The Volatility Effect: Evidence from India

PurposeThe study examines herding in the Indian stock and commodity futures market including agricultural, metal and energy commodities. Herding is studied under various market conditions: rising and declining, high and low volatility. The study also examines spillover effects of herding.Design/methodology/approachThe study adapts the cross-sectional absolute deviation model given by Chang et al. (2000) to examine herding in Indian stock and commodity futures markets.FindingsThe results of the study indicate absence of herding among commodity futures under all market conditions except for the declining market where herding is present among energy futures. The investors investing in agricultural and energy commodities have a higher tendency to herd during high volatility days as compared to low volatility days. Further, the study of herding spillover effects indicates that the price fluctuations in metal commodities affect herding in agricultural and energy commodities.Research limitations/implicationsThe results can help market participants to diversify the risk by investing in agricultural, metal and energy futures along with the stocks.Originality/valueMajority of the previous studies explore herding among stocks and ignore commodities especially agricultural commodities. This study attempts to fill the gap by studying herding among various commodity futures. To the best of our knowledge this is the first study to explore herding spillover effects in the Indian stock and commodity futures market.

Does Investor Heterogeneity Lead to IPO Overvaluation?

This paper shows how stock market volatility regimes affect the cross-section of stock returns along quality and liquidity dimensions. We find that, during crisis periods, low quality and low liquidity stocks experience relatively higher losses than predicted in normal times, while high quality and high liquidity stocks experience rather relatively lower losses. These findings lend strong support to the presence of cross-market and within-market flight-to-quality and to-liquidity episodes during crisis periods. During low volatility periods, however, low quality and low liquidity stocks earn relatively larger returns, while high quality and high liquidity stocks yield lower returns; suggesting that low volatility conditions benefit junk and illiquid stocks but not quality and liquid stocks. Finally, our results reveal that liquidity level dominates liquidity beta in explaining stock returns across the different market volatility regimes.

Abstract: This paper investigates return and volatility spillover effects between the FTSE 100, FTSE 250 and FTSE Small Cap equity indices using the multivariate GARCH framework. We find that return and volatility transmission mechanisms between large and small stocks in the UK are asymmetric. In particular, there are significant spillover effects in both returns and volatility from the portfolios of larger stocks to the portfolios of smaller stocks. For volatility, there is also evidence of limited feedback from the portfolios of smaller stocks to the portfolios of larger stocks, although sub‐period analysis suggests that this is to some extent period‐specific. Simulation evidence shows that non‐synchronous trading potentially explains some, but not all, of the spillover effects in returns, and that it explains none of the spillover effects in volatility. These results are consistent with a market in which information is first incorporated into the prices of large stocks before being impounded into the prices of small stocks.

ASEAN authorities took several steps to facilitate firms’ dual listing decisions within their region. However, more than three quarters of ASEAN-5 firms chose to list in European markets, which raises the need for further investigation to assess the determinants of destination choice decisions. Therefore, this study aims to evaluate the determinants of firms’ destination choice decisions. The logistic model has been employed in this study to evaluate the firms’ destination choice and uncover the determinants that drive the dual listing destination choice decision between Europe and the US. The study collected data on firms from the ASEAN-5 countries for the period of 2003–2017. The study’s findings showed that the higher the home country’s trade openness, the lower the number of firms pursuing a dual listing in European markets. Meanwhile, the greater the openness to FDI, the more likely it is that firms will seek to list in European markets. In addition, European markets are considered the main destination for firms characterized by low ownership concentration and high stock volatility. On the other hand, the US markets are the main choice for firms that originated from countries with low trade openness and high FDI openness, as well as for firms that are described as having high ownership concentration and low stock volatility. The current study has provided information to the authorities, investors, and market makers on the relationship between the abovementioned determinants and destination choice decisions, specifically for firms from the ASEAN-5 countries.

<div class="htmlview paragraph">The objective of this research was to characterize the effects of fuel component volatility on gasoline direct injection (GDI) engine cold start. Three different fuel components, representing gasoline light end, mid-point and heavy end components, were used to form three fuel blends of different volatility. Performance tests and in-cylinder fuel distribution imaging tests using these fuel blends were carried out in a firing single-cylinder optically-accessible engine following a simulated cold start test schedule. Performance results, based on in-cylinder pressure and engine-out hydrocarbon measurements, during the initial transient phase of GDI cold start showed significantly degraded performance with the low volatility fuel blend, while the high volatility blend showed slightly improved performance. Neither the low nor high volatility fuel, however, showed a discernable effect on the quasi-steady state cold start performance. Simultaneous planar laser-induced fluorescence and Mie scattering images showed that fuel volatility affects spray development and fuel-air mixing and as a result the in-cylinder fuel distribution. In particular, high volatility components, because they vaporize more quickly, result in smaller drops, which have less penetration; whereas, low volatility components, because they vaporize more slowly, result in larger drops, which have greater penetration and as a result are more likely to impinge on the cylinder walls. The increased wall wetting that occurs with low volatility fuels effectively reduces the equivalence ratio, causing the poor cold start performance observed with the low volatility fuel blend</div>

Bitcoin is emerging as a distinct asset class among investors given its seemingly detached price behavior relative to market and economic fundamentals. Its incomparably high returns in recent years has further fuelled intense interest and investment into Bitcoin and cryptocurrencies at large. This paper cautions that Bitcoin prices, despite their seemingly attractive independent behavior relative to economic variables, may still be exposed to the same types of market risks which afflict the performance of conventional financial assets. Using a Markov regime-switching model to distinguish between regimes of high and low Bitcoin price volatility, this paper shows that while returns on the aggregate market portfolio cannot explain Bitcoin returns, other asset pricing risk factors, such as interest rates and implied stock market and foreign exchange market volatilities, are important determinants of Bitcoin returns. Distinguishing between periods of high and low Bitcoin price volatility reveals heterogeneity in the explanatory power of market risk factors; in particular, Bitcoin returns are more difficult to explain during periods of high volatility relative to periods with low volatility. This finding can partially explain why extant studies, which neglect to distinguish between exchange rate regimes in Bitcoin, have difficulty linking Bitcoin prices to economic fundamentals.

Style Migration and the Cross-Section of Average Stock Returns

IPOs are essential for companies to raise capital and offer growth potential to investors. Despite global uncertainties and liquidity challenges, ‎IPO activity is expected to rebound, driven by stronger momentum and larger deals. This study analyzes factors influencing IPO listing ‎prices in the Indian stock market from 2020 to 2024, a period marked by global disruptions and domestic political changes. Using data from ‎‎271 IPOs, the study explores the impact of macroeconomic factors, IPO-specific variables, and secondary market conditions. Multiple linear ‎regression shows that GDP growth, IPO size, subscription rates, and P/E ratios positively affect listing prices, while higher interest rates ‎have a negative impact. Larger IPO sizes and higher subscription rates further reflect strong investor confidence and demand. Market volatility and index performance have minimal direct effects, suggesting their influence may be mediated by other factors. These findings offer ‎insights for policymakers, companies, and investors navigating IPO markets‎.

We examined the actions of retail investor’s pre and post COVID-19 pandemic in the Indian stock market. In 2020, Covid impacted the entire year, and the Nifty range was highlighted at the end of each month, reflecting the same year’s Nifty range and a bullish trend by the year’s conclusion. The contribution of retail investor is huge number in Indian stock market. Year on year this number has increased other than HNI, Institutions investors etc. Beside the creation portfolio which generated financial decision by investors behaviour. The Study defined fundamental & technical analysis terms build portfolio of investor. The rate of demat account openings has increased over the years, and currently, this figure for retail continues to rise consistently. The study investigated the history of Indian stock market as well all crashed impacted on market. Then emerge of BSE, NSE, and Nifty. Market sentiment & Volatility effect on Indian retail investor’s behaviour. The study shows benefits of investing.

Investor Herds and Regime-Switching: Evidence from Gulf Arab Stock Markets

Cardiovascular Outcomes in the Wake of Financial Uncertainty.

inflation rate and exchange rate volatility alongside the increase in interest towards investments within the cryptocurrencies market. This study examines how Bitcoin prices, gold prices, Turkey’s Consumer Price Index, and USD/TL exchange rate affected the returns and volatility of the BIST 100 stock market between February 2021 and March 2025. Methodology- This article applies various econometric models, which include E-GARCH, Markov regime-switching, Bayesian impulse response analysis. Findings– E-GARCH analysis reveals that the effect of Bitcoin and gold prices on the volatility of BIST 100 is insignificant, while the effect of Turkish CPI and USD/TL exchange rate is significant and positive. The analysis of the Markov regime-switching model reveals that for the first regime (high volatility), BIST 100 returns are only influenced by USD/TL. In the second regime (low volatility), the significant negative impact is caused by Bitcoin, while the positive impacts are caused by gold, USD/TL exchange rate, and inflation on BIST 100 returns. The Bayesian impulse response analysis reveals that the initial positive impacts are caused by Bitcoin, USD/TL exchange rate, and CPI, while the impact of gold is negative. At the start of Bayesian impulse response analysis, the largest positive impact is caused by Bitcoin, which decreases as the influence of the USD/TL exchange rate increases. At the end of analysis, the impact of Turkish CPI is found to be the largest on BIST 100 returns. Conclusion- On the whole, the results indicate that although Bitcoin and gold have differential effects on BIST 100 returns depending on market conditions, the primary determinants of BIST 100 returns are the inflation rate and USD/TL exchange rate

This paper attempts to answer the question of how much of its current production a gold producer should hedge by forward selling. We develop a model which allows a company manager to determine the optimal (profit-maximising) hedge, and an estimate of the risk of company failure, based on an estimate of the forward price of gold; the mine’s operating costs; and the company’s tolerance to debt. The model is illustrated with data drawn from historical gold prices and recent Australian company data. By simulating multiple instances of companies operating with different degrees of forward selling under varying levels of volatility in the price of gold, it was shown that the optimal degree of forward selling increased as volatility increased. The model allows managers to gain insights into the effect of volatility on a company's return given its particular cost structure which in turn, supports more informed decision making. The model illustrates why, during the recent period of constant growth and low volatility in gold prices, gold producers are reducing their hedge books.

This paper investigates the switching effect of COVID-19 pandemic and economic policy uncertainty on commodity prices. We employ Markov regime-switching dynamic model to explore price regime dynamics of eight widely traded commodities namely oil, natural gas, corn, soybeans, silver, gold, copper, and steel. We fit two Markov switching regimes to allow parameters to respond to both low and high volatilities. The empirical evidence shows oil, natural gas, corn, soybean, silver, gold, copper, and steel returns adjust to shocks in COVID-19 outcomes and economic policy uncertainty at varying degrees––in both low volatility and high volatility regimes. In contrast, oil and natural gas do not respond to changes in COVID-19 deaths in both regimes. The findings show most commodities are responsive to historical price in terms of demand and supply in both volatility regimes. Our findings further show a high probability that commodity prices will remain in low volatility regime than in high volatility regime––owing to COVID-19-attributed market uncertainties. These findings are useful to both investors and policymakers––as precious metals and agricultural commodities show less negative response to exogenous variables. Thus, investors and portfolio managers could use precious metals, viz. Gold for short-term cover against systematic risks in the market during the period of global pandemic.