US Stock Market Volatility Research Articles

Can gold- and USD-backed stablecoins hedge against US stock market volatility? Evidence from a quantile coherency approach

ABSTRACT Emerging markets are increasingly appealing to market participants seeking alternative investments. Using the Quantile Coherency method, this study investigates the interrelatedness between stablecoins backed by two types of traditional assets (gold and US dollars) and the volatility indices of the US stock market across different frequency domains and market conditions. The potential of gold- and USD-backed stablecoins to hedge stock market volatility varies. There is a stronger connection under long-term and extreme conditions than in the short-/medium- and normal markets. The empirical results suggest that the USD-backed stablecoins should be used as hedging assets against US stock market volatility (typically showing a non-negative correlation) rather than the gold-backed stablecoins (which often exhibit a negative correlation). These findings provide valuable insights for market participants and academic research.

Co‐Jump Dependency and Transmission Across US Commodity Futures: A Network Analysis

ABSTRACTThis paper examines the co‐jump transmission in 20 commodity futures returns in the United States using co‐jump network models. Specifically, it reveals co‐jumping behavior in both static and time‐varying settings, considering the overall commodity markets and various commodity groups separately, which helps us understand the dynamic changes in co‐jump dependencies at the overall and sector levels. The main results reveal that co‐jump heterogeneity exists among commodities but is generally more apparent within each commodity group, and co‐jumps vary over time. Gold exerts the strongest influence, with many commodity futures being influenced by the jumps behavior in gold returns. During the COVID‐19 outbreak and the Russia–Ukraine war, the energy group ranks highest in terms of co‐jump network centrality. Our empirical analysis highlights the portfolio performance and risk reduction, and an additional examination shows that centrality information from the co‐jump network contains a highly and statistically forecasting power for US stock market volatility.

Behavioral finance impacts on US stock market volatility: an analysis of market anomalies

Abstract This study investigates the impacts of behavioral finance on stock market volatility. The primary aims are to explain the reasons behind changes in the S&P 500 price within the context of behavioral finance and to analyze investor behavior in response to these changes. To achieve this, the research employs time-series analysis over a 10-year period, focusing on the S&P 500, real interest rates, consumer confidence, market volatility and credit default swaps while considering the effects of behavioral biases. The findings reveal several significant correlations: rising real interest rates negatively affect stocks due to loss aversion and sentiment. Conversely, higher consumer confidence tends to positively influence the stock market, driven by herding behavior and optimism. Additionally, market volatility shows a negative correlation with the S&P 500, influenced by risk aversion, recency bias and herding behavior. Moreover, an increase in credit default swap rates leads to stock market declines, primarily influenced by risk perception, loss aversion and herding behavior.

Dynamic spillovers in higher moments and jumps across ETFs and economic and financial uncertainty factors in the context of successive shocks

Hedging is a particularly important tool in the Exchange Traded Fund (ETF) markets where market makers seek the best ways to mitigate the uncertainty of their exposures. This study relies on high frequency data to assess the spillover effects among ten US sector ETFs and various economic and financial uncertainty indexes based on realized volatility, realized higher moments as well as jumps under a relatively new spillover framework. Next, a time-varying parameter vector autoregression (TVP-VAR) model is used to examine the dynamic connectedness among ETFs and uncertainty factors volatilities while avoiding the sensitivity of spillover results to the choice of the rolling window. Our results showcase higher total connectedness between the different uncertainty indexes and ETFs, though with varying sensitivities. Notably, skewness and kurtosis can spread from one market to another, especially during times of market turbulence, reflecting the significant spillovers in higher-order moments. Interestingly, the market’s 30-day forward looking expectations of US stock market volatility (VIX) has stronger effect on the US sector equity ETFs than the expected 30-day volatility of returns on oil and gold. This analysis emphasizes the implications and contributions of assessing the spillover in higher-order moments covering volatility, skewness, and kurtosis to portfolio hedging and financial risk management. Overall, the results are of considerable practical interest for economic and market agents who are keen to understand market integration and systemic risk propagation to infer asymmetric or fat tail risk related to extreme or downside/upside risks.

Forecasting US Stock Market Volatility: Evidence from ESG and CPU indices

This study investigates the predictability of U.S. stock market volatility using environmental, social, and governance (ESG) and climate policy uncertainty (CPU) indices based on the Markov-regime GARCH-MIDAS model. Our out-of-sample results show that ESG and CPU information is useful to forecast the U.S. stock market volatility. Notably, the ESG index proved to be a more powerful predictor for volatility estimation. Lastly, we ensure the reliability of the study's results through various robustness checks, including the model confidence set (MCS) method. These results suggest that investors and policymakers should consider carefully the impact of ESG and CPU risk on financial decision making and policy realms.

Role of oil shocks in US stock market volatility: A new insight from GARCH-MIDAS perspective

For both local and foreign investors, the equity market and oil price shocks have massive repercussions. In this research, we examine the essential role of the oil shock in predicting the U.S. stock market volatility. The oil shock measures include NPI (net price increase), ANP (asymmetric net prices change), SNP (symmetric net price change), LPI (large price increase), and NPI2 (net price increase) indicators. We select the GARCH-MIDAS model to estimate the volatility. The study provides several notable outcomes. First, in all five oil shocks, symmetric net price change (SNP) information is more useful for forecasting the volatility of the U.S. stock market. Further, we find some evidence for the net price increase (NPI) and asymmetric net price change information (ANP) in some estimation windows. Also, the Covid-19 epidemic provides proof. Our results are robust in the alternative valuation methods, MCS (model confidence set) test, and alternative estimation windows.

Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic

We propose a new clustering approach for comparing financial time series and employ it to study how the COVID-19 pandemic affected the U.S. stock market. Essentially, we compute the forecast accuracy of asymmetric GARCH models applied to S&P500 industries and use the model forecast errors for different horizons and cut-off points to calculate a distance matrix for the stock indices. Hierarchical clustering algorithms are used to assign the set of industries into clusters. We found homogeneous clusters of industries in terms of the impact of COVID-19 on US stock market volatility. The industries most affected by the pandemic and with less accurate stock market prediction (Hotels, Airline, Apparel, Accessories & Luxury Goods, and Automobile) are separated in Euclidean distance from those industries that were less impacted by COVID-19 and which had more accurate forecasting (Pharmaceuticals, Internet & Direct Marketing Retail, Data Processing, and Movies & Entertainment).

Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models

This paper investigates how geopolitical risks influence the prediction performance on the US stock market volatility with machine learning models. Further, it compares the predictive performance of individual and combination forecast methods. With SHAP algorithm, it could identify which factor has a great impact and fully extract the information of geopolitical risks in predicting. Empirical results show that military build-ups and escalation of war have great importance on predicting realized volatility among various geopolitical risks. The research further emphasizes the superior performance of machine learning and forecast combination methods, especially SVR method and trimmed mean combination. In addition, by allocating portfolio according to the machine learning-based volatility forecasts, particularly elastic net and random forest, a mean-variance investor can achieve sizeable financial benefits. Our paper provides substantial implications for political risk management and volatility forecasting.

Analysing Volatility Dynamics of India and US Stock Market

Abstract: Analysing the risk-return trade-off of financial assets, volatility is a key factor. Volatility promotes market liquidity and demonstrates the effectiveness of the financial system. To have an insights of India And US Market Volatility behaviour, this paper examines the volatility dynamics of India And US Stock market by taking Sensex And S & P 500 daily closing prices for the 2nd January 2003 to 31st December 2019. The authors have two questions to examine about volatility whether India and US stock market volatility shows volatility clustering and volatility persistence or not. ARCH LM test and Ljung Box Q2 statistics were used to check for volatility clustering. And for volatility persistence GARCH (1,1) model is used. The results of the study confirm the presence of volatility clustering in in both India And US stock market. Additionally, Indian stock return volatility shows lower volatility persistence than US. The results of the study are quite useful for the academicians, policy makers and investor community in general

Asia-Pacific stock market return and volatility in the uncertain world: Evidence from the nonlinear autoregressive distributed lag approach.

This paper examines the effects of three distinct groups of uncertainties on market return and volatility in the Asia-Pacific countries, including (i) the country-specific and US geopolitical risks; (ii) the US economic policy uncertainty; and (iii) the US stock market volatility (using the VIX and SKEW indices). Our sample includes 11 Asia-Pacific countries for the 1985-2022 period. We employ the nonlinear autoregressive distributed lag approach (ARDL) estimation technique to capture the asymmetric effects of uncertainties on market return and volatility, which are documented in the literature. Some findings are documented as follows. First, we find that US uncertainty indices, including US geopolitical risk, US economic policy uncertainty, and US VIX, significantly impact Asia-Pacific stock markets, while the impacts of domestic geopolitical risk and the US skewness index (SKEW) are relatively weak. Second, Asia-Pacific stock markets tend to overreact to uncertainty shocks stemming from US economic policy uncertainty and US geopolitical risk. Third, US economic policy uncertainty has more significant effects than the US geopolitical risk. Finally, our research documents that Asia-Pacific stock markets react heterogeneously to good and bad news from US VIX. Specifically, an increase in US VIX (bad news) has a stronger impact than a decrease in US VIX (good news). Policy implications have emerged based on the findings of this study.

Market Reactions to COVID-19: Does Systemic Risk Vary Across Industries? A Markov-Switching CAPM Approach

ABSTRACT Despite a broad consensus on the response of US stock market volatility to the coronavirus outbreak, our micro-level understanding of its variation across industries still needs to be improved. This study contributes to the existing literature by providing an industry-level analysis of the COVID-19 pandemic with two different states. Evidence from the MS-CAPM model indicates the role of portfolio diversification. Specifically, the results reveal that some industries, such as materials, real estate, communication, and utilities, have much higher expected returns. On the other hand, other sectors, including consumer discretionary, industrials, and information technology, become less volatile than the market during the lockdown period.

Stock market volatility predictability in a data-rich world: A new insight

This study develops a shrinkage method, LASSO with a Markov regime-switching model (MRS-LASSO), to predict US stock market volatility. A set of 17 well-known macroeconomic and financial factors are used. The out-of-sample results reveal that the MRS-LASSO model yields statistically and economically significant volatility predictions. We further investigate the predictability of MRS-LASSO with respect to different market conditions, business cycles, and variable selection. Three factors (equity market returns, a short-term reversal factor, and a consumer sentiment index) are the most frequent predictors. To investigate the practical implications, we construct the expected variance risk premium (VRP) by using volatility forecasts generated from the LASSO and MRS-LASSO models to forecast future stock returns and find that those models are also powerful.

A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

AbstractThis study is the first to examine the impacts of overnight and intraday oil futures cross‐market information on predicting the US stock market volatility the high‐frequency data. In‐sample estimations present that high overnight oil futures RV can lead to high RV of the S&P 500. Moreover, negative overnight returns are more powerful than positive components, implying the existence of the leverage effect. From statistical and economic perspectives, out‐of‐sample results indicate that the decompositions of overnight oil futures and intraday RVs, based on signed intraday returns, can significantly increase the models' predictive ability. Finally, when considering the US stock market overnight effect, the decompositions are still useful to predict volatility, especially during high US stock market fluctuations and high and low EPU states.

Volatility of the US stock market and business strategy during COVID‐19

AbstractThis paper aims to measure the impact of COVID‐19 and changes in economic policy uncertainty (EPU) on the US stock market volatility using Event Study, Stepwise Regression, and Vector Autoregression (VAR) models. The impact has been examined under three contexts namely inside the US, outside the US and the whole world. This study finds negative cumulative abnormal returns of the market in response to the announcement of new cases and death records whereas the market reacts positively to the announcement of the financial bailout. The negative and significant impact of reported deaths and cases on the volatility of stock indices indicates that the US stock market is highly sensitive to the COVID‐19. EPU has a significant and positive impact on the volatility of the US stock market. The COVID‐19, EPU, and volatility variables are cointegrated and move in a unidirectional way. This study considers the presence of a long‐run relationship among the variables and concludes that the US stock market is more exposed to COVID‐19 compared with the rest of the world.

The impact of expectations on the co-integration relationship between the stock and REIT markets

This paper examines if expectations have a significant impact on the co-integration relationship between stock and real estate investment trust markets. We use two widely followed expectation indicators which are the US yield spread and the expected US stock market volatility (VIX) to test this hypothesis. The US yield spread is decomposed into two components which are the expected short-term interest rate (EF) and a variable term premium (TP) using Hamilton-Kim algorithm. A dataset covering ten developed markets is used. Using co-integration score analysis our findings indicate that expected US short-term interest rates and expected US stock market volatility have a statistically significant and positive impact on the global co-integrations of different countries. This effect is especially valid in the post-global financial crisis period. The expectation-based indicators EF and VIX, however, do not seem to have a significant impact on co-integration at regional and local levels.

Bitcoin, gold, and the VIX: short- and long-term effects of economic policy uncertainty

ABSTRACT We investigate short- and long-term effects of U.S. economic policy uncertainty (EPU) on bitcoin, gold, and the implied US stock market volatility (VIX). We apply an autoregressive distributed lag model (ARDL) to monthly data. Our results suggest EPU significantly negatively (positively) impacts bitcoin over short (long) horizons. In contrast to the extant literature we find the magnitude of the effect of EPU on bitcoin returns weakens over longer horizons. Our empirical results provide a cautionary note for holders of asset portfolios that include bitcoin in their mix as a hedge against uncertainty.

Forecasting stock market volatility using commodity futures volatility information

By incorporating volatility information from nineteen commodity futures prices, this paper compares the predictive ability of traditional individual AR-type and combination forecasting models versus model shrinkage methods in predicting US stock market volatility. Our empirical results show that the Lasso shrinkage method has significantly better out-of-sample forecasting performance in not only the individual models but also the combination approaches. In particular, the Lasso model with all predictors exhibits the best out-of-sample forecasting performance, suggesting that incorporating all commodity futures volatility information by the model shrinkage approach is an effective way for market participants and policy-makers to obtain accurate forecasts of US stock market volatility. Further analysis shows that the predictability evidence is substantially clearer during high volatility periods than in low volatility regimes. Finally, alternative evaluation periods further confirm the robustness of our results.

Can both Bitcoin and gold serve as safe-haven assets? — A comparative analysis based on the NARDL model

This paper uses the nonlinear autoregressive distributed lag (NARDL) model to analyze the different performances of Bitcoin and gold under the impacts of three different uncertainties, namely global economic policy uncertainty (GEPU), US stock market volatility index (VIX) and the CBOE crude oil ETF volatility index (OVX). The results indicate that faced with shocks of different uncertainties, Bitcoin is unable to serve as a safe-haven, while gold can hedge against uncertainties to varying degrees. Moreover, the three types of uncertainties have asymmetric impacts on the prices of Bitcoin and gold respectively. The decrease of uncertainties has a greater impact on Bitcoin price than the increase, while the increase of uncertainties has a greater impact on gold price than the decrease. It suggests that investors are cautious and optimistic about Bitcoin, and gold remains unanimously recognized as the traditional safe-haven.

To keep faith with homoskedasticity or to go back to heteroskedasticity? The case of FATANG stocks.

Did the pattern of US stock market volatility change due to COVID-19 or have the US stock markets been less volatile despite the pandemic shock? And as for tech stocks, are they even less volatile than the market overall? In this paper, we provide evidence in favor of a “quietness” in the stock markets, interrupted by COVID-19, by analyzing dispersion, skewness and kurtosis characteristics of the empirical distribution of nine returns series that include individual FATANG stocks (FAANG: Facebook, Amazon, Apple, Netflix and Google; plus Tesla) and US indices (S&P 500, DJIA and NASDAQ). In comparison with the years before, the daily average return after COVID-19 was 6.48, 2.58 and 2.34 times higher for Tesla, Apple and NASDAQ, respectively. In terms of volatility, the increase was more pronounced in the three stock indices when compared to the individual FATANG stocks. This paper also puts forward a new methodology based on semi-variance and semi-kurtosis. While the value of the ratio between semi-kurtosis and kurtosis is always higher than 70% for the three US stock indices, in the case of stocks the opposite is true, which highlights the importance of large positive returns when compared to negative ones. Structural breaks and conditional heteroskedasticity are also analyzed by considering the traditional symmetrical and asymmetrical GARCH models. We show that in the most recent past, despite the COVID-19 pandemic, the FATANG tech stocks are characterized mostly by conditional homoskedasticity, while the returns of US stock indices are characterized mainly by conditional heteroskedasticity.

US banks in the time of COVID-19: fresh insights from the wavelet approach

This study explores the impact of COVID-19, crude oil price, US economic policy uncertainty, baltic dry index, and the stock market volatility on the US bank indices. This study is conducted based on the daily data ranging from 21st January 2020 to 30th October 2020. The wavelet coherence analysis suggests that rising COVID-19 cases in the US have a strong impact on both bank indices. Also, global COVID-19 cases influence the bank indices, although it is not as strong as US COVID-19 cases. Additionally, we have found that the US economic policy uncertainty and stock market volatility imposed negative and strong effect on the bank indices in this pandemic situation. Moreover, continuous fluctuation of crude oil price makes the US banks volatile throughout the period.