This study aims to examine the influence of the Reserve Bank of India’s (RBI) monetary policy on financial market dynamics, with particular emphasis on the relationship between policy rate adjustments, stock market volatility, and mutual fund performance. It examines the impact of repo changes on share prices, fund flows in equity, debt and hybrid categories, along with investor behaviour during tightening and easing phases of monetary policy. The research is based on a quantitative paradigm using secondary data from the financial market indicators such as repo rate changes, Nifty index values and mutual funds inflows. The amount and the direction of the relationship between market performance indicators and monetary policy variables were measured by correlation and regression analysis through SPSS. The response of mutual fund categories and share prices to the central bank policy changes was estimated using these regression models. Results are indicative of the full effect of the change in repo rate on debt fund inflows being positive, suggesting that investors prefer FISs under monetary tightening conditions. On the other hand, inflows into equity and hybrid funds have a negative association with increasing rates, but only for the hybrid fund is there found to be a significant response. The results also indicate that variations in policy rates account for the modest volatility observed in stock market indicators, and this indicates the spread of monetary policy across investor emotion and liquidity adjustment

Stock market volatility and white-collar criminal behavior: an empirical study of corporate fraud during the financial crisis

The impact of investor sentiment fluctuations on stock market liquidity and volatility: Implications for market returns

Housing Market Variables and Predictability of State-Level Stock Market Volatility of the United States: Fundamentals versus Sentiments in a Mixed-Frequency Framework

Green and innovative assets in times of uncertainty: A portfolio perspective for environmental financial management.

This study examines the role of inflation, monetary policy interest rates, and foreign portfolio investment (FPI) in driving stock market volatility in ASEAN countries through a systematic literature review (SLR). The review synthesizes empirical evidence from studies conducted between 2000 and 2025, providing insights into how these macroeconomic variables influence stock market behavior in the region. The findings reveal that inflation, particularly when high or volatile, negatively impacts stock market stability by eroding purchasing power and increasing uncertainty. Monetary policy interest rates, especially during periods of tightening, are inversely related to stock market returns, exacerbating volatility. Foreign portfolio investment, while providing liquidity, also introduces risk, with capital inflows and outflows linked to shifts in global economic conditions. The interactions between these variables are complex, often creating feedback loops that amplify stock market fluctuations. The study underscores the need for ASEAN policymakers to balance inflation control and interest rate adjustments to stabilize financial markets, while investors should be mindful of the macroeconomic environment when making decisions. The paper contributes to the broader literature on financial market behavior in emerging economies and suggests avenues for future research to better understand the intricate relationships between these macroeconomic variables.

Forecasting volatility in emerging markets is challenging due to their unique characteristics, including data quality issues, structural instability, and complex nonlinear relationships. The GARCH-MIDAS model has emerged as a promising alternative, combining the strengths of GARCH to incorporate mixed-frequency data, critical for modeling financial phenomena. While this approach is used to study volatility, the study of oil prices and oil shocks as drivers of volatility in emerging markets is particularly appealing given these economies’ vulnerability to such shocks, their dependence on oil revenue, and global economic disruptions. Objective: to explore and analyze the literature on the use of GARCH-MIDAS in emerging markets. Methodology: a systematic literature review combined with a bibliometric analysis under the prisma framework to ensure clarity, transparency, and reproducibility. Key findings: Results suggest oil shocks have positive and significant effects on stock market volatility and are relevant for emerging markets. Conclusion: GARCH-MIDAS is a promising tool for forecasting volatility, and the study of oil crises is an important area for future research.

This paper systematically examines the differential time-varying impacts of Chinese and U.S. climate policy uncertainty (CPU) on the volatility of China's new energy stock market. Utilizing a Time-Varying Parameter Stochastic Volatility Vector Autoregression (TVP-SV-VAR) model, this study conducts a comparative analysis of the mechanisms through which CPU from both countries influences the volatility across different time horizons. The results indicate that the impacts of both Chinese and U.S. CPU on the volatility of China's new energy stocks are time-varying. Specifically, Chinese CPU suppresses stock price volatility in the short run, with its effect gradually diminishing to zero in the long run. In contrast, shocks from U.S. CPU persistently amplify market volatility without showing significant decay. Furthermore, the study finds that the influences of these shocks exhibited significant divergence during extreme events, such as China's accession to the Paris Agreement in 2016 and the COVID-19 pandemic in 2020. Based on these findings, this paper proposes corresponding policy recommendations pertaining to international policy coordination and the construction of market stabilization mechanisms.

Abstract The unfavorable global economic conditions caused primarily by the COVID-19 pandemic, as well as the ongoing geopolitical tensions and related disruptions, have adversely affected nearly all financial markets through the process of globalization. Considering these factors, this study analyzes, tests, quantifies, identifies, and compares the nature of correlation and the intensity of the impact of relevant macroeconomic variables on daily investment return rates in selected financial markets of developed countries (the United States and Germany) and emerging markets (Serbia and Croatia). The macroeconomic variables observed include inflation rate, reference interest rate, exchange rate, oil price, and gold price. Investment returns are analyzed through daily changes in the leading stock indices of the selected financial markets. The research covers the period from 2012 to 2022, examining results separately for three subperiods: the entire period (2012–2022), the pre-crisis period (2012–2019), and the crisis and post-crisis period (2020–2022). The results reveal that during the crisis period (2020–2022), stock market volatility in emerging markets (Serbia and Croatia) was primarily driven by exchange rate movements and commodity prices, while in developed markets (USA and Germany), it was more strongly influenced by interest rates and inflation. Furthermore, the estimated models indicate that the magnitude and direction of macroeconomic impacts vary substantially across markets and time periods. These findings provide valuable insight for investors and policymakers seeking to manage risk and respond to economic shocks in both mature and developing financial systems.

Current research investigate the association between investor overconfidence, risk perception, and stock market volatility, with a specific emphasis on the moderating effect of financial literacy. Based on data collected from a sample of retail investors who trade in the Pakistan Stock Exchange (PSX), this study uses structural equation modelling (SEM) techniques to examine the research hypotheses. The results show that investor overconfidence has a significant effect on risk perception, which then enhances stock market volatility. The modulation by financial literacy was, however, limited when considering the attenuation of confidence in risk perception and market volatility. These findings indicate the importance of psychology to determine investment behavior and market outcomes and shed some light on how behavioral biases may lead to volatile behaviors in emerging markets. In addition, the findings add empirical evidence on the behavioural finance literature from the perspective of a developing country, Pakistan. The results have policy implications for policymakers and financial intermediaries who wish to improve investor education and lower the destabilizing impact of irrational investment behavior on market stability.

This study examines the impact of social media on stock market volatility of Nepal. Stock market volatility is the dependent variables. The selected independent variables are social media activity, market fundamentals, investor sentiment, market liquidity and news coverage. The primary source of data is used to assess the opinions of respondents regarding social media activity, market fundamentals, investor sentiment, market liquidity and news coverage. The study is based on primary data. The primary data were gathered from 106 respondents through questionnaires. To achieve the purpose of the study, structured questionnaire is prepared. The correlation and multiple regression models are estimated to test the significance and importance of impact of social media on stock market of Nepal. The study showed a positive impact of social media activity on stock market volatility. It indicates that an increase in social media activity leads to an increase in stock market volatility. Similarly, the study showed a positive impact of market fundamentals on stock market volatility. It indicates that better the market fundamentals, higher would be the stock market volatility. Likewise, the study also revealed a positive impact of investor sentiment on stock market volatility. It indicates that higher investor sentiment leads to increase in stock market volatility. Further, the study observed a positive impact of market liquidity on stock market volatility. It indicates that higher market liquidity leads to increase in stock market volatility. In addition, the study observed a positive impact of news coverage on stock market volatility. It indicates that better coverage leads to increase in stock market volatility.

Last one century we have seen volatility has supremacy in the stock market because volatility creates greed and fear in our mind. Greed and fear have significantly attracted large numbers of buyers and sellers. Due to this reason market gets a new trend. Over the past few decade the focus of GARCH modelling of stock market volatility has been highlighted. Over the last decade there has been a lot of study on the GARCH model in the developed economy. These papers are mainly talking about numerous volatility models, and the ability to predict and hold the specific features of conditional differences about experienced financial data. In my paper, I chosen the three basic models such as GARCH, EGARCH and TARCH which are the family members of ARCH model. At the same time, I find performance estimates of different market, I use three different distributions on error term such as Normal Distribution, Student-t Distribution and General Error Distribution (GED). Finally, the question is arising which model is considering as best model? To find out the best model I took AIC and BIC values because the guideline suggest that lower the value better the model. Here, I take several important global stock markets indexes which have healthy volatility: DOW JONE’s daily index (USA), FTSE100 daily index (UK), HANG SENG daily index (Hong Kong), NIKKEI daily index (Japan) and NIFTY daily index (India).

This study provides a comprehensive evaluation of five deep learning (DL) architectures—TiDE, LSTM, DeepAR, TCN, and Transformer—against the extended Heterogeneous Autoregressive (HAR) model for stock market volatility forecasting. Utilizing 22.5 years of high-frequency data from the S&P 500, DJIA, and Nasdaq indices and incorporating key macroeconomic variables (DXY, VIX, US10Y, and US1M), we assess predictive accuracy across multiple horizons from one day to one month. Our analysis yields three main findings. First, when macroeconomic variables are included, DL models consistently and significantly outperform the HAR benchmark, with TiDE excelling in one-day-ahead predictions and DeepAR dominating longer horizons. Second, in the absence of these exogenous variables, the statistical advantage of DL models over HAR often disappears, highlighting HAR’s enduring relevance in feature-constrained settings. Third, among the DL architectures, DeepAR emerges as the most robust and versatile performer, especially when leveraging macroeconomic data. These results underscore the conditional power of deep learning and provide practical guidance on model selection for financial practitioners and researchers.

Do climate change and geopolitical risk influence volatility? Empirical evidence from leading economies.

Your fear is (partly) mine: the role of non-VIX volatility in forecasting regional stock market volatility using interpretable machine learning

Copula-based dynamic networks for forecasting stock market volatility

Synchronization, which has been a common natural phenomenon, occurs frequently in complex financial systems and is an important contagion mechanism for systemic financial risks and even financial crises. In view of this, we construct a coupled stochastic volatility model and its volatility synchronization analysis framework and combine machine learning methods and rolling cycle window to propose a prediction method for dynamic volatility synchronization. Taking the Shanghai Composite Index (SSEC) and Shenzhen Component Index (SZI) as binary synchronization examples, we analyze the dynamic forecasting performance of the proposed method in an in-sample and out-of-sample empirical comparison by combining multiple loss functions and Superior Predictive Ability (SPA) tests for high-frequency data. It is found that the in-sample estimates of our proposed model are highly consistent with the market behavior and that the model outperforms other models in predicting stock market volatility synchronization accuracy. In addition, by combining dynamic simulation with multivariate empirical mechanism analysis, our methodology not only explores synchronization dynamics but also identifies significant risk events, providing a comprehensive framework for understanding complex system behaviors.

Purpose The primary purpose of this study is to understand how social media affects the stock market through the Market Uncertainty Unit on X (formerly known as Twitter) in both the short and long run. Design/methodology/approach We use an autoregressive distribution lag (ARDL) model. This method is common in financial research and is particularly useful for analyzing how quickly the market reacts to changes in the economy or investor sentiment. By analyzing monthly dataset since 2011, we study the impact of social media sentiment on five major US stock indices, such as the S&P 500, NASDAQ, USA, NYSE and Dow Jones Industrial Average (DJI). The explanatory variables include the Social Media Uncertainty Index (SMX), real effective exchange rate (REX), consumer price index (CPI), interest rate (IR), industrial production index (INPT) and a linear deterministic TREND variable. Findings We find that whenever SMX rises, stock market volatility is also amplified, especially in the short run. Fluctuations in social media sentiment are often a strong signal of short-term stock price changes, especially when discussing economic policy or corporate earnings. Originality/value The fact that a few emotional or controversial tweets from executives can directly pull stock prices up and down further illustrates the importance of digital communication in investment decisions. Investors and financial analysts can use social media sentiment as an aid tool to help them more accurately predict market movements and reduce investment risk.

The study of recurrent events, such as hospital readmissions or stock market volatility spikes, is important in biostatistics and finance but poses statistical challenges due to complex dependencies and censoring. In this paper, we propose a semiparametric framework for analyzing recurrent event data, focusing on gap times between successive events. We develop estimating functions to sequentially estimate regression parameters, yielding estimators with desirable properties, while accommodating time-varying covariates and right-censoring. Unlike previous approaches, we establish a strong law of large numbers and a central limit theorem for stopped martingales under random, potentially unbounded stopping times, useful for inference in dynamic settings. We illustrate the relevance of our approach to the analysis of longitudinal data and autoregressive models.

Utilization of BiLSTM-RNN to identify tweets for forecasting stock market volatility