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The research paper delves into an in-depth analysis of the S&P BSE CARBONEX index, which serves as a benchmark for top companies, evaluating and benchmarking their climate change mitigation practices. The study focuses on volatility analysis of BSE CARBONEX, employing various GARCH models such as GARCH, TARCH, EGARCH, IGARCH, PARCH, and APARCH across five distributions. Through meticulous evaluation based on criteria including AIC, SC, and Log Likelihood values, the APARCH (1,1) model is selected, revealing the presence of asymmetry and volatility clustering within the index. This quantitative, applied, exploratory, and explanatory study conducts volatility analysis of S&P BSE CARBONEX over a period of 2485 daily observations from February 28, 2014, to March 15, 2024. To ensure data stationarity, log returns are calculated based on the previous day's price, and the nature of the data is explored using statistical measures such as mean, median, mode, skewness, kurtosis, and standard deviation. Furthermore, the study conducts heteroscedasticity tests through the ARCH LM test, uncovering signs of a leverage effect, albeit with statistically non-significant coefficients. The analysis also scrutinizes periods of dip and volatility, concluding that the year 2020 saw heightened volatility likely attributed to events such as the COVID-19 pandemic and associated variables, followed by a swift recovery. Additionally, the study observes small memory-based volatilities within the index. Through its comprehensive analysis, this research offers valuable insights into the dynamics of volatility within the BSE S&P CARBONEX index, contributing to a deeper understanding of market behaviour and potential implications for investors and policymakers alike. [ABSTRACT FROM AUTHOR]

The research paper aims to examine the BSE GREENEX index, which was developed to monitor and assess the performance of environmentally conscious companies based on carbon emissions. This index, created in collaboration with IIM Ahmedabad, is a valuable tool for companies aiming to enhance their carbon performance. It facilitates the creation of eco-friendly financial products like mutual funds, exchange-traded funds (ETFs), and structured funds. The study spans 2014 to 2024, utilizing quantitative data and various econometric tools to analyze price movements. With a dataset comprising 2485 observations of S&P BSE-GREENEX spanning from February 28, 2014, to March 15, 2024, the research explores the application of different GARCH models, including GARCH, EGARCH, IGARCH, TARCH, APARCH, and PARCH, across various distributions. By evaluating criteria such as AIC, SC, and Log Likelihood values, the APARCH (1,1) model with a t-distribution emerges as the optimal choice. The study's findings reveal the presence of volatility clustering, leverage effects, and log memory volatility within the BSE S&P GREENEX index. Furthermore, the analysis suggests that macroeconomic fluctuations have led to intermittent declines in volatility. Employing econometric and statistical methodologies, the paper endeavors to comprehensively elucidate these phenomena and their implications. [ABSTRACT FROM AUTHOR]

Currency Derivatives are very important financial instruments for speculation, hedging and arbitrage opportunities, and among them cross-country futures are one of the important types with a huge research gap. Studying them becomes very imperative. This paper studies the volatility of INR based cross country futures (USD, JPY and EUR) and performs forecasting using ML Algorithm and utilizes LSTM for prediction. The study proves to be a first of its kind study involving cross-country futures and is a beacon of hope for all future research on similar subjects. The study will also be helpful to investors and foreign exchange managers along with monetary and fiscal policymakers. The study consists of total of 674 data points of near-month expiry futures expiring on 29th October, 2024. The span of d ata w as 1 year f or JPY a nd E UR a nd n early 1 1 m onths for USD. T he d ata w ere downloaded from NSE website. The USD-INR futures were nearly stable and EUR-INR futures were most volatile. The JPY-INR futures had highest rise in price trends. Prediction of USD/INR future outperformed other two with least error. However, LSTM model that was trained, relatively underperformed in case of JPY-INR. [ABSTRACT FROM AUTHOR]

The goal of this article is to look into the movements of two economic powers, the United States America and China, as well as the volatility spillover between the United States of America and China. Furthermore, the objective of this research is to compare and contrast the accuracy of forecasts made using univariate volatility models with those made using the Normal, Student-t, and Generalized Error models. Student t's Distribution with df and GED with Fixed Parameter Distribution, constructs in predicting stock volatility between the United States of America and China, based on 560 daily observations from the United States stock market (S&P 500 Index) and China's stock market (Shanghai Stock Exchange) (SSE Composite index and HANG SENG index).Advanced econometric models are used to achieve the research-based objectives i.e. The EGARCH (exponential generalized autoregressive conditional heteroscedastic) models (Chang, McAleer, & Tansuchat, 2012) have been applied to the data from January 27, 2000, to March 24, 2023. Asymmetry in volatility transmitting patterns, the movement of shocks with higher positive and negative magnitudes, and the model's suitability are all examined in the study. The potential benefits and dangers of investing are another focus of this empirical investigation. The fitness of financial series returns in the EGARCH model is one outcome. Other outcomes include the movement of financial series, volatility sketching, a description of the data and properties of the EGARCH model, and the fitness of series returns. The results show that, in the long run, the market in question is integrated. The results also demonstrate that the Chinese market mirrors the behaviour of the USA market and that investors have limited avenues for diversification at present. The results of the EGARCH model indicate that there were asymmetric volatility spillover effects between the USA and Chinese markets during the studied time periods. Based on a reliable technique that enables the examination of the dependence structure between the sample variables, this study also investigates co-movement in China. For investors looking for global investment diversification options, the findings offer helpful information. [ABSTRACT FROM AUTHOR]

In order to evaluate and estimate significant aspects of time series data, various models with varying degrees of variation have been built in the last few years. The overarching goal of study is to examine the variance dynamics of several Indian stock market indexes using Normal and Students-t distributions. The sample data spans a long time period (more than 40 years), from April 1979 to May 2023, and includes dramatic happenings. The econometric method relies heavily on the GARCH models and other statistical investigations. The outcomes contribute to the growing body of knowledge in the field. The possible rewards and risks of an investment are the focus of this empirical study. The results encompass the dynamics of financial series, volatility sketching, a statistical and GARCH model feature assessment, and the fitness of series returns. This opens the door to different assessments of the Indian financial market. [ABSTRACT FROM AUTHOR]

This study examines the long-term effects of CO2 emissions, farm mechanization, and other control variables on agricultural production in India from 1960 to 2022. These variables include arable land, fertilizer consumption, GDP, import and export of agricultural raw materials, cereal yield (kg per hectare), and urbanization. To identify the longterm co-integrating relationship between the variables, this study used multiple types of econometric techniques, including the ARDL bound-testing methodology and Johansen co-integration procedure. A meaningful long-term cointegrating relationship between the variables is supported by the empirical findings of the ARDL bound-testing method. The long-term outcomes showed that agricultural productivity is significantly increased by mechanization, exports and imports of agricultural raw materials, and arable land. Results also showed that grain production and fertilizer use had a considerable beneficial impact on agricultural performance; however, CO2 emissions and urbanization have a significant negative impact. Therefore, these findings suggest that India's policymakers should implement more efficient measures to boost investments in agricultural R&D and farm mechanization. By assuring the nation's food security, this will increase agricultural output. To support India's efforts to achieve a sustainable environment and development, the report advises integrating climate change measures into the nation's national strategies, policies, and planning. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study was conducted on BUX Index volatility for the post-2008 (from 2011) global financial crisis period using advanced GARCH models (GARCH, TGARCH, EGARCH, IGARCH, PARCH, APARCH). Based on parameters and test results appropriate model was chosen (APARCH (1,1) at Student's t distribution) to study volatility, the presence of asymmetry, leverage effect, volatility clustering, and decay factor. Test results were linked with the macro and microenvironment (Political instability, Geopolitical crisis, Unemployment, Inflation, COVID-19, Slowdown, etc.) of the index and unique features of the index have been discovered (Like a sudden huge dip between 2020-2022). The study through mathematical and econometrics terms establishes causal links among the variables affecting the index. The paper is relevant to both investors and policymakers as BUX is one of the most important indicators as far as stock market in Hungary is concerned. [ABSTRACT FROM AUTHOR]

The study assesses the impact of financial leverage, interest coverage ratios, and debt-to-equity ratios on the earnings per share (EPS) of Indian banks during the COVID-19 pandemic. It analyzed 23 Indian banks in the NIFTY 100 index, including 10 private and 13 public sector banks. The results showed that financial leverage, interest coverage ratios, and debt-to-equity ratios significantly impacted EPS. The interest coverage ratio had a favorable effect, while financial leverage and debt-to-equity ratios had negative effects. [ABSTRACT FROM AUTHOR]

In the stock market, volatility is a term used to describe the degree to which the prices of assets oscillate and determines the level of risk or uncertainty. The foremost objective of the present analysis is to model the behaviour of the Denmark stock market using data from December 20, 2016, to September 20, 2023. Through the application of GARCH family prototypes which, include GARCH/TARCH, EGARCH, Component ARCH (1,1), and PARCH. The analysis used a sample number of 1668 daily observations for OMXC 25 or OMX Copenhagen 25 Stock Index representing the Denmark stock market. We used some statistical techniques such as Phillips-Perron and Augmented Dickey Fuller tests, Kwiatkowski-Phillips-Schmidt-Shin test statistic, The ARCH Lagrange Multiplier (LM) test, PARCH model. We utilized the E-Views 12 Econometrics package. This empirical investigation adds to the corpus of financial econometrics and emphasizes the importance of precisely and painstakingly modelling the behaviour of stock markets. Our ability to forecast market movements and make informed decisions in a turbulent financial climate will be improved by the findings and research methodologies covered in this paper, which will serve as a solid foundation for future investigations. [ABSTRACT FROM AUTHOR]

The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The act of modeling and forecasting stock market volatility has become essential to risk management practice; it has become one of the most prevalent subjects in financial econometrics and has been mainly and continuously used in the valuation of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The purpose of this paper is to contrast the FIGARCH model in the prospect of establishing the best algorithm to forecast PSI 20 index (PSI stands for Portuguese Stock Index). This study analyzed PSI 20 index to identify the behavior of the Portugal stock market in terms of volatility and then evaluated the forecasting ability of GARCH family models using Portugal PSI 20 index sample data for the long period from May 2010 - August 2023 (which is more than 13 years in daily data). There are 3396 daily observations. The results indicated that the PSI 20 index's volatility is asymmetric. The present empirical investigation also seeks to illustrate the potential for investment returns as well as the associated risk. Our findings may have implications for risk management in Portugal, as well as a deeper understanding of the PSI-20 Portugal stock market volatility dynamics, given the scarcity of prior such studies. [ABSTRACT FROM AUTHOR]

KHADI AND VILLAGE INDUSTRIES IS A MAJOR WING IN MSMES. THE PRESENT STUDY IS ANALYTICAL IN NATURE AND IS ENTIRELY BASED ON SECONDARY DATA. SECONDARY DATA RELATED TO THE PRODUCTION, SALES AND EMPLOYMENT OF KHADI& VILLAGE INDUSTRIES OF INDIA ARE COLLECTED FROM THE VARIOUS ANNUAL REPORTS OF MSMES GENERATED BY THE MINISTRY OF MSME, GOVERNMENT OF INDIA AND ANNUAL REPORTS OF KHADI AND VILLAGE INDUSTRIES COMMISSION (KVIC). THE ANNUAL REPORTS OF THE MSME SECTOR FROM 2010-11 TO 2020-21 HAS BEEN ANALYZED AND EVALUATED TO DRAW THE CONCLUSION. THE PURPOSE OF THIS STUDY IS TO ANALYSE THE GROWTH AND DEVELOPMENT OF KHADI AND VILLAGE INDUSTRIAL SECTOR IN TERMS OF PRODUCTION, SALES AND EMPLOYMENT GENERATION. AS WELL AS IT STUDIES THE BUDGET SUPPORT TO THE KVIC SECTOR BY THE GOVERNMENT. DATA INDICATE THAT THE KVI SECTOR MADE A MAJOR CONTRIBUTION TO EMPLOYMENT. HOWEVER, THIS SECTOR IS PERFORMING WELL, WITH INCREASING PRODUCTION AND SALES. COMPARED TO OTHER INDUSTRIES, THE KVI GROWTH IS SLOWER THAN OTHER SECTOR INDUSTRIES. IN THE KHADI SECTOR, EMPLOYMENT IS LOWER THAN AVERAGE. IT IS REPORTED THAT ALMOST 95% OF THE YEARLY BUDGET'S MONEY HAVE BEEN DISBURSED. FOR CALCULATION OF RESULTS OF CORRELATION HAS BEEN USED AND CONCLUDED THAT PRODUCTION AND SALES KHADI SECTOR AND VILLAGE INDUSTRY IS POSITIVELY HIGHLY CORRELATED. TO IMPROVE THE RURAL ECONOMY AND CREATE JOBS IN RURAL REGIONS, THE GOVERNMENT HAS TAKEN A NUMBER OF STEPS IN THE DIRECTION OF THE KVI SECTOR. DESPITE THE FIERCE COMPETITION, THE ENTIRE PERFORMANCE OF KHADI AND VILLAGE INDUSTRIAL SECTOR HAS BEEN OUTSTANDING. SALES AND PRODUCTION ARE ENCOURAGED BY THE KHADI AND VILLAGE INDUSTRIAL COMMISSION THROUGH THE IMPLEMENTATION OF SEVERAL PLANS FOR THE KVI SECTOR. [ABSTRACT FROM AUTHOR]

IN THE STOCK MARKET, VOLATILITY IS A TERM USED TO DESCRIBE THE DEGREE TO WHICH THE PRICES OF ASSETS FLUCTUATE AND DETERMINES THE DEGREE OF RISK OR UNCERTAINTY. THE MAIN AIM OF THE PRESENT STUDY IS TO MODELING THE BEHAVIOR OF THE SWITZERLAND STOCK MARKET USING DATA FROM 4TH JANUARY, 2000 TO 9TH NOVEMBER, 2023. THROUGH THE APPLICATION OF GARCH FAMILY MODELS WHICH, INCLUDE GARCH/TARCH, EGARCH, COMPONENT ARCH (1,1), AND PARCH. THE STUDY USED A SAMPLE NUMBER OF 5994 DAILY OBSERVATIONS FOR SWISS STOCK INDEX REPRESENTING THE SWITZERLAND STOCK MARKET. WE USED SOME STATISTICAL TECHNIQUES SUCH AS PHILLIPS-PERRON AND AUGMENTED DICKEY FULLER TESTS STATISTIC. THE ARCH LAGRANGE MULTIPLIER (LM) TEST, PARCH MODEL. WE UTILIZED THE EVIEWS 12 ECONOMETRICS PACKAGE. THIS STUDY HIGHLIGHTS THE SIGNIFICANCE OF ACCURATELY AND METICULOUSLY SIMULATING STOCK MARKET BEHAVIOR IN ADDITION TO ADDING TO THE CORPUS OF KNOWLEDGE IN FINANCIAL ECONOMETRICS. THE CONCLUSIONS AND METHODS DISCUSSED IN THIS STUDY PROVIDE A STRONG BASIS FOR FURTHER RESEARCH, ENHANCING OUR CAPACITY TO PREDICT MARKET MOVEMENTS AND MAKE WISE CHOICES IN A VOLATILE FINANCIAL ENVIRONMENT. [ABSTRACT FROM AUTHOR]

MSMEs in India play a substantial role in the growth of country's economy, contributing 30% to its GDP. The Micro, Small & Medium Enterprises or MSMEs in India employees over 11.10 crore people and facilitates in employment generation. These enterprises are involved in the production of over 6000 different items, showcasing their diverse range of economic activities. However, despite their significant contributions, Indian MSMEs face a major obstacle in the form of lack of finance. Along with the financial problem, due to many other challenges, micro-enterprises are struggling for their growth and development. To overcome the problem of financial support, the Government of India launched the MUDRA (Micro Units Development and Refinance Agency) Scheme to provide financial assistance and support to MSMEs on April 8, 2015. The seven sister states are the neighboring states of Arunachal Pradesh, Assam, Meghalaya, Manipur, Mizoram, Nagaland and Tripurain north eastern India. However, the region has a blessed strategic location and abundant resources with specialties of production from each state. Indeed, the people of the region can take advantage to a greater extent by utilizing the available resources and promoting the MSME. Thus, This research paper aims to critically evaluate the performance of the MUDRA scheme in India since its inception. Additionally, the paper aims to evaluate the state-wise number of accounts and disbursement amount under Shishu, Kishore and Tarun under Mudra Scheme across all 7 Northern East states of India. The study covers the period from 2015-16 to 2021-22. The analysis is based on secondary data, collected from the annual report of 2022-2023 generated by the Ministry of MSME, GOI. The application of ANOVA demonstrated a significant difference in the average number of accounts under Shishu, Kishore and Tarun under the Mudra Scheme across all 7 Northern East states of India from 2015-2016 to 2021-2022. Conversely, the analysis did not indicate a significant difference in the Average disbursement amount under Shishu, Kishore and Tarun under MUDRA Scheme across all 7 Northern East states of India during 2015- 2016 to 2021-2022. [ABSTRACT FROM AUTHOR]

Stocks play a crucial role in the stock market, which is at the very core of every nation's economic growth. Investors, analysts, and others in related fields have turned their attention to stock price analysis. This study empirically investigates the conditional variance (volatility) or empirically estimates the price volatility spillover transmission in the daily returns of IPC Mexico index from Mexico stock market, for the long period January 1993 - July 2023 (which is more than 30 years daily data) using the GJR- GARCH model. There are 7661 daily observations included in the study. The recurrence of outcomes had leptokurtosis, were skewed to the left, and were not normally scattered; there was also confirmation of ARCH effects. The insights of the symmetric GARCH model showed confirmation of volatility clustering and endurance; the results of the GARCH-GJR model demonstrated the leverage impact and volatility clustering in the sample index. Favorable events alter the conditional variance (volatility), resulting in significant asymmetric GJR-GARCH values. This leads to the conclusion that favorable information has greater effects on index return volatility than adverse information. The results of this research constitute vital information for financiers, risk analysts, and regulators. [ABSTRACT FROM AUTHOR]

GARCH (Generalize Autoregressive Conditional Heteroscedasticity) family models, notably the GARCH (1,1), GJR-GARCH,EGARCH, M GARCH, and TGARCH models,(Chang, McAleer, & Tansuchat, 2012) are used in this study to empirically test volatility clusters of the IBOVESPA index using data from 20 May 1993 to 17 March 2023. A further perspective on asymmetric volatility clustering and leptokurtosis patterns is offered by the empirical analysis, which is based on daily observations from the Brazil stock market (7380). The study examines the existence of asymmetries in the patterns that transmit volatility, the movement of shocks with higher positive and negative magnitudes, and the suitability of the model. In addition, the purpose of this study is to evaluate the accuracy of volatility forecasts using both univariate and multivariate models that of Normal, Student-t, and Generalized Error Distribution constructs in predicting stock volatility over a period of more than 30 years, from May 1993 to March 2023 in the Brazilian stock markets. The results supported the existence of the leverage effect during the sampling period. Furthermore, the empirical results revealed that the sample returns of the chosen stock market had a significant level of volatility. By adding new factual data on the long-term performance of the Brazilian stock market, particularly in light of exaggerated events, this study adds to the body of literature already in existence. By adding new empirical data on the long-term behavior of the Brazilian stock market, particularly in light of extraordinary occurrences, this study adds to the body of literature already in existence. [ABSTRACT FROM AUTHOR]

The purpose of the empirical research study is to analyze the volatility of OMX Tallinn Index in Estonia from 2002 to 2022 in three major period phases i.e. 2002-2008, 2009-2015 and 2016 to 2022. Moreover, it attempted to formulate PARCH Model for each phases of OMX Tallinn Index in Estonia from 2002 to 2022 that could grasp not only the volatility but also asymmetric volatility caused by various important events for each particular period. The total sample size is 6,032 i.e. 3 phases of 1826 observations each. The selected period covers a series of extreme events such as the global financial crisis, the COVID-19 pandemic, the war between Russia and Ukraine that began in 2021 and others. The empirical results are relevant and contribute to the existing literature. [ABSTRACT FROM AUTHOR]

The act of modeling and forecasting stock market volatility has become essential to risk management practice; it has emerged as one of the most popular themes in financial econometrics and has been mainly and constantly used in the valuation of financial assets and the Value at Risk, as well as the pricing of options and derivatives, are all part of the process. The purpose of this paper is to contrast the GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) family models -- GARCH, PGARCH, EGARCH, GJR-GARCH, MGARCH, and IGARCH -- with the EWMA (Exponentially Weighted Moving Average) models in the prospect of establishing the best algorithm to forecast TSEC volatility weighted index from Taiwan stock market. We employ daily returns for the range between July 1997 and July 2023. The sample data spans a long time period i.e. 26 years of data with 6375 observations, which includes dramatic events such as the financial crisis of 2008, the COVID-19 epidemic, and the war between Russia and Ukraine. We observe that the asymmetric model PGARCH with a Student's t distribution develops the best model for assessing the volatility of the chosen index, but PGARCH with a normal error distribution produces the best estimating performance outcomes and hence outperforms the EWMA model. The present empirical investigation also seeks to illustrate the potential for investment returns as well as the associated risk. Our findings may have implications for risk management in Taiwan, together with a deeper exploration of the TSEC weighted index from Taiwan stock market volatility dynamics, given the scarcity of prior such studies. [ABSTRACT FROM AUTHOR]

THE PURPOSE OF THIS STUDY IS TO ASSESS THE EFFICACY OF THE DIGITALIZATION IN HIGHER EDUCATION OF BIHAR DURING THE LOCKDOWNS OF COLLEGES AND UNIVERSITIES DUE TO COVID-19 PANDEMIC AND TO FIND WHETHER GENDER, COURSE OPTED, STREAM OF STUDY, MEDIUM OF STUDY, RESIDENTIAL AREA, FAMILY INCOME LEVEL, ONLINE CLASS ATTENDED, ACTUAL TIME SPENT ON USING INTERNET FOR EDUCATION, AND OWNERSHIP OF GADGETS USED IN ONLINE CLASSES AFFECT THE EFFECTIVENESS OF DIGITALIZATION IN HIGHER EDUCATION IN BIHAR. THE DATA USED IN THIS STUDY ARE PRIMARY IN NATURE AND COLLECTED BY USING THE QUESTIONNAIRE METHOD FROM 429 STUDENTS OF THIRTEEN DIFFERENT STATE UNIVERSITIES AND ONE OPEN UNIVERSITY. AS MANY PREDICTORS ARE CATEGORICAL IN NATURE MULTINOMIAL LOGISTIC REGRESSION HAS BEEN USED TO ASSESS THE IMPACT OF VARIOUS DEMOGRAPHIC AND SOCIO-ECONOMIC FACTORS ON EFFICACY OF DIGITALIZATION IN HIGHER EDUCATION IN BIHAR. IT HAS BEEN FOUND THAT THE VARIABLES LIKE MONTHLY INCOME OF THE FAMILY, TIME ACTUALLY SPENT BY THE STUDENTS FOR EDUCATION BY USING INTERNET PER DAY, STREAM OF THE STUDY, MEDIUM OF THE STUDY, RESIDENTIAL AREA AND GENDER ARE SIGNIFICANT FACTORS THAT AFFECT THE DIGITALIZATION IN HIGHER EDUCATION IN BIHAR DURING THE PANDEMIC. THE STUDY COULD CONTRIBUTE IN TWO WAYS. FIRST, THE RESULTS OF EFFICACY OF DIGITALIZATION IN HIGHER EDUCATION IN BIHAR OF A LOWEST EDUCATION INDEX RANKING STATE WILL DEPICT AN AUTHENTIC PICTURE OF EFFECTIVENESS OF DIGITALIZATION IN HIGHER EDUCATION IN BIHAR IN BACKWARD STATES AND THEREBY ADD UP TO THE EXISTING LITERATURE AND SECOND, THE SIGNIFICANT FACTORS OF THE MODEL COULD ASSIST THE POLICY MAKERS IN TAKING CORRECTIVE MEASURES WHILE TAKING ANY DECISION TO DIGITALIZE THE EDUCATION IN SUCH KIND OF STATES IN INDIA. [ABSTRACT FROM AUTHOR]