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PurposeThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).Design/methodology/approachThe authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.FindingsAccording to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.Originality/valueThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.
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The COVID-19 pandemic has been causing unprecedented economic downturn worldwide. As it wreaks havoc on every aspect of global economic activities, stakeholders are wondering how its impact can be quantified to craft viable responses. In the exotic field of cryptocurrencies, prior to the pandemic, everyone was excited about Bitcoin and its multitude of potentials. However, a day after COVID-19 was officially announced by the World Health Organization as a pandemic, the rate of return to Bitcoin dropped by an unheard-of one-day decline of-46.5%, and people started to rethink the prospects of Bitcoin. A day after this steep decline, Bitcoin recovered and started a sustained bull run which lasted for almost a year and even posted an all-time high daily uptick of 59.6%. By the end of July 2021, the price reached its all-time high but lost more than half of it at the end of the sample period. This study aims to empirically analyze the risk-return profile and the market efficiency of Bitcoin utilizing a 1,306-day data set conveniently subdivided into pre-pandemic and pandemic periods. The general conclusion of the study is: During the pandemic, Bitcoin is extremely volatile and does not subscribe to the efficient market hypothesis. © 2023 by De La Salle University.
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The study empirically examines the impact of the COVID-19 on different sectoral indices of the National Stock Exchange (India) using the event study method and a generalised autoregressive conditional heteroskedasticity (GARCH) model. We provide evidence of positive impacts on the auto, oil and gas, healthcare, and pharma sectors. While the bank, financial services, and private bank sectors are the most adversely impacted sectors, the PSU bank, media, and reality sectors are the least impacted, and the rest are moderately impacted sectors. The overall impact of COVID-19 was negative until the implementation of nationwide lockdowns and the announcement of stimulus packages. The GARCH results exhibit more substantial evidence for the negative impact of the pandemic on the FMCG, IT, metal, oil and gas, and PSU bank sectors. We also find a more favourable impact on FMCG, pharma, and healthcare sectors in India.
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Although several studies on the integration of diverse stock markets have been conducted in the financial literature, most of them have focused on the integration and volatility spillovers across established stock markets. The present study explores the dynamics of integration and volatility spillover across gold, oil, forex, and stock markets during four significant events in India: the pre-changed government regime, the post-changed government regime, the post-Brexit referendum date, and the COVID era. Daily data from 2010 to 2022 is divided into four categories using the Chow test. This is done to examine if these events' financial turmoil affects market interconnectivity. The unit root test determines data stationarity. The ARCH LM test examines series volatility clustering, and the BEKK GARCH test examines market volatility spillover. Results indicate that gold cannot be considered a hedge or safe haven. Secondly, market interconnectedness increased during the crisis period. Third, domestic political and geopolitical conditions globally do not increase the scale of spillover amongst financial assets, though they impact the spillover's magnitude. The results of this study have several important implications for portfolio diversification and risk management. © Varsha Ingalhalli, Prachi Kolamker, 2023.
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This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.
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Inflation is a critical economic series, and proper targeting is required for a stable economy. With the current economic conditions that the world has faced as a result of COVID-19, understanding the effects of this on economies is critical because it will guide policies. Recent research on South African inflation has focused on statistical modelling, specifically the ARFIMA, GARCH, and GJR-GARCH models. In this study, we extend this into deep learning and use the MSE, RMSE, RSMPE, MAE, and MAPE to assess performance. To test which model has better forecasts, we use the Diebold-Mariano test. According to the findings of this study, clustered bootstrap LSTM models outperform the previously used ARFIMA-GARCH and ARFIMA-GJR-GARCH models.
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The unprecedented outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in extreme volatility in stock markets. This study mainly examines the predictive ability of the Internet concern about COVID-19 on stock index returns, based on the framework of GARCH type models. Instead of using the whole sample period, we divide the Internet concern about COVID-19 into high-concern and low-concern periods by breakpoint test method and then examine its predictive ability for stock returns in different periods, respectively. Using stock indexes of 10 countries and abnormal Google search volume of 'coronavirus' as study samples, the results reveal that (1) the Internet concern about COVID-19 has a negative impact on the stock index returns in the whole and high-concern periods, while its influence in the low-concern period is mixed;(2) the Internet concern about COVID-19 improves the prediction accuracy of stock index returns in the high-concern period, while seems to lose its powerful predictive ability in the whole and low-concern periods.
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Purpose: This study examined the financial contagion between crude oil and gold prices with the equity prices of different sectors in the Indian equity market during the recent COVID crisis. Design/Methodology/Approach: Dynamic conditional correlation (DCC) GARCH model was employed to analyze the behavior of time-varying conditional correlation during the time of COVID-19. For examining the financial contagion, regression analysis was performed on the dynamic conditional correlation and the conditional volatilities of the different markets. Findings: The DCC model showed a sharp increase in correlations between markets during the COVID-19 wave. It also suggested the presence of financial contagion between the crude oil and gold markets and the different equity sectors. It also indicated that the COVID-19 effect on the conditional correlation between gold and equity sectors was temporary. In contrast, it increased the correlation between crude oil and the equity sectors. Practical Implications: The findings of this study have implications for portfolio diversification methods because higher correlations lower the benefits of diversification. Originality: This study examined the financial contagion during COVID-19 from crude oil and gold to equity sectors. Not all sectors react in the same way to changes in the prices of these commodities, and some may witness less impact compared to others during the crisis period, which makes it interesting for the study. © 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved.
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The present study conducts a dynamic conditional cross-correlation and time–frequency correlation analyses between cryptocurrency and equity markets in both advanced and emerging economies. The purpose of the study is twofold. First, the study investigates the presence of the pure (narrow) form of financial contagion between cryptocurrency and stock markets in both advanced and emerging economies, during the black swan event of the COVID-19 crisis. Second, the study examines the hedging and safe-haven properties of cryptocurrencies against equity markets, before and during periods of financial upheaval triggered by the COVID-19 pandemic. Two econometric models are used: (1) the dynamic conditional correlation (DCC) GARCH and (2) the wavelet analysis models. Using the DCC GARCH model, the study found the evidence of high conditional correlations between cryptocurrency and equity markets. The high conditional correlation was mostly detected in periods of financial turmoil corresponding to the first quarter and the second quarter of 2020. The increase in conditional correlation during periods of financial upheaval (compared to a tranquil period) indicates the presence of the pure form of financial contagion. The wavelet cross-correlation analysis showed the evidence of positive cross-correlation between the Bitcoin and the equity markets during period of financial turmoil. The cross-correlation was identified in both short and long (coarse) scales. In short scales, the equity markets lead the cryptocurrency market, while the cryptocurrency market leads equity markets in coarse scales. The findings of the present study revealed that the degree of interdependence between cryptocurrency and equity markets has substantially increased during the COVID-19 period, and this has negated the safe-haven and hedging benefits of cryptocurrencies over equity markets. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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The purpose of the study was to evaluate the relationshi ps between factors and the variability of Asian Emerging Stock Markets for the time before, during, and following the COVID 19 Outbreak. Descriptive, ADF Test, GARCH (1.1) Model, and Pair-wise Granger Causality Test were used in the research. From the outcomes of empirical analysis, the study found that the information about the COVID 19 Pandemic played a major role in the movement of Asian emerging countries, stock markets. But the fear of a COVID 19 pandemi c exerci sed mi xed i mpact on t he count ry' s market performance. As a result, while investing in the stock markets, the i nvest or shoul d keep a keen wat ch on market movements. International stock market investors in particular, should watch numerous worldwide events, for a sound investment in the global stock markets. © Indian Institute of Finance.
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Using a bivariate dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (GARCH) model, this study compares the safe-haven properties of various assets against the major Gulf Cooperation Council (GCC) stock indexes during two periods of financial turmoil, the COVID-19 pandemic and the 2008 Global Financial Crisis (GFC). Sovereign bonds offered the highest hedging benefits under both crises. The traditional safe assets, gold and silver, which were reasonably productive under the GFC, have been less so during the pandemic. The Japanese yen emerged as a very safe choice for investors holding GCC stock indexes. Both sector indexes and stock indexes failed to safeguard investors most of the time during each crisis.
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Lumber is one of the most essential forest products in the United States. During the first year of the COVID-19 pandemic, lumber prices almost quadrupled, and fluctuations reached record levels. Although market experts have pointed to various drivers of such high price volatility, no firm conclusions have been drawn yet. Using the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) framework, this study assesses the potential drivers of lumber price volatility, with predictors including the Google Trends Web Search Index, housing starts, US lumber production quantity, and VIX index, representing public attention, housing demand, lumber supply, and macroeconomic concerns, respectively. We have found that housing demand is the key driver of lumber price volatility, followed by public attention. It is worth noting that US lumber supply and macroeconomic concerns have played a modest role in explaining lumber price volatility. Also, forecasting lumber price by using the housing demand variable substantially outperforms others. Market participants, including lumber mills, wholesalers, and home builders can get valuable information from the housing market to manage lumber price risk.Study Implications: The findings of this study can be used to improve hedging strategies, design option pricing formulas, and setting margin requirements. Critical information for price risk management on the lumber market can be gained by lumber market participants from the housing market. For forest management decisions by landowners, giving close attention to housing market would provide valuable information on the appropriate time for timber harvesting, because changes in the housing market affect lumber price that will indirectly affect the demand for timber, which is the most important factor of production for lumber mills.
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We revisit the "fear of missing out” (FoMO) effect of Bitcoin by observing asymmetric volatility dynamics and further investigate its driving factors. Using a longer sample period covering the COVID-19 pandemic, our results show evidence of positive asymmetric volatility behavior in the Bitcoin market, confirming the presence of the FoMO effect. This effect also exists in some other major cryptocurrencies. Further analysis indicates that the happiness index, the ratio of short-term to long-term Bitcoin trading volume, and the geopolitical risk index contribute positively to the FoMO, while the volatility index and the Twitter-based uncertainty index exert an opposite effect.
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In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.
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Reducing the unemployment gender gap is seen as an indicator of women's empowerment capacity for the equitable growth of the country's economy. At the regional level, Colombia exhibits one of the highest unemployment gaps, despite the efforts made to close them. The objective of this study is to model the evolution of the unemployment gender gap in Colombia during the period 2001:01 to 2021:06, to forecast its behavior, and determine its volatility. For this purpose, a Seasonal Autoregressive Integrated Moving Averages (SARIMA) and Generalized AutoRegressive Conditional Heteroskedasticity model (GARCH) were fitted. The results indicate that, although the gender gap had been slightly declining in the last two decades, it was adversely affected by the Covid-19 pandemic, causing the gap to increase again. On the other hand, there is an increase in the volatility of the series, making it more vulnerable to economic and seasonal cycles. Finally, it is forecast that the gap will tend to decrease in the following months, however, it will increase again in December due to the seasonal component.
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The results of the research presented in the article regard the importance of publication of macroeconomic data from the United States for the short-term USD/PLN currency pair exchange rate volatility. The main purpose of the research was to indicate what macroeconomic data is important for the short-term USD/PLN exchange rate volatility. The following research questions have been posed does the USD/PLN exchange rate react to the published macroeconomic data from the American economy and second could greater USD/PLN exchange rate volatility be observed during the COVID pandemic and has the war in Ukraine impacted the USD/PLN exchange rate volatility. International Foreign Exchange Market is the largest and most dynamically developing financial market in the world. In the globalized world the exchange rates are mainly influenced by economic factors. The most significant economic factors that impact short-term exchange rate volatility are primarily macroeconomic data from the American economy. Therefore in this article the author attempts to analyze macroeconomic data and their impact on short-term USD/PLN exchange rate volatility. Data based on which the research was made is as follows: Consumer Price Index, Non-Farm Payrolls (NFP), Services PMI, Manufacturing PMI, Empire State Manufacturing Index or Retail Sales. The analysis of connections between the publication of macroeconomic data and the reaction of exchange rates was carried out using the linear regression model with GARCH process for the random parameter. Conclusions of this research is exchange rate volatility USD/PLN was higher after publications of the macroeconomic data from Americans economy. The strongest exchange rate reaction was after publication of data regarding inflation, Manufacturing PMI and Retail Sales. In the COVID (1.03.20-14.02.22) period we observed increased USD/PLN exchange rate volatility. Exchange rate volatility was expressly larger in the period of war in Ukraine (15.02.22 - end of experiment).
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The COVID-19 virus, which was detected for the first time in Wuhan, China in December 2019, spread to all countries of the world and, therefore, became a global epidemic. Although more than two years have passed since the outbreak of the COVID-19 pandemic, the economic effects of it continue. One of these is the effect of the pandemic on precious metal prices. Precious metals, which are called safe harbours and used as investment tools, have had a serious volatility in the last century as a result of the economic, political and pandemic factors changing the international balances. From this point of view, in this study, it is aimed to determine the appropriate forecasting model to predict the volatility of gold, silver, platinum and palladium prices, which are called precious metals, during the COVID-19 pandemic period. The econometric analysis covers the period between March 11, 2020, when the global epidemic was declared by the World Health Organization, and September 13, 2021, and includes 326 days of observation. To determine the appropriate forecasting model, ARCH, GARCH, T-GARCH, E-GARCH and PARCH are used as symmetrical and asymmetrical volatility models.
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Forecasting stock markets is an important challenge due to leptokurtic distributions with heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast the direction of stock markets, the inclusion of leading indicators to volatility models is highly important;however, such series are generally at different frequencies. The paper proposes the GARCH-MIDAS-LSTM model, a hybrid method that benefits from LSTM deep neural networks for forecast accuracy, and the GARCH-MIDAS model for the integration of effects of low-frequency variables in high-frequency stock market volatility modeling. The models are being tested for a forecast sample including the COVID-19 shut-down after the first official case period and the economic reopening period in in Borsa Istanbul stock market in Türkiye. For this sample, significant uncertainty existed regarding future economic expectations, and the period provided an interesting laboratory to test the forecast effectiveness of the proposed LSTM augmented model in addition to GARCH-MIDAS models, which included geopolitical risk, future economic expectations, trends, and cycle industrial production indices as low-frequency variables. The evidence suggests that stock market volatility is most effectively modeled with geopolitical risk, followed by industrial production, and a relatively lower performance is achieved by future economic expectations. These findings imply that increases in geopolitical risk enhance stock market volatility further, and that industrial production and future economic expectations work in the opposite direction. Most importantly, the forecast results suggest suitability of both the GARCH-MIDAS and GARCH-MIDAS-LSTM models, and with good forecasting capabilities. However, a comparison shows significant root mean squared error reduction with the novel GARCH-MIDAS-LSTM model over GARCH-MIDAS models. Percentage decline in root mean squared errors for forecasts are between 39% to 95% in LSTM augmented models depending on the type of economic indicator used. The proposed approach offers a key tool for investors and policymakers.
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The rapid growth of BRICS has increasingly integrated their markets into the global economy. Thus, making their financial markets more vulnerable to external shocks. This study examines BRICS stock markets' response to global economic policy uncertainty using a panel GARCH model. The results show that global economic policy uncertainty significantly raises volatility with homogeneous response across the markets. The findings also suggests that COVID-19 has amplified the adverse impact of the uncertainties on prices and volatility. One major implication of the findings is that the BRICS can develop a joint policy for mitigating policy uncertainties spillovers. © 2023 Elsevier Inc.
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The COVID-19 pandemic brings significant effects to the global stock market, including Indonesia. This study investigates the behavior and fluctuation of Jakarta Composite Index (JKSE) before the COVID-19 pandemic arises (2018–2019) and 2 years during the COVID-19 pandemic (2020–2021) and its alignment with the government policy in the energy sector. This study will use the JKSE data before and during the Covid-19 pandemic. The study showed that before COVID-19 pandemic, the JKSE was in normal conditions and showed an increasing trend. However, the study found anomalies in the JKSE volatility when COVID-19 pandemic was officially announced in Indonesia during 1st quarter 2020. This study is able to find the forecasted next 30 days best models that can describe the pattern of JKSE data are AR (2)–GARCH (1,1) models for the closing price of JKSE data before the COVID-19 pandemic and AR (5)–GARCH (1,1) models for the closing price of JKSE data during the COVID-19 pandemic. With the government economic recovery program related to the energy sector, this study was able to forecast the next 30 days for the closing price of JKSE during COVID-19, which showed the improvement of JKSE into the small increasing trend. These findings are expected to increase public investor trust, especially foreign investors investing their money in the JKSE. The positive trend in JKSE will ensure the government continues its economic policy recovery plan. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.