ARIMA-GARCH Model Research Articles

Forecasting stock index closing points using ARIMA-GARCH with a rolling data window

This research develops and examines the efficacy of a hybrid ARIMA-GARCH model, augmented by a rolling data window approach, to enhance the accuracy of stock index prediction, specifically focusing on the NEPSE index. Accurate predictions of stock indices are of paramount importance to investors, analysts and policy makers to navigate and circumvent the market uncertainties. The AutoRegressive Integrated Moving Average (ARIMA) model captures linear trends and temporal dependencies in time series data, while the General AutoRegressive Conditional Heteroskedasticity (GARCH) model addresses volatility clustering—ubiquitous character of financial time series—thereby providing a comprehensive framework for prediction. Utilizing a dataset comprised of daily closing points of NEPSE index approximately three years and nine months, the study identifies the ARIMA (5,1,0)-GARCH (1,1) model as optimal fit, upon integrating 180-day rolling data window. This model achieved a Mean Percentage Error of -0.0058% and a correlation of 0.995, which is indicative of superior fit to the underlying time series data. These findings underscore the hybrid model’s capacity to adaptively respond to dynamic market conditions and acclimatize prediction parallel to most recent market trends and volatility. This research is useful for optimizing investment strategies for those invested in Nepalese stocks. Also, this research lays a foundational framework for future investigations into application of this advanced forecasting method in other emerging markets, financial instruments and indices.

A study of fresh produce pricing and replenishment strategy based on FCM cluster analysis and ARIMA-GARCH time series analysis modelling

Aiming at the shortcomings of poor robustness and weak applicability in the formulation of pricing and replenishment strategies of fresh food superstores and the limitations of ARIMA model in pricing strategies, this paper is based on maximizing superstores' revenue, and proposes to establish an FCM cluster analysis model based on the elbow rule to analyse the correlation of different single products, and concludes that the sales volume of edible mushrooms and aquatic roots and tubers have a strong correlation, and spinach and choy sum have a strong correlation. Then the total sales volume of each category was negatively correlated with cost-plus pricing through least squares fitting. Finally, based on the ARIMA-GARCH model, a daily replenishment model was established, and it was predicted that the maximum daily replenishment of foliage and flowers would reach 213.908kg on 2 July, and the minimum daily replenishment of eggplant would reach 8.415kg on 4 July in the coming week.

Bi-level electricity and heat sharing strategy for cross-border integrated energy system based on Nash game

As global energy demands continue to rise and the need for sustainable development becomes more urgent, Cross-Border Integrated Energy Systems (CBIES) offer a crucial pathway to improving energy efficiency and security through international collaboration. This paper proposes a bi-level electricity and heat sharing model based on Nash game theory, which optimizes resource allocation and management in cross-border energy systems. The model addresses two sub-problems: maximizing the overall benefits of the alliance and ensuring fair redistribution of those benefits within the alliance. To tackle the complexities of these problems, the Alternating Direction Method of Multipliers (ADMM) is employed in a distributed manner. Additionally, the ARIMA-GARCH model is introduced to handle the uncertainties related to exchange rate volatility in cross-border energy transactions. Carbon capture and storage (CCS) and power-to-gas (P2G) technologies are also integrated into the cogeneration system to promote low-carbon energy production, aligning with environmental sustainability goals. The results show that the upper-level electricity sharing sub-problem reduced the total operational cost from 1,173,330 CNY to 875,582 CNY after 55 iterations, while the lower-level heat sharing sub-problem further optimized the cost to 687,356 CNY after 14 iterations. Post-cooperation cost comparisons showed that the costs for Country B and Country C were reduced by 207,828 CNY and 390,672 CNY, respectively, demonstrating the model’s effectiveness.

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

PENENTUAN VALUE AT RISK (VAR) PADA PORTOFOLIO BIVARIAT DENGAN PENDEKATAN COPULA GUMBEL

One way to minimize risk in stock investment is stock portfolio. Value at Risk (VaR) is a calculation method that can be used to estimate the risk of a stock portfolio. VaR can be measured by parametric and non-parametric approaches. Calculation of VaR with Monte Carlo simulation assumes the data is normally distributed. Stock return data generally has high volatility so that the residual variance of the model is not constant (heteroscedasticity) and not normally distributed. The ARIMA-GARCH model can be used to solve heteroscedasticity problems. Copula is a tool used to model the combined distribution of residuals from the ARIMA-GARCH model which does not require normality assumptions. Gumbel's copula is copula that has the best sensitivity to high risk. This study uses stock data of PT Bukit Asam Tbk (PTBA) and PT Chandra Asri Petrochemical Tbk (TPIA) for the period April 1 2020 – December 1 2022. The initial step of this research is model stock returns using the ARIMA-GARCH method and then calculate portfolio VaR using the Gumbel’s copula. The results showed that the best model for PTBA is ARIMA(2,0,2) GARCH(1,1) and for TPIA is ARIMA(1,0,0) GARCH (1,1). At the 95% confidence level, the portfolio risk is 2,41%.

Handling Volatility and Nonlinearity in Wind Speed Data: A Comparative Analysis between ARIMA-GARCH and ARIMA-MLP

One of the notable features of wind speed is its volatility and nonlinearity. Thorough assessment on the presence of these features is crucial to obtain a wind speed forecasting model with higher accuracy. In this study, the conventional time series linear model; ARIMA model was applied to assess the internal structure of the wind speed daily data in two stations in Johor; Senai and Mersing. Due to the existence of some nonlinearity features in the residuals part of ARIMA modelling, two nonlinear models were introduced to capture the internal structure of the residual data. Both conventional time series models; GARCH, and machine learning model; MLP was applied to model the residuals of ARIMA model. The out-sample performance in forecasting accuracy was compared between the ARIMA-GARCH model and the ARIMA-MLP model. The findings proves that MLP model has outperformed GARCH model in capturing the dynamics in the residual data by providing the lowest error measurements. Thus, the machine learning approaches has proven its superiority against the conventional time series nonlinear model in handling the nonlinearity in the daily wind speed series for wind speed forecasting.

Analysis of the Medical Industry in the Stock Market under the Influence of the Pandemic in China

This paper is working to analyze the influence of COVID-19 on the medical industry in Chinas stock market. Because the pandemic has influenced our society and development impressively, using statistical methods to analyze its influence is a necessary method to estimate the risk of investment. Some common statistic analyzing methods, the ARIMA model and GARCH model, will be used in this paper, and the data of the stock price of a medical manufacturing company Zhangzhou Pientzhng Phrmctcl Co Ltd during the COVID-19 is collected and introduced in the models. Meanwhile, machine learning is also implemented in the prediction and analysis of the data. The results of these two models both demonstrate the high volatility in the stock market under the influence of pandemic, which is meaningful for further research about the risks of future investment in medical industry of Chinas stock market.

ARIMA-GARCH MODEL IN OVERCOMING HETEROSCHEDSDATICITY IN STOCK PRICE PREDICTION (CASE STUDY: PT INDOFOOD, TBK (INDF))

Ensuring access to future stock price information holds significant weight for investors in formulating investment strategies. The Indonesian capital market serves pivotal economic and financial roles within the economy, offering various instruments, such as stocks. Among these, blue-chip stocks are recognized for their minimal risk exposure. The impact of the COVID-19 pandemic is anticipated to influence stock price dynamics, including those of blue-chip stocks. Statistical methodologies, such as Autoregressive Integrated Moving Average (ARIMA), are commonly utilized for stock price prediction. However, the efficacy of ARIMA is contingent upon the fulfillment of homoscedasticity assumptions. Failure to meet this assumption due to fluctuating stock price developments poses a challenge. Consequently, an ARIMA-GARCH hybrid model has been developed to address heteroskedasticity concerns in stock price forecasting. This study focuses on INDF stock data, exemplifying a blue-chip stock with positive performance. Results indicate that combining ARIMA-GARCH models, particularly the ARIMA(0,1,3)-GARCH(1,3) model, yields optimal predictions for subsequent stock prices. The MAPE value of the ARIMA-GARCH model stands at 1.41%, indicating superior performance compared to standalone ARIMA modeling. These findings are expected to serve as a valuable resource for investors navigating investment decisions.

Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0)

A frequently employed time series model is the Autoregressive Integrated Moving Average (ARIMA) model. In highly volatile data, ARIMA models sometimes produce residual variances that are heteroscedasticity. One method that can overcome the problem of residual variance heteroscedasticity is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. The purpose of this study is to obtain the ARIMA-GARCH model for daily gold price data in Indonesia for the period 1 January 2022 to 31 December 2022, and to obtain daily gold price forecasting results in Indonesia. The daily gold price forecasting model obtained for Indonesia is ARIMA (0,1,1) - GARCH (1,0) with a MAPE value of 0.5745% which shows that the model is very good because the MAPE value is less than 10%. The results of Indonesia's daily gold price forecast from January 1st, 2023 to January 3rd, 2023 remain stable.

Heterogeneity In Stock Price Forecasting-Based on the ARIMA-GARCH Model And PCA-LSTM Model

Heterogeneity In Stock Price Forecasting-Based on the ARIMA-GARCH Model And PCA-LSTM Model

Stock Closing Price Prediction Based on the ARIMA-GARCH Model

Amidst a burgeoning stock market, a plethora of predictive models for stock prices have been steadily surfacing, with a wide array of Autoregressive models finding extensive application. This study sets out to evaluate the efficacy of the ARIMA-GARCH model in the domain of stock price prediction, taking as its dataset the stock information of Jinan Hi-Tech Development (600807). This paper begins by transforming the closing prices of Jinan Hi-Tech Development (600807) from July 13, 2023, to July 27, 2023, into a time series for the purpose of model fitting, identifying the ARIMA model parameters, and examining ARCH effects alongside the normality and independence of residuals. Subsequently, GARCH model parameters are discerned and integrated with the ARIMA model to establish the ARIMA-GARCH model, which is then subjected to residual testing. Concluding the study, a comparative error analysis between the predicted and actual closing prices reveals that the ARIMA-GARCH model boasts substantial accuracy in short-term stock price forecasting.

Differential impacts of COVID-19 on Indian sectors: Resilience of pharmaceuticals and automobiles, contrasting with negative effects on IT and real estate - An ARIMA-GARCH analysis

The COVID-19 pandemic had a profound impact on financial markets worldwide, including those in India, and it has resulted in increased volatility with spillover effects across various industries. This study provides insights into the differential impact of COVID-19 on various industries in India by employing the ARIMA-GARCH model. The study utilized daily data spanning from 2017 to 2023 to examine industry responses during the COVID-19 pandemic, delineating three distinct periods: 2017 to 2021, denoting the pre pandemic era; 2020 to 2023, encompassing the turbulent during-COVID phase; and 2021 to 2023, representing the post-COVID era. In this study, the IT and real estate sectors are considered dependent variables, while the automotive and pharmaceutical sectors are regarded as independent variables. The primary aim is to uncover the complex influence of the IT industry on the automotive, pharmaceutical, and real estate sectors during these time frames; and to elucidate how the real estate industry reciprocally affects these sectors. Our empirical analysis provides insights into the asymmetric transmission mechanism of spillovers. By disentangling overall shocks into macroeconomic impacts affecting all sectors and industrial influences exclusive to specific sectors, we discern that macroeconomic shocks, rather than industrial shocks, primarily dictate the asymmetry in spillover transmission dynamics. This study contributes to a comprehensive understanding of how industries responded to the unprecedented challenges posed by the pandemic, offering valuable insights for strategic decision-making and policy formulation in the post-COVID-19 era.

A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA-GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy.

Os choques e integrações da pandemia da COVID-19 nos mercados agrícolas chineses: evidências empíricas comparativas da China e de outros países

ABSTRACT: We use the ARIMA-GARCH model to estimate the shocks of COVID-19 on Chinese agricultural markets and utilize the ARDL-ECM model to estimate the integration relationships between Chinese and international agricultural markets which are based on the effective market hypothesis theory and daily trading data of US, UK, China and India. The main study findings showed that COVID-19 has a significant negative impact on agricultural product market, specifically, India suffered with the greatest negative impact, followed by the UK, the US and China. Further, this study results revealed that Chinese grain markets that are considered strategic places have not been separated from international markets, but there still exist segmentation phenomena among non-strategic agricultural markets.

The Volatility and Risk-Return Trade-Off of Malaysian Islamic and Conventional Indexes During the Global Financial Crisis and COVID-19 Pandemic

Manuscript type: Research paper Research aims: This study aims to investigate the volatility characteristics and risk-return trade-off of Islamic and conventional indexes in the Malaysian market. Design/Methodology/Approach: The research covers the daily data of the period from August 2007 to December 2022, divided into four distinct periods: the full sample, the period during and after the 2007- 08 financial crisis, and the period during the COVID-19 pandemic. The research employs a hybrid model that combines ARIMA with GARCH family models. Research findings: In this research, both Islamic and conventional indexes in the Malaysian market demonstrate a memory effect, emphasising the persistence of market volatility through the influence of past volatility. Additionally, historical data, represented by lagged values, significantly shape volatility, while negative shocks have an immediate and pronounced impact compared to positive shocks, providing valuable insights for investors and risk management. Lastly, during the COVID-19 crisis, the conventional index showed no leverage effect, and the Islamic index lacked safe haven characteristics, making this crisis unique in the Malaysian financial context. Theoretical contribution/Originality: This research contributes to the understanding of market volatility dynamics in the Malaysian context by utilising a hybrid ARIMA-GARCH model. The identification of memory effects, asymmetric responses, and the unique characteristics observed during the COVID-19 crisis adds to the body of knowledge on financial market behaviour. Practitioner/Policy implication: The findings of this study have practical implications for investors and supervisory entities operating in the Malaysian market. Understanding the persistence of volatility and the differential impact of positive and negative shocks can help investors make more informed decisions, while regulators can use this information to quantify and manage market volatility effectively. Research limitation/Implications: The limitation of the present study is that the results may be influenced by the selection of the sample period, potentially yielding different outcomes depending on market conditions, such as the presence of bull or bear markets, periods of high or low volatility, or other contextual factors.

High-frequency data significantly enhances the prediction ability of point and interval estimation

Accurate prediction of dissolved oxygen (DO) dynamics is crucial for understanding the influence of environmental factors on the stability of aquatic ecosystem. However, limited research has been conducted to determine the optimal frequency of water quality monitoring that ensures continuous assessment of water health while minimizing costs. To address these challenges, the present study developed a hybrid stochastic hydrological model (i.e., ARIMA-GARCH hybrid model) and machine learning (ML) models. The objective of this study is to identify the best-performing model and establish the optimal monitoring frequency. Results revealed that high-frequency DO monitoring data exhibit greater variability compared to low-frequency data. Moreover, the ARIMA-GARCH model demonstrates promising potential in predicting DO concentrations for low-frequency monitoring data, surpassing ML models in performance. Furthermore, increasing the monitoring frequency significantly improves the prediction accuracy of models, regardless of whether point (with lower R2 values of 0.64 and 0.51 for daily detection than these of every 15 min (0.96 and 0.99) at CHQ and LHT, respectively) or interval predictions (with RIW higher values of 2.00 and 1.55 for daily detection higher than these of 0.02 and 0.16 in every 15 min at CHQ and LHT, respectively) are considered. Additionally, a 4 hourly monitoring frequency was found to be optimal for water quality assessment using each model. These findings identify the superior performing of the ARIMA-GARCH model and highlight the crucial role of monitoring frequency in enhancing DO prediction and improving model performance.

Optimal trading strategies based on time series analysis

Quantitative investment has been widely used in the field of foreign finance, especially the rapid development of international investment in the past decade. And financial activity is an important field of national economic activity. The frequency of financial transactions is an important indicator of the complexity of a country's economy, so it is of great significance to study the optimal investment strategy. This article uses daily price streams from past investments in gold, cash, and bitcoin to determine whether traders should buy, hold, or sell assets in their portfolios. The outlier data were processed by boxplot analysis, and the EM algorithm based on maximum likelihood estimation was used to visualize the case data. The ARIMA model and GARCH model are used to establish the portfolio optimization model and obtain the best portfolio scheme. The time series prediction model is used to conduct specific quantitative analysis on gold and Bitcoin and obtain the investment forecast of the initial $1000 in the future.

Forecasting Analysis of Stock Prices on European Markets Using the ARIMA-GARCH Model

The achievement of profits when trading on the stock markets is conditioned by a quality analytical forecast of the development of stock prices in the coming period. This research attempts to compare the results of the ARIMA model and the ARIMA-GARCH model to forecast the development of stock prices on a sample of selected stocks from the Czech, German, Austrian, Polish and British markets. The 4 most liquid titles from each of the above-mentioned markets were selected for the sample of analyzed stocks. Available daily closing stock price data, mostly from the period 2000–2022, were used for the analysis. Research has shown that for most of the analyzed titles, it is more appropriate to use the ARIMA-GARCH model, which better captures variability for this data than just the ARIMA model. The quality of the selected model is evaluated by autocorrelation, heteroskedasticity tests, and Theil´s inequality coefficient.

Research on Forecasting the Shanghai and Shenzhen 300 Index Based on the ARIMA-GARCH Model

This paper aims to forecast the closing prices of the Shanghai and Shenzhen 300 Index using time series models. By combining the ARIMA and GARCH models, the study aims to improve the accuracy of short-term predictions for the index and enhance risk management capabilities. The research begins by collecting and preprocessing historical data of the Shanghai and Shenzhen 300 Index. Then, an ARIMA-GARCH model is established, and the model parameters are estimated and tested. Finally, the model is used to forecast the closing prices of the index, and the accuracy of the predictions is evaluated. The results demonstrate that the constructed ARIMA-GARCH model can provide accurate predictions for the closing prices of the Shanghai and Shenzhen 300 Index. This research is expected to enhance prediction accuracy, improve risk management, increase practical value, and serve as a reference for similar studies.

ARIMA-GARCH Model Price Forecasting in PT. Unilever Indonesia Tbk

The purpose of this research is to predict the closing stock price of PT. Unilever Indonesia Tbk using the ARIMA-GARCH method. The data used in this study covers the period from February 20, 2020, to February 17, 2023, consisting of 734 daily observations. The data processing is performed using E-Views software. The closing stock price data of PT. Unilever Indonesia Tbk is non-stationary, thus requiring natural logarithm transformation and differencing. This is followed by model identification, parameter estimation, and diagnostic checking. The best-selected ARIMA model is ARIMA ([3,9],1,0), which accounts for the presence of heteroscedasticity. Subsequently, the GARCH method is applied, including model identification, parameter estimation, and diagnostic checking. The best GARCH model is GARCH (1,1), with the mean equation = 0,000047 + 0,230821 + 0,681968σ , which is free from heteroscedasticity effect. The forecast using the ARIMA ([3,9],1,0) GARCH (1,1) model yields a Mean Absolute Percentage Error (MAPE) of 2.423%, indicating a close approximation to the actual data. From the results of this research, the best model for forecasting PT. Unilever Indonesia Tbk for the next period was obtained. Therefore, the findings can assist PT. Unilever Indonesia Tbk and prospective investors in making decisions regarding the sale and purchase of shares in PT. Unilever Indonesia Tbk.