Return Volatility Research Articles

Equity return volatility in Africa’s stock markets: A dynamic panel approach

Equity return volatility in Africa’s stock markets: A dynamic panel approach

A Study of the Abnormal Dividend Decisions of New Zealand Firms during COVID-19

We investigated the stock return risk associated with the various types of dividend decisions announced by New Zealand firms during the COVID-19 pandemic in 2020. The sample includes a group of firms that initially announced cash dividends but a number of days later made announcements cancelling their payments. Using multinomial logistic regression analysis, we found that higher pre-pandemic payout policy significantly increased the likelihood of a cancellation, an omission or an increase decision. Higher growth and higher profitability reduced the probability of an omission and a reduction decision, respectively. Moreover, higher stock return volatility increased the likelihood of an omission, a reduction or an increase decision. Further event study analysis revealed that investors reacted more feverishly to the announcements of cancellation decisions than any other types of dividend decisions. Moreover, we report strong evidence of negative abnormal returns around the cancellation announcements followed by positive post-announcement price reversals, a pattern that is not observed for the omission announcements. This paper contributes to the literature by studying a cancellation sample and reveals, for the first time, significant shareholder risk associated with cancellation decisions, which was not observed for omission decisions. We alert managers to carefully weigh the costs and benefits of breaking a promise of dividend payout.

The spouse effect and CEO risk‐taking

AbstractWe examine how a CEO's family life affects their corporate decisions. Specifically, we investigate whether a CEO's spouse's professional status affects the CEO's risk‐taking behavior. Using a sample of S&P 500 firms from the 2010 to 2012 period, we find evidence that CEOs with spouses who are professionals, defined as working spouses, spouses holding graduate degrees, or spouses graduated from Ivy League schools, tend to adopt riskier corporate policies. Our evidence suggests that firms led by CEOs with professional spouses exhibit higher accounting return volatility, make more aggressive financial reporting decisions, and invest more in risky assets. We do not find a significant association between professional spouses and firms’ market return volatility. Our results are consistent with the indirect channel theory, which suggests that CEOs with professional spouses might be inclined to undertake higher risks, potentially stemming from heightened conflicts between family and work commitments or improved wealth diversification attributed to their spouses' professional standing.

A Study on KOSPI Return Volatility Estimation Using Heterscedastic Time Series Model

In this study, return volatility prediction model was estimated by converting composite stock index into return variables. As for the heteroscedasticity test for the return, the Q-test and the LM-test showed that heteroscedasticity existed at the 2nd and 7th lags. Therefore, the order was determined using the SBC statistic, the parameters were estimated using the ARCH (2) model, and the model fit test was conducted. The parameter estimates of the ARCH(2) model were statistically significant, but the residual analysis showed that autocorrelation existed and did not satisfy the normality test. In the results of applying the GARCH (1,1) model, the parameter estimates, residual analysis, and normality were found to be satisfactory. Therefore, the GARCH (1,1) model was determined as the KOSPI return volatility estimation model and volatility was predicted. Volatility was predicted to show high volatility in the period of January and February 2023, and to stay calm after March for a long-term. Continuing a calm state means that the probability of large volatility is high. Therefore, those in charge of government agencies need to check system improvement or policy establishment and make efforts to identify trends in volatility by market.

Text-Based Measure of Supply Chain Risk Exposure

Using textual analysis techniques, including seeded word embedding and bag-of-words-based content analysis, I develop a firm-level measure of supply chain risk exposure from a novel source of unstructured data—the discussion between managers and equity analysts on supply chain-related topics during firms’ quarterly earnings conference calls. I validate the measure by showing that (1) the measure exhibits intuitive variations over time and across firms, successfully capturing both routine and systematic supply chain risk events; and (2) the measure is about risk exposure, as it significantly correlates with realized and options-implied stock return volatility, even after controlling for well-known aggregate risk measures. I then demonstrate that the measure is specifically indicative of the supply chain component of risk exposure. (3) Consistent with theoretical predictions, firms facing higher supply chain risks have higher inventory buffers, particularly in raw materials and intermediate inputs, increased cash holdings in lieu of investments, and significantly lower trade credit received from suppliers. Moreover, (4) during unexpected risk episodes, such as the Tohoku earthquake, firms with higher ex ante risk exposure have worse operating and financial performance. These results indicate that the text-based measure provides a credible quantification of firm-level exposure to supply chain risks and can thus be reliably utilized as outcome or explanatory variables in empirical supply chain research. This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4927 .

Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models

The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.

The Impact of Investors Overconfidence Bias on Investment Strategy Based on Behavioral Finance

As international financial markets continue to diversify and become more complex, the application of behavioral finance in financial markets is becoming more and more widespread. At the same time, as people's living standards continue to rise, the securities market is a relatively optimal investment choice. To protect the wealth of investors and improve the standard of living of the nation, it is essential to ensure the stability of the investment market, which forces us to study the impact of imperfectly rational behavior on the stock market. Overconfidence bias is one of the most common types of imperfectly rational psychology. Therefore, it is of great significance to study the impact of overconfidence on investment portfolios. This paper aims to examine the effect of overconfidence bias on an investor's investment strategy through a literature review and empirical methods. The paper concludes that overconfidence bias shows a different relationship with return yield in the initial, completion, and diminishing periods. Secondly, overconfidence bias for investors exists. The characteristics of better-than-average-effect (BTAE) and the illusion of control have different effects on trading volume, return volatility, and market efficiency, and the role of contrarian investment strategy (CIS) in overcoming this bias is discussed.

Application of ARIMA Model in Portfolio Optimization

This paper investigates the portfolio allocation strategy using the ARIMA time series model with eight companies in four industries: Technology, Healthcare, Financial Services, and New Energy. This research aims to identify the best portfolio allocation approach that maximizes the returns for investors in each industry. This paper collected daily stock prices of Apple and Microsoft in the technology industry, Johnson & Johnson and Pfizer in the healthcare industry, JPMorgan Chase and Goldman Sachs in the financial services industry, and Tesla and NextEra Energy in the new energy industry from January 2016 to December 2019. The logarithmic returns first were calculated for the first three years as the training data. Then, an appropriate ARIMA time series model would be fitted to forecast the stock return for the last year and perform the portfolio optimization based on the predicted return for 2019 following a result of weights for each asset. By comparison, the paper also applied portfolio optimization to the raw logarithmic daily return from 2016 to 2018 without the time series model fitted and got another set of weights for each asset. Finally, both cumulative returns for these two portfolios computed and compared with visualizations. There is a finding that the modified strategy of portfolio with ARIMA model forecasted data could provide a more obvious trade-off relationship between volatility and sharpe-ratio for returns than the traditional one performed with the raw data. The result of this paper provides a potential scope of an application of portfolio optimization worked with time series modeling for investors to make investment decisions that possibly yield a higher return.

Did Feds Rate Policy Benefit U.S. Company: Evidence from Tesla

This paper studies the impact of the Federal Reserve's interest rate policy on American companies, taking Tesla as evidence. Data on the Tesla stock as well as the USD/RMB exchange rate for the previous year were gathered. The impact of a rising exchange rate on companies goes in two directions. On the one hand, the appreciation of the dollar means the depreciation of foreign operating income, and the depreciation of the domestic currency is bad for exports. On the other hand, it will affect how the stock market operates. An increase in dollar holdings in international financial markets would increase the demand for stocks, which would increase stock prices. The conclusion is that the Fed's interest rate policy to control inflation in the United States has, to some extent, come at the expense of stock market growth. The VAR model and ARMA-GARCH model were used to model and analyze the data, and forecast the trend of the company's stock price, then make recommendations for policymakers and investors. For policymakers, it is necessary to adjust the policy appropriately to minimize the volatility of stock market returns, and investors should turn to assets with lower risk instead of blindly investing.

Effects of carbon emissions on audit fees

PurposeThis paper aims to examine the effect of carbon emissions on audit fees. The authors hypothesize that firms in cities with higher carbon emission levels have lower reporting transparency, higher return volatility or are subject to higher reputation risk, causing them to be charged higher audit fees for auditing services.Design/methodology/approachThe authors use panel data of 25,960 firm-year observations from a sample of Chinese firms. The carbon emission data for each Chinese city are obtained from the China Emission Accounts and Datasets for Emerging Economies. This paper adopts a multiple regression model to study the impact of carbon emissions on audit fees.FindingsThe authors find that firms located in cities with higher carbon emission levels and firms with more carbon emissions are charged, on average, a higher audit fee. This audit fee effect of carbon risk is transmitted by lessened information transparency and elevated financial risk within these firms. This paper shows that auditors consider carbon risk in their audit fee decisions and other factors that could influence audit risk and effort.Originality/valueThis study draws a connection between carbon emissions and audit fees. It is especially relevant due to the increasing importance of environmental factors in the audit risk assessment. In addition, the findings suggest that a firm implementing a proactive environmental strategy benefits the economy and decreases the costs to the firm for services such as auditing.

Potential diversification benefits: A comparative study of Islamic and conventional stock market indexes

The study aims to contribute to the better understanding of potential diversification benefits for US and its major trading partners, namely Canada, Japan, and the UK, across both Islamic and conventional indexes. We applied a Dynamic Conditional Correlations (DCC) model to examine the dynamic correlation and volatility of returns during 2001–2017. The findings put in evidence that, unlike Canadian peers, Japanese indexes exhibit a low dynamic correlation with US indexes, which ultimately suggests diversification opportunities for US-based investors. Understanding volatilities and correlations patterns through international markets would help investors, policymakers, and market participants to take informed decisions, mitigate risks, and anticipate potential spillover effects.

Forecasting Trading-Session Return Volatility in Taiwan Futures Market: A Periodic Regime Switching with Jump Approach

Forecasting Trading-Session Return Volatility in Taiwan Futures Market: A Periodic Regime Switching with Jump Approach

Spatial GARCH models for unknown spatial locations – an application to financial stock returns

ABSTRACT Spatial GARCH models, like all other spatial econometric models, require the definition of a suitable weight matrix. This matrix implies a certain structure for spatial interactions. GARCH-type models are often applied to financial data because the conditional variance, which can be translated as financial risks, is easy to interpret. However, when it comes to instantaneous/spatial interactions, the proximity between observations has to be determined. Thus, we introduce an estimation procedure for spatial GARCH models under unknown locations employing the proximity in a covariate space. We use one-year stock returns of companies listed in the Dow Jones Global Titans 50 index as an empirical illustration. Financial stability is most relevant for determining similar firms concerning stock return volatility.

Be nice to the air: Severe haze pollution and mutual fund risk

Motivated by the significant impacts of environmental risks on economic decisions and the increasing roles of mutual funds in financial markets in recent decades, this study examines the impact of ambient pollution on mutual funds' risk outcomes. Our fund fixed-effect regression estimates use manually collected propriety data from several datasets, showing that polluted air increases tracking errors and mutual fund return volatility. Adopting different identification strategies, including instrumental variable estimations and difference-in-difference analyses based on two natural experiments, suggests that the impact of air pollution on mutual funds' risk is causal. Our findings suggest that air pollution harms fund managers' cognitive abilities and impairs their investment efficiency, thereby increasing mutual funds' tracking errors and return volatility. Overall, our findings provide insights into the impact of climate change on social behavior by shedding new light on the impact of air quality on asset managers' behavior.

The impact of retail investor sentiment on the conditional volatility of stocks and bonds: Evidence from the Tel-Aviv stock exchange

We measured bond and stock conditional return volatility as a function of changes in sentiment, proxied by six indicators of the Tel-Aviv Stock Exchange. We found that changes in sentiment affect conditional volatilities at different magnitudes and often in an opposite manner in the two markets, subject to market states. We are the first to measure the bond conditional return volatility of retail investor sentiment, thanks to a unique dataset from a limit-order book with highly active retail traders. This market structure differs from the prevalent over-the-counter platforms, where institutional investors are active, yet less prone to sentiment.

Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX

Research Question: The launch and the beginning of trade on the South East Electric Power Exchange (SEEPEX) in Belgrade, early in 2016, opened the issue of forecasting volatility and price movements in the market. Motivation: The issue is of vital importance for all market actors for the purpose of maximising profits, reducing risks, planning production and making investment decisions. Forecasting volatility and price movements in electric power markets is important for traders with profit maximisation and yield-to-risk ratio optimisation in mind and, equally, for producers, large industrial consumers, investors and portfolio managers. Idea: Exploring models and techniques to forecast volatility in electricity markets and subsequently testing statistical methods based on time series data, the ARMA-GARCH being the preferred model, with a view to identifying optimal methods for this market. The volatility of the power market and price movements have been tested during a given period. The results can be used to gauge market parameters and opportunities to extrapolate future volatility and movements in electricity prices. Data: For the purposes of this analysis, a time series involving price movements and trade volumes were used, covering a period between the SEEPEX trade launch and the end of 2019. Tools: In the empirical part of the paper, "Stata 13" statistical and econometric software was used to explore stylised facts and model the volatility of SEEPEX electricity price returns. Findings: The authors offer an overview of different methods used in the research, having selected different specifications of the ARMA-GARCH model as the most reliable in predicting volatility in the given market. The exponential GARCH model with student-t error distribution is believed to have provided the best overall performance in modelling the SEEPEX return volatility, as well as the best volatility forecast. Contribution: This is one of the first empirical studies of the Serbian power market that deals with risk modelling. Forecasting time-varying electricity exchange volatility is important for all market participants interested in variance forecasts to be used to calculate risk and hedging measures.

Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models

Abstract The presence of volatility in residential property market prices helps investors generate substantial profit while also causing fear among investors since high volatility implies a high return with a high risk. In a financial time series, volatility refers to the degree to which the residential property market price increases or decreases during a particular period. The present study aims to forecast the volatility returns of real residential property prices (RRPP) in Malaysia using three different families of generalized autoregressive conditional heteroskedasticity (GARCH) models. The study compared the standard GARCH, EGARCH, and GJR-GARCH models to determine which model offers a better volatility forecasting ability. The results revealed that the GJR-GARCH (1,1) model is the most suitable to forecast the volatility of the Malaysian RRPP index based on the goodness-of-fit metric. Finally, the volatility forecast using the rolling window shows that the volatility of the quarterly index decreased in the third quarter (Q3) of 2021 and stabilized at the beginning of the first quarter (Q1) of 2023. Therefore, the best time to start investing in the purchase of real residential property in Malaysia would be the first quarter of 2023. The findings of this study can help Malaysian policymakers, developers, and investors understand the high and low volatility periods in the prices of residential properties to make better investment decisions.

Interconnections assessment of banking capitalization with macroeconomic stability, including corruption and shadow economy

The research focuses on bank capitalization and macroeconomic stability, including corruption and the shadow economy. A well-capitalized banking system and a low corruption level are important for maintaining macroeconomic stability and reducing the size of the shadow economy. The paper is aimed at assessing the relationship between bank capitalization and macroeconomic stability, which includes corruption and shadow economy through canonical analysis. The research is conducted on the basis of financial and economic reporting of 35 countries with different levels of socio-economic development during 2010–2021 based on data from the World Bank and the European Central Bank. The main input blocks macroeconomic stability – corruption – shadow economy are characterized. During the research, the following tools were used: – methods of grouping, analysis and comparison in determining the characteristics of the elements of the chain «level of capitalization of the banking system – macroeconomic stability – corruption – shadow economy»; – factor analysis by applying the method of principal components in the selection of statistically significant indicators of the bank capitalization level and macroeconomic stability. The quantitative assessment of the bank capitalization level revealed the following dependencies: there is a strong correlation between return on assets and the level of non-performing loans, return on assets and return on equity. Using the principal components method, the following statistically significant indicators of macroeconomic stability were identified: GDP, Gini index, corruption perception index, corruption control index, and political stability index. Research, based on the canonical analysis, determined that 71.1% of changes in macroeconomic stability indicators are explained by fluctuations in the level of bank capitalization. The level of non-performing loans has a negative impact on macroeconomic stability, while the volatility of return on assets and return on equity has a greater positive impact on the development of the country's economy. The obtained results can be used by banks in the development of their resource and management policies, in the analysis of the volatility of capitalization level, by state bodies in the development of national policies of the country's economic development.

An analysis of dependency of stock markets after unlimited QE announcements during COVID-19 pandemic

PurposeUnlimited quantitative easing (QE) is one of the monetary policies used to stimulate the economy during the coronavirus disease 2019 (COVID-19) pandemic. This policy has affected the financial markets worldwide. This empirical research aims at studying the dependence among stock markets before and after unlimited QE announcements.Design/methodology/approachThe copula-based GARCH (1,1) and minimum spanning tree models are used in this study to analyze 14 series of stock market data, on 6 ASEAN and 8 other countries outside the region. The data are divided into two periods to compare the differences in dependence.FindingsThe findings show changes in dependence among the volatility of daily returns in 14 stock markets during each period. After the unlimited QE announcement, the upper tail dependence became more apparent, while the role of the lower tail dependence was reduced. The minimum spanning tree can show the close relationships between stock markets, indicating changes in the connection network after the announcement.Originality/valueThis study allows the dependency to be compared between stock market volatility before and after the announcement of unlimited QE during the COVID-19 pandemic. Moreover, the study fills the literature gap by combining the copula-based GARCH and the minimum spanning tree models to analyze and reveal the systemic network of the relationships.

The effect of Hungry Ghost Festival (HGF) on stock market returns and volatility: An empirical analysis of Asian stock markets

This study examines the effect of the Hungry Ghost Festival (HGF) on stock returns and volatility in five Asian stock indices. Shanghai demonstrated the highest average return and dispersion, while Singapore had the lowest. During the HGF, all countries’ kurtosis spiked, except for Philippines and Shanghai, signalling a high-risk investment with high returns. Finally, the current volatility of daily stock returns persists across countries and is influenced by previous shocks. Investment during the HGF may appear riskier and more volatile in some countries, but there is inconclusive evidence to conclude that the HGF had a significant effect on all markets.