management forecasts were pessimistic or inaccurate, the analysts need more time to incorporate the information in the forecast revision.
 [Implications] Our findings imply that the adoption of IFRS increases the informative value of management forecasts and that the news and the reliability of management forecast significantly affect the analyst’s use of management forecasts in revision earnings forecasts. In particular, it can be evaluated as using a more sophisticated methodology than prior studies in that financial analysts analyze the time spent revising their own forecasts.

How Machine Learning Can Be Used To Improve Predictive Analytics

Abstract: Machine learning algorithms are being used to improve predictive analytics. These algorithms are getting better at predicting future data. This is a great thing. But there are a few things you need to know before you begin to use these algorithms. Machine learning is an important part of predictive analytics and can increase the speed of data analysis and processing. It also allows predictive analytics algorithms to learn on larger data sets and conduct deep analysis on multiple variables. As a result, machine learning has become an important part of many businesses. Though there is still some controversy regarding its use, many industries are successfully implementing it. This technique has a long history of use in the financial sector, particularly in banking and investing, E-commerce, Customer service, Medical Diagnosis, Sales and Marketing, Financial Services, Cybersecurity and can help organizations forecast asset values. Additionally, it can help users understand the relationships between variables.

Assessing Long Strangle Strategy in Gold Price Index Using 1985-2020 Data

According to the gold price index from 1985 to 2020 data cited from www.gold.org with the XAU code, the gold price index fluctuated between these periods. Price index fluctuation impacted to daily return value. Fluctuations in returns indicated that there was price volatility. Concerning its price, it is necessary to have risk management to anticipate its volatility. The options strategy can be used to minimize the risk of a volatile market. There are two kinds option such as Call Option (right to buy) and Put Option (right to sell). This research aims at testing implementation of options model between Black Scholes and GARCH on gold price index using Long Strangle strategy which data came from www.gold.org year 1985 till 2020. By employing comparative research, this research seeks to compare the errors that arise between the Black Scholes method with historical volatility and the Black Scholes method with GARCH. The accuracy testing was done using with an error value using the Average Mean Square Error (AMSE) method. This method is the average squared error between the forecast value and the actual value, which is usually used to check the estimated error value of the forecast. AMSE values that are low or close to zero indicate forecast results that are following actual data and are stated to be good values compared to those far from zero. Maturity periods of option contract are using 3 methods such as 1-month, 2-months and 3-months. Keywords: Gold Price, Options Contract, Black Scholes, GARCH, Long Strangle, AMSE