Volatility Spillovers Between Stock Prices and Exchange Rates: Insights for Risk Management and Investment Strategies: Evidence from China, India, and Pakistan Using BEKK-GARCH Models

Volatility Modelling of the BSE SENSEX Using the ARCH Model: An Empirical Study of Select Large-Cap Constituent Stocks (2021–2026)

Stock market volatility is of continuing interest to investors, portfolio managers, corporates and policymakers because it directly influences risk assessment, asset pricing and capital allocation decisions. This paper examines the return-generating and volatility process of the Bombay Stock Exchange Sensitive Index (BSE SENSEX) using the Autoregressive Conditional Heteroskedasticity (ARCH) model of Engle (1982), estimated separately for five consecutive financial years spanning April 2021 to January 2026. Daily closing prices of the SENSEX and ten actively traded, large-capitalisation constituent stocks were employed as explanatory variables in the mean equation, while the conditional variance was modelled as a GARCH(1,1)-type specification and estimated through Maximum Likelihood in EViews. The results show that the ARCH coefficient is positive and statistically significant in four of the five years, confirming the presence of volatility clustering in SENSEX returns, whereas the GARCH persistence term is small and, in three of the five years, negative, implying that shocks to variance die out quickly rather than persisting. The explanatory power of the mean equation ranged between 0.69 and 0.81 across the five years, and descriptive statistics of daily returns confirm negative skewness and excess kurtosis, validating the use of an ARCH-family model over ordinary least squares. The findings carry implications for risk management, portfolio diversification and short-horizon volatility forecasting in the Indian equity market.

Volatility Dynamics and Forecasting of the Nepal Stock Exchange Index: A Comparative Analysis of GARCH and EGARCH Models

Volatility is a fundamental characteristic of financial markets and plays a crucial role in investment decision-making, portfolio management, and financial risk assessment. Understanding the behaviour of stock market volatility is particularly important for frontier markets such as the Nepal Stock Exchange (NEPSE), where market fluctuations may differ from those observed in developed economies. This study investigates the volatility dynamics and forecasting performance of the NEPSE Index using daily closing prices from March 2021 to March 2026. Daily logarithmic returns were analysed following tests for stationarity and conditional heteroskedasticity. The volatility process was modelled using the symmetric GARCH(1,1) and asymmetric EGARCH(1,1) models, while model adequacy and forecasting performance were evaluated through diagnostic tests, information criteria, rolling-window out-of-sample forecasting, the News Impact Curve, volatility half-life estimation, and structural break analysis. The empirical findings confirm that NEPSE returns exhibit the stylized characteristics of financial time series, including volatility clustering, leptokurtosis, and persistent conditional volatility. The EGARCH model provides a marginally better in-sample fit and identifies significant asymmetric volatility, with negative market shocks producing stronger increases in future volatility than positive shocks of similar magnitude. However, the GARCH(1,1) model demonstrates slightly superior out-of-sample forecasting performance, indicating that the simpler specification remains more reliable for short-term volatility prediction. The analysis also identifies a structural break in July 2022, suggesting that volatility dynamics changed during the study period and that persistence estimates should be interpreted with caution. By integrating model comparison, forecasting evaluation, News Impact Curve analysis, volatility persistence, and structural break testing within a single analytical framework, this study provides a comprehensive assessment of NEPSE volatility and offers practical insights for investors, portfolio managers, policymakers, and future researchers interested in frontier equity markets.

The Impact of Exchange Rate Volatility on Foreign Direct Investment in Emerging European Economies: Empirical Evidence from Hungary, Poland, and Romania

This study investigates the impact of real effective exchange rate (REER) volatility on foreign direct investment (FDI) inflows in three major Central and Eastern European (CEE) economies—Hungary, Poland, and Romania—using quarterly data spanning from 2007-Q1 to 2024-Q4. The exchange rate volatility is modeled using a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework, and country-specific relationships are estimated through Autoregressive Distributed Lag (ARDL) bounds testing and Toda–Yamamoto causality analysis. Our research indicates that a uniform relationship does not exist across the region. In Hungary, the utilization of directional FDI data excluding Special Purpose Entities (SPEs), in conjunction with structural breaks and quarterly seasonal controls, reveals a statistically significant nonlinear (inverted U-shaped) relationship between long-run exchange rate volatility and FDI inflows. In addition, domestic financial development exerts a substantial buffering effect on the transmission of volatility in Hungary by bypassing SPE flows that previously obscured this effect. In Poland and Romania, a stronger currency consistently discourages investment by reducing cost competitiveness. Romania shows a distinct pattern: volatility initially attracts FDI, and while deeper financial markets meaningfully dampen this effect, the net relationship remains positive, unlike Hungary, where sufficiently deep credit markets fully reverse it. These results suggest that policymakers should look beyond short-term exchange rate stabilization and instead prioritize structural reforms, competitive exchange rate levels, transparent FDI reporting standards, and deeper domestic financial markets to sustain FDI inflows.

Exploring the Dynamics of ZAR/USD Exchange RateVolatility Using the fGARCH and First-Order Beta-Skew-T-EGARCH Models

This study investigates and explores the volatility dynamics of the South African rand against the US dollar (ZAR/USD) using the Family GARCH (fGARCH) model and the First-Order Beta-Skew-T-Generalised Autoregressive Conditional Heteroskedasticity (Beta-Skew-T-EGARCH) model. Currency volatility across the globe, uncertainties, and instability in emerging markets have become increasingly consequential for trade flows, investment allocation, and macroeconomic management. The ZAR/USD serves as a benchmark of South Africa’s economic wealth and vulnerability to external shocks and is one of the most valued, significant, and heavily traded pairings of emerging market currencies. Simple standard GARCH (sGARCH) is one of the most useful models for exchange rate volatility; however, the sGARCH model has some limitations: it fails to accommodate or allow the long memory effects, skewness distribution, and leverage dynamics consistently observed in emerging-market currency returns. This study addresses these limitations by using the fGARCH model, which includes the most popular GARCH models and Beta-Skew-T-EGARCH for daily ZAR/USD returns ranging from 5 January 2000 to 1 October 2024. Five innovation distributions are used for evaluation and comparison under fGARCH and sGARCH, namely generalised hyperbolic (GH), generalised error (GED), skewed Student’s t (SSTD), skewed generalised error (SGED), and Student’s t (STD), with model fitness criteria assessed using the Shibata criterion (SIC), Hannan–Quinn criterion (HQ), Bayesian information criterion (BIC), and Akaike information criterion (AIC), choosing the specification with the lowest overall penalty. It is found that the fGARCH(1,1) model fitted to return-frequency data under the SSTD achieves the lowest AIC, outperforming sGARCH. The study also includes an analysis among covariates, which are day, month, trend, oil, and platinum; the trend variable is a statistically significant predictor, with p = 0.007, showing a positive influence on ZAR/USD volatility. The Beta-Skew-T-EGARCH model with two components divides volatility into long-run and short-run components, which is found to deliver a superior fit over the one-component variant, evidenced by a lower BIC (3.068435) and a higher log-likelihood (−748.464826). The two components confirm that the model captures declining conditional volatility, whereas the one-component model sustains persistence in the evaluated estimates.

Price Conflict and US Stock Return Volatility Forecasting: Insights from over 150 Years with a Mixed-Frequency Framework

A Comparative Study of Stock Return and Volatility Across Major Indian Sectors Using ARIMA and GARCH Model

Forecasting stock returns and analyzing market volatility are important aspects of financial research, as they help investors and financial analysts make informed decisions while managing investment risk. This study examines the return and volatility behaviour of five major sectors of the Indian economy: Banking, FMCG, Information Technology, Pharmaceutical & Healthcare, and Renewable Energy & Power. Using ten years of daily stock price data, the study applies the Auto-regressive Integrated Moving Average (ARIMA) model to forecast stock returns and the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to analyse volatility dynamics. The results indicate that return predictability and volatility persistence vary across sectors, reflecting differences in risk and market sensitivity. While sectors such as FMCG exhibit relatively stable performance, Banking, Pharmaceutical & Healthcare, and Renewable Energy show higher levels of volatility. The findings demonstrate the effectiveness of ARIMA and GARCH models in capturing sector-specific market behaviour and provide valuable insights for investors, portfolio managers, and researchers seeking to improve investment decisions, portfolio diversification, and risk management strategies in the Indian stock market.