Short-run Volatility Research Articles

Volatility Spillover from the Chinese Stock Market to the G20 Stock Markets in the Wake of the Pandemic COVID-19

We analyze the volatility spillover effect from the Chinese stock market to different stock markets in the G20 countries. We employ dynamic conditional correlation and vector autoregression (VAR) to analyze adjusted daily closing stock indices extending from 1st October 2019 to 30th June 2020. The result reveals that there is short-run volatility in sample stock return except Australia and South Korea. Similarly, there is long-term volatility persistence in sample countries’ stock exchange except Australia, Saudi Arabia, Russia, and France. However, Australia is only the country where there is no short- and long-run information transmission derived from China. Therefore, there is a portfolio diversification opportunity in this country during COVID-19. Overall, this paper shows significant interdependencies between the Chinese and the G20 markets which furnish momentous implications to the stakeholders of markets.

Return volatility of Asian stock exchanges; a GARCH DCC analysis with reference of Bitcoin and global crude oil price movement

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.

Volatility Dynamics of Base Metal Futures: Empirical Evidence from an Emerging Economy

The paper examines the leverage effects and the spillover effects in the base metal cash and futures market. The study also attempts to find the trend and the pattern of volatility clustering in the base metal markets of India. Further, the significance of the risk premium and the possible downside risk of the market are also examined. The study confirms that unlike aluminium futures market, leverage effect is found for copper futures traded at MCX, India. Similar to aluminium, it is evident that the market advances generate larger volatility than the market turbulence in the cash and futures market of nickel. The study finds that the variance term (ξ) is not statistically significant for both cash and futures markets, which indicates that the risk premium of the asset is not significant to hedge. Further, unlike copper and aluminium, short-run volatility spillover is absent from the futures to the cash market of nickel. The paper concludes that the long-run volatility shock of futures has a persistent effect on the cash market of aluminium, copper and nickel and vice-versa. Future research might address the cross-volatility spillover between the base metal futures market. Further, the spillover between the Indian and London base metal futures markets is left for future research.

Fintech industry risk: does investor attention matter?

ABSTRACT The Fintech industry risk challenges the stability of China’s financial system. This paper makes the first attempt to capture the dynamics of the Fintech industry risk, represented by Fintech stock volatility, from the investor attention perspective. The novelty of this paper is that we fully consider the regime changes in the Fintech industry risk by adopting the AR-MS-GARCH-MIDAS with the Markov regime-switching process incorporated in the short-run volatility component, long-run volatility component, or both. We find that (1) Fintech industry risk is subject to structural breaks, (2) higher investor attention tends to alleviate the Fintech stock volatility, while the negative impact strengthens when the industry is in volatile condition, (3) investor attention contributes to improving the accuracy of the Fintech stock volatility forecasting, (4) considering the structural breaks of investor attention is important in volatility forecasting. We enrich the literature on the Fintech industry risk regulation, stock volatility forecasting, and the attention allocation theory.

Causes of Exchange Rate Volatility

This study examined the determinants of exchange rate volatility basing evidence on 7 African countries; Niger, Sudan, Cameron, Equatorial Guinea, Tunisia, Congo, and Cote D’Ivoire from 1990-2023. The study conducted the Autoregressive Distributive Lag (ARDL) bounds testing for co-integration and also estimated the error correction model. Furthermore, ARCH and GARCH models were analyzed to measure the volatility of a time series by fitting an autoregressive model to the squared residuals of the time series. The ARCH and GARCH results suggest the volatility of the exchange rate markets in the aforementioned countries is not random. The speed of adjustment of the volatility in the exchange rate of the Sudanese economy is 39%, in Niger Republic it is 50%, in Cameroon it is 52%, in Tunisia it is 55%, in Congo the speed is 32%, in Equatorial Guinea, the speed of adjustment is 58% and in Côte D’Ivoire the speed is 45%, respectively. The study found that the determinants of exchange rate volatility among African countries vary depending on the specific country. The observed volatility in the Sudanese exchange rate was anchored by the significant positive influence of inflation and income differentials as well as the significant negative influence of interest rate differential. In Niger Republic, exchange rate volatility was driven by the significant positive influence of productivity growth and money supply as well as the significant variation in oil prices and interest rate differentials. The observed short-run volatility in Cameroon's exchange rate was significantly and positively influenced by inflation differential and money supply variation whereas it was significantly but negatively propelled by interest rate differential and oil price shock. In Tunisia, exchange rate volatility was stimulated by the significant positive influence of inflation differential, productivity growth, oil price shock, and the significant negative role played by trade balance. The observed short-run volatility in the Congolese exchange rate was induced by the significant positive impact of inflation differential, income differential, trade balance, variation in money supply, and the significant negative impact of interest rate differential. In Equatorial Guinea, the observed exchange rate volatility was determined based on the significant and positive impact of differential in the inflation rate, oil price shock, changes in the money stock, and the foreign balance of trade. The observed volatility in the Côte D’Ivoire exchange rate was significantly and positively driven by the differentials in inflation rate, interest rate, and income level, the foreign trade balance but significantly stimulated by the negative influence of oil price shock. The general policy advice is that governments of all the countries covered by the study should implement exchange rate controls to limit the volatility of their currency fluctuation by imposing a limit on the amount of foreign currency that can be traded in the country. African governments should monitor the inflation differential between their own country and their trading partners to see if it is becoming too large. If it is, the government might raise interest rates to make its currency more attractive to investors.

Powers Correlation Analysis of Returns with a Non-stationary Zero-Process

Abstract The higher order dynamics of individual stocks is investigated. We show that classical powers correlation analysis can lead to a spurious assessment of the volatility persistence or long memory volatility effects, if the zero return probability is non-constant over time. In other words, classical tools are not able to distinguish between long-run volatility effects, such as IGARCH, and the case where the zero returns are not evenly distributed over time. As a remedy, new diagnostic tools are proposed that are robust to changes in the zero return probability. Since a time-varying zero return probability could potentially be accompanied by a non-constant unconditional variance, we also develop powers correlation analysis that is robust in such a case. In addition, the diagnostic tools we propose offer a rigorous analysis of the short-run volatility effects, while the use of the classical powers correlations lead to doubtful conclusions. Monte Carlo experiments, and the study of the absolute value correlation of daily returns taken from the Chilean financial market and the 1-min returns of Facebook stocks, suggest that the volatility effects are only short-run in many cases.

Contagion or interdependence Evidence from Asian emerging stock markets in times of COVID-19 pandemic

This study examines the presence of contagion effect along with interdependency between the stock markets of Asian emerging economies like China, India, South Korea, Indonesia, Hong Kong, and Thailand that are caused due to COVID-19 pandemic. The study employs an adjusted correlation coefficient along with the DCC-MGARCH model to capture the contagion effect or interdependency between Shanghai Composite and other select stock markets. Mild enhancement of short-run volatility (DCCα1) is found to be significant under the DCC framework during COVID-19 period only, although long-run volatility is insignificant in both periods. Contagion effect can be slightly traced in BSE Sensex, KOSPI, and SET 100 while market co-movement remains same for Hang Seng and JKSE Composite and thus they are interdependent with Shanghai Composite. Interestingly, adjusted correlation coefficients between Shanghai Composite and other stock markets increased substantially during COVID-19 period indicating the presence of high contagion effect amongst the markets.

Does the Economic Policy Uncertainty in the USA and China Affect Indian Stock Markets Symmetrically and Asymmetrically? An Empirical Analysis of Pre- and Post-Coronavirus Disease 2019 Timelines

Globalization and economic interconnections have recently garnered a lot of attention, and scholars and policymakers are delving into the effects of policy uncertainty on macroeconomic fundamentals against this backdrop, the current study investigates the symmetric and asymmetric effects of economic policy uncertainty (EPU) in the USA and China on the Indian benchmark index. Furthermore, the study also analyses the variation in results due to the occurrence of uncertain events. For the empirical analysis, non-linear autoregressive distributed lag (NARDL) and autoregressive distributed lag (ARDL) techniques have been employed, and the findings substantiate an asymmetric relationship between the US EPU and the Indian stock market, while a symmetric relationship between the Chinese EPU and the Indian stock market. The study also reveals that in the short run, the Indian stock market is immune to the fluctuations in the Chinese stock market and policy uncertainty. Moreover, the outcomes further reveal that the US and Indian stock markets project a mirror performance. In addition, the study further reveals that, unlike the pre-Coronavirus Disease 2019 (COVID-19) timeline, during the COVID-19, the short-run volatility was high between the explanatory and outcome variables. The study is an original work and offers several useful recommendations.

Innovation of the Component GARCH Model: Simulation Evidence and Application on the Chinese Stock Market

The component GARCH model (CGARCH) was among the first attempts to split the conditional variance into a permanent and transitory component. With the application to economic and finance data, it helps investigate the long- and short-run movements of volatility affecting securities. Like all GARCH-type models, the innovation series of the CGARCH are usually assumed to follow a Normal distribution, which cannot accommodate fat-tailed properties commonly present in empirical data. The resulting estimates are not efficient when a Normal assumption is employed. In this paper, we consider the tempered stable distribution, which has the attractive stability under aggregation property missed in other popular fat-tailed distributions such as Student’s t-distribution and General Error Distribution (GED). Through systematically designed simulation studies, our results demonstrate that a CGARCH model with tempered stable distribution uniformly outperforms those with Normal, Student’s t-distribution and GED. Our empirical study on the Shanghai Stock Exchange index also leads to the same conclusions. Therefore, we argue that the CGARCH model with tempered stable distribution could be widely used to model economic and financial data in general contexts, focusing on both the long- and short-run volatility behaviours.

Investigating Short and Long Run Volatility Movements in the Context of COVID-19 Pandemic: A Case Study for Norwegian Stock Market

Investigating Short and Long Run Volatility Movements in the Context of COVID-19 Pandemic: A Case Study for Norwegian Stock Market

Risk premia in the term structure of crude oil futures: long-run and short-run volatility components

Risk premia in the term structure of crude oil futures: long-run and short-run volatility components

The Impact of Interest Rate Volatility on Stock Returns Volatility: Empirical Evidence from Pakistan Stock Exchange

Apprehension pertaining to Stock return volatility always has been producing the appreciable significance in the various current research works and it has been lucrative to many researchers for forecasting stock market volatility. This study is about the forecasting of stock returns volatility on the basis of interest rate volatility in the well established Pakistan Stock Exchange (PSX). The stock returns are calculated on the basis of KSE 100 index and interest rate volatility is calculated on the basis of monthly treasury bills rate during a period of 1994 to 2016. Various volatility models like Auto Regressive Conditional Heteroscedasticity (ARCH) and Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) were used to predict stock return volatility on the basis of interest rate volatility in Pakistan. ARCH model is one of the well known methods to forecast the error term in the data and which will certain our forecast regarding stock prices. In the Pakistan Stock Exchange the ARCH (1, 1) has been statistically significantly proved. The GARCH (1, 1) model is also used to estimate the stock volatility. This model shows the short run volatility affect the lagged stock returns and is contributing to the overall volatility. The sum of α and β is less than 1 so the short run volatility is positively related to the overall stock volatility. The GARCH (1, 1) model has outperformed the other volatility models in the case of Pakistan Stock Exchange.

Cryptocurrencies and Market Efficiency

The exponential growth and volatility of cryptocurrencies have led to a global interest in crypto assets and their distribution as digital wealth. Currently, cryptocurrencies and tokenised gold are the two popular digital wealth. While tokenised gold has the backing of un-mined physical gold, crypto has the support of an ideology-driven trust with the patronage of a mining process using supercomputing power that rewards coins. Thus, the acceptance of cryptocurrency as a reliable digital wealth depends to a large extent on its price consistency and the related independence. Many researchers tested the price efficiency of Bitcoin for different time periods up to 2017. In most cases, inefficiencies of the coins were inferred by them. Furthermore, many studies focused on the long-run and short-run volatility dynamics of Bitcoins leaving the altcoins almost unexamined. Therefore, a fresh look at the price efficiency and the inter-relationships of Bitcoin and the three altcoins, which altogether have a market capitalization of nearly 80 per cent, is attempted here. In all, six rigorous tests for independence or random walk are applied on the daily returns of the coins. Bitcoin returns showed a random walk in four out of the six tests, Ethereum and Ripple in three tests, and Bitcoin Cash only in two tests. The strong symptoms of weak form of efficiency found at least in the case of Bitcoin can be considered as its acceptance as a digital wealth. While the daily returns of all the four cryptos have a long-run equilibrium, the daily prices of Bitcoin, Ethereum and Bitcoin Cash excluding Ripple showed cointegration among themselves. Nearly 88 per cent of the short-run fluctuations in Bitcoin returns are adjusted on a daily basis. Bitcoin Granger Cause Ethereum and Ripple, a bidirectional Granger Causality is found between Ripple and Ethereum. Thus, the acceptance of cryptos, especially Bitcoin, as a digital wealth option can increase in the future.

Exchange rate risk and sectoral returns: A wavelet-based MRA-EDCC GARCH analysis

This study empirically investigates the spillover between exchange rate risk and sectoral returns in Pakistan over the period of 1992–2017 using high-frequency data. Building on the Wavelet-multi-resolution-extended dynamic conditional correlation GARCH (MRA-EDCC GARCH) model, our findings suggest that exchange rate risk transmission to sectoral stock returns is scale-dependent. The extent of mean and volatility spillover prevalence varies across different scales. Hence, homogenous trading strategies for short-term and long-term investors may not be favorable. The finding further reveals that the risk transmissions are notable over the short run. In the context of cross spillover, there is greater evidence of exchange rate volatility impact on sectoral returns, whereas, few sectoral returns are found to affect exchange rate. Our results are robust to alternative methods of causality and short-run volatility spillover through GARCH-in-mean SVAR model. Our findings provide useful implications for risk mitigation of portfolio managers and speculators.

Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach

We utilize a fundamentals-based component volatility model to forecast the short-run volatility of exchange rate changes using monetary fundamentals quoted at different frequencies. Specifically, we allow the component volatility model to distinguish short-run exchange rate fluctuations from long-run movements that are directly linked to monetary fundamentals. Relative to more traditional time series volatility models, we find significant improvements in the ability to forecast the daily volatility of exchange rate changes by incorporating the monthly monetary fundamentals’ volatilities as predictors into the component volatility model. In the utility-based comparisons, we find that an investor is willing to pay a positive annual management fee of 5.72% on average to switch from the benchmark model to the fundamentals-based models. Of these models, the model with the symmetric and homogeneous Taylor rule and interest rate smoothing obtains the highest positive annual management fee.

Volatility Prediction Using a Realized-Measure-Based Component Model

Abstract This article introduces a volatility model with a component structure allowing for a realized measure based on high-frequency data (e.g., realized variance) to drive the short-run volatility dynamics. In a joint model of the daily return and the realized measure, the conditional variance of the daily return has a multiplicative component structure: the first component traces long-run (secular) volatility trends, while the second component captures short-run (transitory) movements in volatility. Despite being a fixed-parameter model, its component structure implies time-varying parameters, which are “data-driven” to capture changing volatility regimes. We discuss the model dynamics and estimation by maximum likelihood. The empirical analysis reveals statistically significant out-of-sample gains compared to benchmark models, particularly for short forecast horizons and during the financial crisis.

Does Oil Prices Cause Financial Liquidity Crunch? Perspective from Geopolitical Risk

ABSTRACT This study measures whether oil prices affect financial liquidity (FL) in the Kingdom of Saudi Arabi (KSA). The results indicate a positive association between oil prices (OP) and FL in the medium run. FL led OP in the short run during the global financial recession, when the KSA used foreign reserves to stabilize the impact of low OP. Similarly, geopolitical risk (GR) led OP in the medium term and had a positive influence on FL in the short term, especially during periods of higher uncertainty. The correlation between OP and FL becomes more noticeable in the medium term in the presence of GR. Short-run volatility can exert pressure on foreign reserves, which can be effectively managed by keeping reserves in the national currency. Similarly, economic growth sources other than oil income and a peaceful solution to regional differences can reduce defense spending.

Risk Premia in the Term Structure of Crude Oil Futures: Long-Run and Short-Run Volatility Components

This paper studies how volatility affects the risk premium in crude oil futures through a discrete-time term structure model with long-run and short-run GARCH-type volatility components. Estimated using WTI crude oil futures data from January 1990 to July 2016, our model simultaneously matches futures prices and volatilities for the nearest twelve maturities. We document a significant positive relation between volatility and futures risk premia before May 2005, but a significant negative relation after that time period. The dynamic changes of the risk-return relationship concur with the structural changes, specifically the financialization, in the commodity markets. Risk premia decomposition indicates that the short-run volatility component represents the most important contribution to futures risk premia, both before and after the structural change.

U.S., European, Chinese economic policy uncertainty and Moroccan stock market volatility

We investigate the impact of economic policy uncertainty in the U.S., France, Spain, Germany, the U.K., Italy and China on the long run volatility in the Moroccan stock market. For this reason, we combine both daily returns and monthly uncertainty data in the GARCH-MIDAS model. We decompose total volatility into a long run persistent component and short run cyclical component. This study will contribute to stock market research and international economic policy uncertainty (EPU).Our findings show that, before the global financial crisis (GFC) of 2008, the link between the Moroccan stock market and the EPU is generally insignificant for the long run volatility component, namely for France and Spain. However, this effect was relatively significant with the U.S. and German economic policy uncertainty. This result means that the total variance was more explained by the short run volatility component generated by daily stock market returns. Nevertheless, during the post-crisis period we compare our results with benchmark GARCH models by using the AIC criterion and variance ratios values. They display that almost all GARCH-MIDAS models with economic policy uncertainty perform significantly in explaining the long run volatility in the Moroccan stock market, particularly, the EPU of France, Spain and the US.Our paper findings could be very helpful for policy makers and portfolio managers. Understanding the long-term relationship between the Moroccan stock market volatility and international economic policy uncertainty can be crucial in terms of adopting efficient decisions. They can also improve the efficiency of asset allocation for investors.

Long Run and Short Run Risk Premium in Currency Market

In this paper, I investigate risk premium of long run and short run volatility component of exchange rate returns in currency market. I find that high interest rate currencies of carry trade strategy load negatively on long run volatility innovation, while low interest rate currencies load positively. Risk price of long run volatility innovation is negative which implies that high carry trade returns are considered as compensation for time varying long run volatility risk. In contrast, risk price of short run volatility innovation is positive. Low interest currencies deliver low returns and high interest rate currencies yield high returns under times of high short run volatility. In terms of momentum strategy, risk price of short run volatility innovation is negative and statistically significant, while risk price of long run component is insignificant. Therefore, long run volatility does not provide any explanation for high returns of currency momentum strategy. High momentum returns, on the other hand are reward for investors to bear short run volatility risk.