How Does News Affect Stock Return Volatility in a Frontier Market?

Macroeconomic Impact on Stock Market Returns and Volatility: Evidence from Sri Lanka

PurposeThis study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.Design/methodology/approachThis study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.FindingsThe outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.Research limitations/implicationsThe analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.Originality/valueMost of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

The paper provides an updated evidence of the linkage between stock market and macroeconomic factors in Pakistan. The sample period is from January 2011 to November 2017. Macroeconomic variables used are money supply, exchange rate, treasury bill rate, inflation and industrial production. Generalized autoregressive conditional heteroscedasticity (GARCH) models have been used to examine the impact of macroeconomic factors on stock market return and stock market volatility. Findings suggest that macroeconomic factors have an impact on stock market volatility. The fluctuations in inflation and money supply negatively influence the volatility of stock market returns. In contrast, industrial production positively affects the fluctuations of stock market returns. The findings are important for shareholders, investors, regulatory authorities and policymakers.

Chapter 2 - The Effects of Natural Disasters on Stock Market Return and Volatility in Hong Kong

Shifts in Volatility Driven by Large Stock Market Shocks

This paper studied the impact of inflation rates and US Dollar exchange rates in Indonesian stock market return volatility in the period of 2002-2012. Daily data of stock market return, inflation rates and US Dollar exchange rates were used to estimate the daily volatility of the stock return. The data of stock market price, inflation rates and US Dollar exchange rates were taken from Yahoo Finance, Indonesia statistics bureau and central bank of Indonesia, respectively. The result showed that both inflation rates and US Dollar exchange rates did not have a significant impact on the Indonesian stock market return volatility. Inflation rates contributed 0.03% change in daily stock market return volatility whereas a 1% appreciation of Rupiah contributed to a 0.0000001% change in daily stock market return volatility. This research finding was different from the result in Nigeria where Yaya’s & Shittu’s (2010) research had the same variables. Inflation rates and US Dollar exchange rates were to examine the impact on Nigeria stock market index return volatility.

The Indonesian Sharia stock market has experienced significant growth over the past year, accompanied by an increase in market capitalization. However, high volatility remains a critical challenge for investors when deciding to invest in Sharia stocks. Modeling Sharia stock return volatility is essential to help investors minimize investment risks. This study aims to identify the most effective model for measuring volatility in the Sharia stock market by comparing classical models such as Generalized Autoregressive Conditional Heteroscedasticity (GARCH), asymmetric models including Exponential GARCH (EGARCH), Threshold GARCH (TGARCH), and Asymmetric Power GARCH (APGARCH), along with the Markov Switching GARCH (MSGARCH) and a Bayesian MSGARCH model that incorporates structural regime changes. The results indicate that the Bayesian MSGARCH model outperforms the other models in capturing the volatility of the Jakarta Islamic Index Sharia stock returns, achieving the lowest prediction error and improved accuracy in parameter estimation. Moreover, the study reveals that investment activities influence the volatility structure during periods of market appreciation and depreciation, with identifiable durations, thereby providing valuable insights for the formulation of effective investment strategies.

It is sometimes argued that an increase in stock market volatility raises required stock returns, and thus lowers stock prices. This paper modifies the generalized autoregressive conditionally heteroskedastic (GARCH) model of returns to allow for this volatility feedback effect. The resulting model is asymmetric, because volatility feedback amplifies large negative stock returns and dampens large positive returns, making stock returns negatively skewed and increasing the potential for large crashes. The model also implies that volatility feedback is more important when volatility is high. In U.S. monthly and daily data in the period 1926-88, the asymmetric model fits the data better than the standard GARCH model, accounting for almost half the skewness and excess kurtosis of standard monthly GARCH residuals. Estimated volatility discounts on the stock market range from 1% in normal times to 13% after the stock market crash of October 1987 and 25% in the early 1930's. However volatility feedback has little effect on the unconditional variance of stock returns.

A dynamic neural network (DNN) and a new computationally efficient functional link artificial neural network (CEFLANN) combination optimised with differential evolution (DE) is presented in this paper to predict financial time series like stock price indices and stock return volatilities of two important Indian stock markets, namely the Reliance Industries Limited (RIL), and NIFTY from one day ahead to one month in advance. The DNN comprises a set of 1st order IIR filters for processing the past inputs and their functional expansions and its weights are adjusted using a sliding mode strategy known for its fast convergence and robustness with respect to chaotic variations in the inputs. Extensive computer simulations are carried out to predict simultaneously the stock market indices and return volatilities and it is observed that the simple IIR–based DNN–FLANN model hybridised with DE produces better forecasting accuracies in comparison to the more complicated neural architectures.

IN MY judgment," maintained William McChesney Martin, Jr., Chairman of the Board of Governors, Federal Reserve System, "we can never under the present margin regulations have the same result that occurred in terms of a financial crash in 1929 through undermargined accounts, low margins resulting in a financial debacle."' A widely used elementary economics textbook claimed, "Margin requirements have exercised an important restrictive influence on security speculation, as contrasted with the uncontrolled period of the late 1920's... Nearly everyone agrees that Federal Reserve margin requirements exercise a healthy restraint on speculative stock purchases in a boom" (Bach, 1963, p. 116). But what evidence is there that margin requirements have the effects claimed? At the December meetings of the American Economic Association, George J. Stigler, in the Presidential address, called on economists to test their assumptions that state regulation of private activity has indeed been successful (Stigler, 1965). This article brings together some data on the efficacy of margin requirements. While the figures can only be considered preliminary, and more data should be collected to test margin requirements, the data that are available and presented here indicate that not one of the aims of the legislation establishing margin requirements has been accomplished.2

This paper estimates and compares alternative distribution density forecast methodology of three generalised autoregressive conditional heteroscedasticity (GARCH) models for Botswana and Namibia stock market returns. The symmetric GARCH and asymmetric Glosten Jagannathan and Runkle (GJR) version of GARCH (GJR-GARCH) and exponential GARCH methodology are employed to investigate the effect of stock return volatility in both stock markets using Gaussian, Student-t and generalised error distribution densities. The evidence reveals that the current shocks to the conditional variance will have less impact on future volatility in both markets. News impact is asymmetric in both stock markets leading to the existence of leverage effect in stock returns. Besides, both markets exhibit reverse volatility asymmetry, contradicting the widely accepted theory of volatility asymmetry. Regarding forecasting evaluation, the results reveal that the symmetric GARCH model coupled with fatter-tail distributions present a better out-of-sample forecast for both stock markets.

Forecasting stock market volatility is an important and challenging task for both academic researchers and business practitioners. The recent trend to improve the prediction accuracy is to combine individual forecasts using a simple average or weighted average where the weight reflects the inverse of the prediction error. However, a problem in the existing forecast combination methods is that the weights remain fixed over time. This may prove inadequate, especially in economics data, where changes in policy regimes may induce structural change in the pattern of forecast errors of the different models, thereby altering the relative effectiveness of each model over time. In this paper, we present a new forecast combination approach where the forecasting results of the Generalized Autoregressive Conditional Heteroskedastic (GARCH), the Exponential GARCH (EGARCH), stochastic volatility(SV), and Moving average(MAV) models are combined based on time-varying weights that can be driven by regime switching in a latent state variable. The results of an empirical study indicate that the proposed method has a better accuracy than the GARCH, EGARCH, SV and MAV models, and also combining forecast methods with constant weights.KeywordsCombining forecastsGARCHStochastic volatilityRegime switchingStock market volatility

The impact of oil price shocks on the stock market return and volatility relationship

We investigate the empirical relationship between stock returns, return volatility and trading volume in the Brazilian stock market (Bovespa). Our sample contains stock return and trading volume data from a theoretical portfolio including stocks participating in the Bovespa Index (Ibovespa) extending from 01/03/2000 through 12/29/2005. The empirical methods used include cross-correlation analysis, unit-root tests, bivariate simultaneous equations regression analysis, GARCH and VAR models, and Granger causality tests. We find support for a contemporaneous as well as a dynamic relationship between stock returns and trading volume, implying that forecasts of one of these variables can be only slightly improved by knowledge of the other. Besides, our results indicate that contemporaneous and dynamic relationships between return volatility and trading volume also exist. Additionally, by applying Granger’s causality test, we find that return volatility contains information about upcoming trading volume and vice versa.

The onset of the novel coronavirus pandemic (COVID-19) and previous financial and currency crises have heightened interest in understanding the nature of the interaction of stock market and exchange rate volatility. This paper aims to investigate the interdependence and volatility transmissions between the stock and foreign exchange markets for South Africa over the period of 1979:01–2021:08, including the effect the COVID-19 pandemic has had on the interdependence and volatility transmissions. Through the use of bivariate Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) modeling, the empirical outcomes from this study provide strong evidence in support of the “stock-orientated” approach, where significant price and volatility spillovers propagate from the stock market into the foreign exchange market, whilst evidence of the “flow-orientated” approach is seen in the second moment and significant shock and asymmetric spillovers from the exchange to stock market are found. The results support the asymmetric and long-range persistence volatility spillover effect and show strong evidence of contagion between stock and foreign exchange markets. These spillovers became more pronounced during the COVID-19 pandemic, confirming heightened contagion in these markets during the periods of crisis. The results heed important implications for not only policymakers who are concerned by the contagion across financial markets and better regulations of these markets to promote economic growth, but also investors and fund managers who seek to hedge investment risks in South Africa.