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We present evidence that several popular low-frequency measures of effective spread suffer from a volatility-induced bias and that volatility is the primary driver of the variation of these liquidity proxies. Using data for U.S. equities and major foreign exchange rates, we show that the bias arises when the effective spread is small relative to volatility. We document that the bias has become more acute over time and show that volatility-biased measures fail to replicate some well-known results in empirical finance. We conclude by providing guidance on the choice of low-frequency measures in empirical applications. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

AbstractWe derive a new fractional Heston model with self-exciting jumps. We study volatility persistence and demonstrate that the quadratic variation necessarily exhibits less memory than the integrated variance, which preserves the degree of long-memory of the instantaneous volatility. Focusing on realized volatility measures, we find that traditional long-memory estimators are dramatically downward biased, in particular for low-frequency intraday sampling. Conveniently, our Monte Carlo experiments reveal that some noise-robust local Whittle-type estimators offer good finite sample properties. We apply our theoretical results in a risk forecasting study and show that our frequency-domain forecasting procedure outperforms the traditional benchmark models. [ABSTRACT FROM AUTHOR]

The technology sector serves as a fundamental pillar for all industry sectors in the economy, playing a central role in driving worldwide economic growth. It particularly distinguishes itself in the U.S., representing approximately one-third of the global technology market. However, the technology sector is not a homogenous group of similarly focused companies, its industries vary significantly in their characteristics. This paper analyzes the relationships among U.S. technology industries defined by the Global Industry Classification Standard using Vine copulas and transfer entropy. It spans 1 January 2010–31 January 2024, to clarify intra-sectoral heterogeneity and identify key industries predicting the performance of others. Our findings highlight the predictive power of the semiconductor industry, with significant information transmission to all other industries. Contrarily, the flow in the opposite direction is significant in only 3 of 5 cases. The identification of semiconductors’ central role is particularly valuable for both retail and institutional investors concentrating on technology stocks, as it may indicate future developments in the entire sector. Moreover, due to the industry’s global importance, shocks in the semiconductor industry could propagate across the entire technology sector, leading to broader economic instability. Therefore, the semiconductor industry should be closely monitored by the policymakers too. [ABSTRACT FROM AUTHOR]

The purpose of this research is to investigate the use of cross-hedging in the South African soybean market, by using the JSE BEAN contract as a cross-hedge instrument for the JSE SOYA contract. The research study involves a detailed assessment of the data, which includes the use of stationarity tests such as the Phillips-Perron and Augmented Dickey-Fuller, as well as the Johansen co-integration approach to analyse long-term relationships between the variables. To evaluate the efficiency of cross-hedging strategies, three distinct hedging models are used: the ordinary least squares (OLS) model, error correction model (ECM) and EC-GARCH. The results demonstrate that these models produce significant hedge ratios. Through back-testing and the application of hedging strategies, it is determined that the JSE BEAN contract holds the potential to serve as a cross-hedge for the JSE SOYA contract in the South African soybean market. This study provides useful information for market participants and model selection in the context of cross-hedging. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to improving forecasts of stock market indexes by considering common stock prices as hierarchical time series, combining clustering with forecast reconciliation. We propose grouping the individual stock price series in various ways including via metadata and using unsupervised learning techniques. The proposed approach is applied to the Dow Jones Industrial Average Index and the Standard & Poor 500 Index and their component stocks, and the results obtained with different grouping approaches are compared. The results empirically demonstrate that the combined use of clustering and reconciliation improves the forecast accuracy of the stock market indexes and their constituents. [ABSTRACT FROM AUTHOR]

In this article, to model risk contagion between the U.S. and China stock markets based on high-frequency financial data, we develop a novel continuous-time jump-diffusion process. For example, we consider three channels for volatility contagion—such as integrated volatility, positive jump variation, and negative jump variation—and each stock market is able to affect the other stock market as an overnight risk factor. We develop a quasi-maximum likelihood estimator for model parameters and establish its asymptotic properties. Furthermore, to identify contagion channels and test the existence of a structural break with a known structural break date, we propose hypothesis test procedures. Using the proposed diffusion model with high-frequency financial data, we investigate the effect of the U.S.–China trade war on stock markets from a financial contagion perspective. From the empirical study, we find evidence of financial contagion from the United States to China and evidence that the risk contagion channel has changed from integrated volatility to negative jump variation. [ABSTRACT FROM AUTHOR]

We propose a four-state regime-switching model that pairs low-volatility and high-volatility (HV) states to test eight stock–safe-haven asset portfolios' risk properties. We find the correlations between gold, U.S. T-bond, and the Swiss franc and stock markets are negative or zero in all states, including the HV–HV state, while the correlations between Bitcoin (BTC) and stock markets are positive in the HV–HV state, implying that gold, T-bond, and the Swiss franc are full safe-havens and BTC is a partial safe-haven asset. Moreover, our model is effective in portfolio construction, performing better than conventional time-varying generalized autoregressive conditional heteroskedasticity-based models. [ABSTRACT FROM AUTHOR]

The automotive industry is widely considered to be crucial for the economy, as it reflects economic development in general. Despite interest in financial performance, few studies have considered paying attention to the ownership structure among stockholders. Hence, the study aims to find out how the degree of ownership concentration, measured through the independence indicator of the Bureau van Dijk, is reflected in the financial management of companies in the automotive industry among selected European countries. The generalized method of moments (GMM) technique is widely used while investigating panel data with a short estimating period, i.e. nine years annually in this case. However, this study reveals that, without deploying techniques, subsequently introduced a modified version of GMM estimators with panel data by providing an implementation using Stata statistical software. Otherwise, these particular econometric tools to analyze a dynamic panel can often give false significant estimates. Overall, liquidity seems to be significant in the case of firms with less concentrated ownership, whereas companies with a major owner are affected more by selected macroeconomic variables.

There is a consensus that stock markets are procyclical. However, answers to some important questions remain unclear. Do stock markets lead or lag business cycles? More interestingly, what is the duration with which they lead or lag them? This study uses different time-series filters and time-difference analysis to answer these questions by examining the dynamic interactions between three major stock indices and key macroeconomic indicators in the United States. The findings show that stock markets have been strongly procyclical, lagging industrial production by one to three months in recent decades. There have been noteworthy changes in the relationship between inflation and stock market cycles. The correlations changed from negative in the 1980s and 1990s to positive in the 2000s and 2010s. The results also reveal close associations between the stock indices, offering new insights into the interplay between financial markets and economic cycles. [ABSTRACT FROM AUTHOR]

This study introduces the dynamic Gerber model (DGC) and evaluates its performance in the prediction of Value at Risk (VaR) and Expected Shortfall (ES) compared to alternative parametric, non-parametric and semi-parametric methods for estimating the covariance matrix of returns. Based on ES backtests, the DGC method produces, overall, accurate ES forecasts. Furthermore, we use the Model Confidence Set procedure to identify the superior set of models (SSM). For all the portfolios and VaR/ES confidence levels we consider, the DGC is found to belong to the SSM. [ABSTRACT FROM AUTHOR]

This study examined contagion involving the aggregate and regional housing markets of the United States (US) with other asset markets using multichannel tests during the 2007–2008 global financial crisis based on a unique high-frequency, i.e., daily data set. To arrive at bias free results several contagion tests: the Forbes and Rigobon (FR) correlation test for contagion, the Fry, Martin and Tang coskewness (CS) test for contagion, the Hsiao cokurtosis (CK) test for contagion and the Hsiao covolatility (CV) test for contagion were employed. At the country level, the linear (correlation) channel indicates that contagion is present from (to) average housing returns to (from) the S&P500, with the correlation contagion also running from average housing returns to REITs. Moreover, the coskewness, cokurtosis and covolatility channels are strongly active with contagion running only from average housing returns to the S&P500, bond returns and REITs. At the Metropolitan Statistical Area (MSA) level, our results indicate that the linear (correlation) channel of contagion is relatively inactive, but the coskewness, cokurtosis and covolatility channels are strongly active with contagion running mostly from housing returns to the S&P500. Our results have important implications for investor and policymakers, given the possibility of differential results based on tests and whether we rely on regional or aggregate data. [ABSTRACT FROM AUTHOR]

We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms' return, volatility, and trade volume dynamics. To this end, we apply causal machine learning on the earnings announcements of a wide cross-section of U.S. companies. This approach allows us to investigate firms' price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic, financial, and aggregated investors' moods in a properly defined causal framework. Our empirical results support the presence of (i) economically sizable differences in the effects among sentiment types that are mostly of a non-linear nature depending on the underlying economic and financial conditions; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors' underreaction to news. In particular, we show that the difference in the average causal effects of the sentiment's types is larger and more relevant when the general macroeconomic conditions are worse, the investors are pessimist about the behavior of the market and/or its uncertainty is higher, and in market regimes characterized by high stocks' liquidity. [ABSTRACT FROM AUTHOR]

In this article, we use a deep quantile estimator, based on neural networks and their universal approximation property to examine a non-linear association between the conditional quantiles of a dependent variable and predictors. This methodology is versatile and allows both the use of different penalty functions, as well as high dimensional covariates. We present a Monte Carlo exercise where we examine the finite sample properties of the deep quantile estimator and show that it delivers good finite sample performance. We use the deep quantile estimator to forecast value-at-risk and find significant gains over linear quantile regression alternatives and other models, which are supported by various testing schemes. Further, we consider also an alternative architecture that allows the use of mixed frequency data in neural networks. This article also contributes to the interpretability of neural network output by making comparisons between the commonly used Shapley Additive Explanation values and an alternative method based on partial derivatives. [ABSTRACT FROM AUTHOR]

Graphical models are a powerful tool to estimate a high-dimensional inverse covariance (precision) matrix, which has been applied for a portfolio allocation problem. The assumption made by these models is a sparsity of the precision matrix. However, when stock returns are driven by common factors, such assumption does not hold. We address this limitation and develop a framework, Factor Graphical Lasso (FGL), which integrates graphical models with the factor structure in the context of portfolio allocation by decomposing a precision matrix into low-rank and sparse components. Our theoretical results and simulations show that FGL consistently estimates the portfolio weights and risk exposure and also that FGL is robust to heavy-tailed distributions which makes our method suitable for financial applications. FGL-based portfolios are shown to exhibit superior performance over several prominent competitors including equal-weighted and index portfolios in the empirical application for the S&P500 constituents. [ABSTRACT FROM AUTHOR]

We examine the clustering behavior of price and variance jumps using high-frequency data, modeled as a marked Hawkes process (MHP) embedded in a bivariate jump-diffusion model with intraday periodic effects. We find that the jumps of both individual stocks and a broad index exhibit self-exciting behavior. The three dimensions of the model, namely positive price jumps, negative price jumps, and variance jumps, impact one another in an asymmetric fashion. We estimate model parameters using Bayesian inference by Markov Chain Monte Carlo, and find that the inclusion of the jump parameters improves the fit of the model. When we quantify the jump intensity and study the characteristics of jump clusters, we find that in high-frequency settings, jump clustering can last between 2.5 and 6 hours on average. We also find that the MHP generally outperforms other models in terms of reproducing two cluster-related characteristics found in the actual data. [ABSTRACT FROM AUTHOR]

This work examines the time-varying interlinkages among economic confidence, energy prices, geopolitical stress, and short/long-term interest rates in the Euro Area. Our research meticulously explores the interplay between economic confidence and various determinants, including financial indicators, geopolitical stress incidents, and energy prices. Employing innovative approaches such as the time-varying parameter vector autoregression (TVP-VAR) time and frequency-domain connectedness, we uncover the nuanced relationships between economic confidence, financial indicators, and energy prices. We illuminate the systemic nature of shock transmission in the Euro Area, identifying key net transmitters and recipients of shocks, with short-term interconnectedness emerging as a dominant feature, especially during pivotal events such as the global financial crisis, the COVID-19 pandemic, and geopolitical conflicts. Our empirical findings can be summarized as follows: First, both the time and frequency-domain connectedness indexes correctly associate with major financial/geopolitical events. Second, BCI and CCI respond to the GFC asymmetrically. Third, Brent and short/long-term interest rates are the net transmitters of shocks on average. Fourth, there is a considerable augmentation in return spillovers during the period characterized by the pandemic crisis compared to the GFC. Finally, our findings for frequency-dependent connectedness networks indicate that the market is particularly susceptible to short-term shocks. This study has significant ramifications for investors, market players, and policymakers.