US Stock Indices Research Articles

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.

Practical Applications of An Inconvenient Fact:Private Equity Returns and the Billionaire Factory

In <b>An Inconvenient Fact: <i>Private Equity Returns and the Billionaire Factory</i></b>, from the December 2020 issue of The <b><i>Journal of Investing</i></b>, author <b>Ludovic Phalippou</b> (of the <b>Saïd Business School</b> at the <b>University of Oxford</b>) challenges the perception that private equity (PE) provides institutional investors with higher returns that justify its higher fees. PE is not available to the general public—and since PE investments are not continuously traded, there are no publicly available measures of PE rates of return. Therefore, PE firms use internal rate of return (IRR) as their primary advertised performance measure. However, a more appropriate measure of PE performance is based on a net present value (NPV) calculation, such as the public market equivalent (PME). Using this performance measure, Phalippou shows that US PE has produced about the same net-of-fees returns as the most common US stock indexes since 2006. Meanwhile, a small number of PE managers have become billionaires by receiving incentive fees based on absolute performance, not performance relative to any benchmark. The author questions who really benefits from PE investing, explains why pension funds and universities continue to embrace it, and offers suggestions to make PE investing more sustainable. <b>TOPICS:</b>Private equity, statistical methods, performance measurement

An Inconvenient Fact:Private Equity Returns and the Billionaire Factory

In <b>An Inconvenient Fact: <i>Private Equity Returns and the Billionaire Factory</i></b>, from the December 2020 issue of The <b><i>Journal of Investing</i></b>, author <b>Ludovic Phalippou</b> (of the <b>Saïd Business School</b> at the <b>University of Oxford</b>) challenges the perception that private equity (PE) provides institutional investors with higher returns that justify its higher fees. PE is not available to the general public—and since PE investments are not continuously traded, there are no publicly available measures of PE rates of return. Therefore, PE firms use internal rate of return (IRR) as their primary advertised performance measure. However, a more appropriate measure of PE performance is based on a net present value (NPV) calculation, such as the public market equivalent (PME). Using this performance measure, Phalippou shows that US PE has produced about the same net-of-fees returns as the most common US stock indexes since 2006. Meanwhile, a small number of PE managers have become billionaires by receiving incentive fees based on absolute performance, not performance relative to any benchmark. The author questions who really benefits from PE investing, explains why pension funds and universities continue to embrace it, and offers suggestions to make PE investing more sustainable. <b>TOPICS:</b>Private equity, statistical methods, performance measurement

Empirical Evidence of Dynamic Conditional Correlation Between Asian Stock Markets and US Stock Indexes During COVID-19 Pandemic

This study aims to explore the dynamic conditional correlation (DCC) between ten Asian stock indexes, the US stock index, and Bitcoin by using the dynamic conditional correlation model. The time span of the daily data is between January 2015 to May 2021, the total observation is 1,116. DCC(1,1)-EGARCH(1,1) with multivariate t and normal distributions for the DCC and EGARCH models, respectively, outperforms other models by the goodness of fit values. Except for Bitcoin, we discovered that the majority of the securities' volatilities have a very high volatility persistence. Furthermore, the negative shocks/news have more impact on the volatilities than positive shocks/news in most of the cases, except the stock index of China and Bitcoin. Most of the correlation pairs exhibit higher correlation during the COVID-19 pandemic compared to the pre-COVID-19, except Hong Kong-The US and Malaysia-Indonesia. Moreover, the correlation between Asian stock indexes during the COVID-19 pandemic is statistically higher than the pre-COVID-19 pandemic. However, there are a few instances where the Hong Kong stock index and a few countries are identical. The result of correlation size shows the connectedness between Asian stock markets, which are well-connected within the region, especially with South Korea, Singapore, and Hong Kong.

Zmiana poziomu efektywności amerykańskiego rynku akcji – od kryzysu finansowego 2007-2009 do pandemii COVID-19

Purpose – Verification of the change in the efficiency of the US stock market from the 2007-2009 financial crisis to the COVID-19 pandemic. Research method – The BDS test (occurrence of non-linear relationships between daily logarithmic rates of return) and the Quenouille autocorrelation test up to the fifth order (occurrence of linear relationships between daily logarithmic rates of return) for the S&amp;P500 and DJIA indices were used. Results – The occurrence of unusual phenomena changes the level of US stock market effectiveness. An increase in inefficiency was observed both during the 2007-2009 financial crisis and now, during the COVID-19 pandemic. Higher-level relationships between rates of return were found during the COVID-19 pandemic. Originality / value / implications / recommendations – A comparison of the impact of two recent global events on the level of efficiency of the US stock market. Additionally, the article indicates which of the two key US stock indices provided more frequent investment opportunities.

The uncertainty in extreme risk forecasts from covariate-augmented volatility models

For a GARCH-type volatility model with covariates, we derive asymptotically valid forecast intervals for risk measures, such as the Value-at-Risk or Expected Shortfall. To forecast these, we use estimators from extreme value theory. In the volatility model, we allow for leverage effects and the inclusion of exogenous variables, e.g., volatility indices or high-frequency volatility measures. In simulations, we find coverage of the forecast intervals to be adequate for sufficiently extreme risk levels and sufficiently large samples, which is consistent with theory. Finally, we investigate if covariate information from volatility indices or high-frequency data improves risk forecasts for major US stock indices. While—in our framework—volatility indices appear to be helpful in this regard, intra-day data are not.

Impact of Early COVID-19 Pandemic on the US and European Stock Markets and Volatility Forecasting

This study examines the impact of COVID-19 on US and European stock indexes, implied volatility (IV) indices, and proposes forecasting accuracy of IV indices form daily data of March 2005 to May 2020, having an out-of-sample assessment of COVID-19. The empirical findings of Canonical Correlation Analysis (CCA) contribute significant insight into COVID-19 over the US and European stock indexes. This result shows that the death and recovery cases of COVID-19 have a significant positive impact of VIX, VXD, and VXN; however, the S&P 500, DJIA, and NASDAQ 100 show a negative association. Again, we also observe the significant impact of COVID-19 in stock trading prices and volatility expectations. The evidence of the point forecasts is more reliable for European IV indices than the US IV indices. Finally, this study validates IV indices' informational efficiency in the financial markets and suggests investors about portfolio management and investment risk minimization for similar future pandemic situations.

Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&amp;P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

The convergence of ethical investment business models and their reliance on the conventional US investment market

ABSTRACT This study investigates the extent of convergence of the two key types of ethical investment business models: Islamic Finance (IF) investment and Socially Responsible Investment (SRI) and the level to which these ethical business models are disconnected from the conventional investment market. We undertake an empirical assessment using three key US stock indexes: the Dow Jones Industrial Average, the Dow Jones Islamic Market, and the Dow Jones Sustainable Index. We find that IF and SRI indexes show significant similarities, suggesting that the ethical investment models share common moral and societal values and react similarly to market forces indicating convergence. We also find that the ethical investment models appear to be closely dependent on the conventional US stock market, suggesting inefficiency in the ethical investment model and, therefore, a dominant effect of conventional finance market on ethical investments. These findings are relevant as they allow us to better clarify the perceived convergence/divergence of values in IF and SRIs and to measure the degree of efficiency and credibility of ethical investment models to determine whether they are capable of successfully managing investments, in consideration of social, environmental, and economic factors, with fair, sustainable, and responsible outcomes.

COVID-19 Effects on the US Stock Index Returns: An Event Study Approach

This paper aims to measure the impact of COVID-19 pandemic on the US stock market.It applies Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Vector Autoregressive (VAR) and Event study Method (ESM) models. The ESM follows three different timelines, such as pre-event, event-day, post-event windows. Data comes from the major US stock indices. We find that the US stock market reacts negatively toward confirmed and death cases due to COVID-19, while death cases have a significant impact on stock market volatility. This study bridges the research gap and adds significant insights to the existing literature.

Are clean energy stocks efficient? Asymmetric multifractal scaling behaviour

We examine the multifractal scaling behaviour and weak form market efficiency of clean energy stock indices using an asymmetric MF-DFA. We find asymmetric multifractality in the US, European, and global clean energy stock indices. Asymmetric multifractality in the clean energy stock index of the US is due to fat-tails and long-range correlation. However, for European and global clean stocks, multifractality is due only to fat-tailed distribution. We find higher efficiency in the upward trend of the European and global clean stock markets whereas, for the US, the market is less efficient when the market is upward trending. The time-varying market deficiency measure shows that US clean energy stocks are becoming relatively more efficient over time.

Everybody likes shopping, including the US capital market

In this study, we investigate whether Black Fridays are associated with abnormal returns. We find that significant abnormal returns are evident in the main Canadian and US stock indices, as well as in several economic sectors. The Black Friday effect also seems to be size-related, as portfolio indices comprised of small stocks yield abnormally positive returns. The results remain persistent regardless of the sub-periods analyzed or the statistical methods and portfolios used. In explaining this anomaly, we consider rational as well as irrational explanations from psychology, decision-making, and marketing. Finally, investors can use simple strategies that take advantage of the apparent market inefficiencies documented in this study.

Characterization of time series through information quantifiers

Abstract The paper proposes to apply the K-Fisher (KF) index and the complexity-entropy curves based on k-entropy into studying the time series. The KF index is a good extension of the traditional Shannon-Fisher (SF) index. In the complexity-entropy curves, the results show that the normalized k-entropy and its corresponding complexity has the same extreme value k = 0.6 , interestingly, the KF index is the smallest when k = 0.6 , which suggests that the time series with k = 0.6 might be the most stable and verifies that the KF index is an effective tool to measure the stability of sequence. Furthermore, we conclude that this new complexity-entropy curves and the KF index could clearly distinguish financial stock indices and find that the stability of US stock indices are higher than the Australian and Chinese stock indices.

Does financial deepening matter in financial crisis? An empirical investigation of developed and emerging economies

This study explores the impact of financial deepening on financial crises in United State of America and Pakistan. The study uses the annual data on financial crisis and financial deepening ranging from 1997 to 2016 in order to encapsulate financial crisis in emerging and developed economies. Study applies Patel and Sarkar (1998) approach to identify and assess accurate form of financial crises. The findings suggest a positive relationship between financial deepening and financial crises in the case of USA, however, no such relationship could be established in case of Pakistan. Keywords: Financial deepening, financial crises, Pakistani stock index, US stock index.

Do higher order moments of return distribution provide better decisions in minimum-variance hedging? Evidence from US stock index futures

Using daily S&amp;P 500 spot index and index futures data, this article examines the effects of conditional skewness and kurtosis parameters of a skew-Student density function on dynamic minimum-variance hedging strategies. We find an important role for autoregressive marginal skewness and joint kurtosis in risk management. While static higher order moments improve reductions in variance of hedged portfolios over the case of normality, the inclusion of an autoregressive component significantly extends these improvements. This occurs in both tranquil and tumultuous periods. Furthermore, when transaction costs are considered, taking into account variations of higher order moments retains the best performance. JEL Classification: G11, G13

Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?

Although VIX has long been recognized as an index to measure fear sentiment in US stock markets, a set of similar measurements called Equity Market Volatility (EMV) trackers are newly created based on the text-counts of newspaper articles including several keywords related to US economy or stock market volatility. In this paper, we use GARCH-MIDAS method to quantify the in-sample explanatory and out-of-sample predictive powers of these two kinds of fear indices in US stock markets. Our empirical results show that VIX has larger in-sample impacts on US stock market volatility than EMV trackers. However, the out-of-sample volatility predictive performances of EMV trackers are generally superior to VIX across different US stock indices and prediction time horizons. In addition, policy-related EMV tracker acts better than VIX and other EMV trackers in predicting volatilities of US stock markets.

On the interplay between US sectoral CDS, stock and VIX indices: Fresh insights from wavelet approaches

In this study, we examine the time and frequency connectedness between CDS-stock and CDS-VIX pairs as well as combinations of the three indices for eleven US sectors. The main novelty of the present study is the use of bivariate and multivariate wavelet approaches to explore credit risk hedging and arbitrage opportunities. The findings point to a significantly changing pattern in the dynamic linkage between the given assets in the time–frequency domain. A strong negative (positive) association is unveiled in the long-term horizon for CDS-stock (CDS-VIX) pairs. Three markets seem disconnected in the utilities and industrial sectors and highly connected in the cyclical sectors such as basic materials. The implications of the findings are discussed.

Signal analysis of global financial crises using Fourier series

In this paper, we analyze the annual signals generated by the US stock market, using the returns of US monthly stock-indices. To this end, we transform the signals into a Fourier series and deduce financial information from the amplitude and phase of each component of the Fourier series. We show that, as global financial crises approach, low-frequency components increase more sharply than high frequency components. We also show that they react selectively to specific financial crises, under four major modes, and generate sharp peaks, showing their own characteristics of monthly propagation. By comparing these results with the same type of analyses for the UK and German stock markets, we demonstrate that this method works well for detecting global financial crises. Further, we apply this method to recent US stock-indices, and find that remarkable signals are being generated, suggesting that the current status is unstable. In other words, a financial crisis may be approaching.

Expected shortfall and portfolio management in contagious markets

Abstract We study the impact of market contagion on portfolio management. To model possible recurrence in the arrival of extreme events, we equip classic Poisson jumps with long memory via past-weighted randomization of the likelihood of their occurrences (Hawkes processes). Within this framework, we tackle the problem of optimal portfolio selection in terms of Expected Shortfall (ES). We use the generalized method of moments to estimate the model on three US stock indexes, representing three major sectors of the US economy. The moment conditions of the model are computed efficiently in closed form applying a novel technique. Given parameter estimates we maximize (at a monthly frequency in the period 2001–2016) the expected return subject to a constraint on the ES of a portfolio consisting of the three US sector indexes. We find that the weights of the optimal portfolio are significantly adjusted when the level of contagion is high. Finally, we perform an extensive out-of-sample back-test of the model’s ability to measure ES and find that the Hawkes jump-diffusion model outperforms two traditional models that are commonly implemented.

Extreme Market Value Declines: How Well Do Rating Agency Assumptions Hold?

S&amp;P Global Ratings has been rating leveraged closed-end funds regulated in the United States under the Investment Company Act of 1940 for more than 30 years. The principal source of repayment for these market value securities is the proceeds from liquidating the underlying pledged collateral. In its rating process, S&amp;P applies haircuts to the value of the pledged securities, discounting their value to cover the potential market value decline during the liquidation period. But how appropriate are these haircut assumptions? This article studies the extreme price changes in two major US stock indexes, the Dow Jones Industrial Average and the S&amp;P 500 Index, using extreme value methods. The analysis compares extreme market value declines to the assumptions S&amp;P uses in its market value securities ratings criteria. Moreover, the analysis estimates the likelihood of observing declines larger than those in the criteria (exceedance probabilities) and the average waiting time to observation of declines of various extreme magnitudes (return periods). From the analysis, we find that the likelihood of exceeding the assumptions in the rating methodology is generally in line with S&amp;P’s rating definitions.