A Multi-market Comparison of the Intraday Lead-Lag Relations Among Stock Index-Based Spot, Futures and Options.

Using 1-min data, we explore the dynamic variation of the intraday lead-lag relations between stock indices and their derivatives through a comprehensive study with broader coverage of research objectives and methodologies. This paper provides explicit evidence that the futures and options exhibit price leadership over the spot market, and the options is ahead of the futures on most trading days in all three markets. This paper also reports a new finding that the relation between the derivative and its underlying index reverses when the index return has a significantly larger mean value, and the reversal phenomenon is also observed in the relations between the futures and the options, which enriches the empirical results of intraday lead-lag relations. Moreover, these conclusions still hold under the impact of extreme events, e.g., the outbreak of the Covid-19. Finally, we construct a pair trading strategy based on the intraday lead-lag relationships, which can get better performance than the corresponding spot index. Our findings can potentially help regulators understand the price discovery process between the index and its derivatives, and also be of great value for timely adjustment of investors intraday trading strategies.

US weekly economic index: Replication and extension

We revisit the US weekly economic index (WEI) put forth by Lewis, Mertens, Stock and Trivedi (2021). In a narrow sense, we replicate their main results with data gathered from its original sources. In a wide sense, we apply the methodology established in Wegmüller, Glocker and Guggia (2023) to adjust the weekly input series for seasonal patterns, calendar day effects, and excess volatility. In a long sense, we show that our proposed data adjustment significantly improves the nowcasting performance of the WEI. © 2023 John Wiley & Sons, Ltd.

Risk connectedness between crude oil, gold and exchange rates in China: Implications of the COVID-19 pandemic.

This study examined the risk connectedness and its asymmetry between oil, gold, and foreign exchange under the realized volatility, spillover index framework, and high-frequency data during the COVID-19 pandemic. It was found that: (1) At the beginning of the pandemic outbreak, the total volatility spillover in the system declined, which may indicate that the pandemic cuts the trading activities in the financial markets by inhibiting personnel mobility, then, the spillover experienced a short-term sharp rise due to panic. (2) The exchange rate had a significant risk connectedness with gold and international crude oil, but a restrict connectedness with domestic crude oil after the outbreak. These variations of risk transmission caused by the pandemic emerged later than the outbreak, reflecting a certain lag. (3) The impact of the pandemic on the asymmetric risk connectedness between oil, gold and the exchange rate was limited, and the risk transfer resulting from bad news was dominant during the sample period; however, gold was less affected by bad news than the oil and exchange rates. These findings suggested that the establishment of Chinese crude oil futures could restrain volatility spillovers from the exchange rate; the foreign exchange reserve structure should be optimized. Gold has been proved to have a hedging function with the crude oil, and its proportion in foreign exchange reserves should be appropriately increased.

Realized higher-order moments spillovers across cryptocurrencies

Using 1-min data of nine cryptocurrency prices, spanning the period 2017 to 2021, the analysis extends Hasan et al. (2021) and Ahmed and Al Mafrachi (2021) papers that explore the dynamic spillovers connectedness of returns and realized moments, including realized volatility, realized skewness, and realized kurtosis, via a time-varying parameter vector autoregression (TVP-VAR) connectedness approach. Our study improves it by offering a larger set of cryptocurrencies, as well as new evidence on the mechanisms that can explain the presence of connectedness. Moreover, our new findings document that spillover effects intensify during shock periods, such as the ‘COVID-19′ pandemic, a fact that the above papers did not consider. Higher-order moment spillovers contain additional information that cannot be observed from return and realized volatility spillovers. Shocks from the cryptocurrency market identify different submitters and recipients across the cryptocurrencies under study. Furthermore, the analysis through quantile regressions illustrate that spillovers are generally affected by a number of factors within the cryptocurrency markets, as well as the COVID pandemic, depending on what tail point of the distribution we are. The findings are of significant importance for investors, portfolio managers, regulators and policymakers who should be aware of the impact of shocks within those markets on the dynamics of spillovers for the sake of investment decisions and financial stability. © 2023 Elsevier B.V.

Weekly economic activity: Measurement and informational content

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables;and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators. © 2021 International Institute of Forecasters

Payout suspensions during the Covid-19 pandemic.

We provide evidence on the unprecedented rate at which firms suspended dividend payments and share repurchases following the outbreak of the Covid-19 pandemic, compare it to the Global Financial Crisis, and estimate the amount of cash firms saved through payout suspensions.

Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures

This study investigates whether China's crude oil futures (INE) and West Texas Intermediate (WTI) markets hold valuable information for estimating the realized volatility of seven Asian stock markets. This study has several notable findings. First, China's oil futures can trigger forecast accuracy for three equity indices (Nikkei 225, NSEI, and FT Straits Times), whereas WTI helps forecast the volatility of the two indices (KSE 100 and KOSPI). Second, comparing China's crude oil futures with WTI's crude oil futures, we find that the former could be an effective indicator for all seven Asian stock markets during a high-volatility period, while WTI information is helpful in forecasting the volatility of the KSE 100, NSEI, and FT Strait Times during the low-volatility period. Further, information of both oil futures is ineffective for the Hang Seng and SSEC equity indices. Our results are robust in several robustness checks, including alternative evaluation methods, recursive window approach, and alternative realized measures, even during the COVID-19 pandemic.

Good versus bad information transmission in the cryptocurrency market: Evidence from high-frequency data

Using 5-minute high-frequency data, we study realized volatility spillovers in major crypto-currencies, employing generalized forecast error variance decomposition. We also include COVID19 period observations and report time-varying and asymmetric connectedness across various cryptocurrencies using realized volatilities and semi-variances. Our study provides diverse connections after distinctly considering good-and bad volatilities, which is unique in the related literature. Bitcoin and Ethereum are central to the system and dominant transmitters of positive shocks, while Litecoin propagates negative shocks abundantly. Ripple and Stellar are the least connected currencies with others, whereas Cardano and EOS are isolated in the network. This feature makes these currencies suitable diversifiers in a portfolio with other cryptocurren-cies. Further, the majority of these connections are asymmetric in the long-and short-run. The time-varying and asymmetric nature of connections offers potentially unique opportunities for diversification and portfolios strategies. Total volatility connectedness is not only significantly enhanced but also changed in its nature during the COVID19 period. We observe no significant changes in results after the robustness check through varying lengths of the rolling-window. The findings are important to crypto investors and regulatory authorities for better diversification strategies and effective market oversight, respectively.

COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis.

Jumps in commodity prices can make asset risk management challenging. This study explores the influence feature of the COVID-19 epidemic on China's commodity price jumps, using 5-min intraday high-frequency futures data of three China's commodity markets (energy, chemical, and metal) from January 23, 2020 to June 10, 2022. We find that firstly the information spillover from the COVID-19 spread situation to China's energy price jumps is relatively weak, and the COVID-19 epidemic shows the most substantial jump information spillover pattern to China's chemical price. The information spillover pattern is time-varying across the COVID-19 spread situation phase. Secondly, there are co-movement patterns between China's commodity price and China/global COVID-19 confirmed cases. This co-movement feature mainly occurs at the medium- or long-run time scales, and varies across commodities. Thirdly, the demand elasticity for China's commodities and its dependence on imports and exports are the main factors influencing the sensitivity of its price jumps to the COVID-19 outbreak.

An oil futures volatility forecast perspective on the selection of high-frequency jump tests

This paper examines the forecasting performances of high-frequency jump tests for oil futures volatility from a comprehensive perspective. It contributes to the literature by investigating which jump test is the best for oil futures volatility forecasting under different circumstances and whether the jump component extracted from multiple alternative tests is useful for further improving forecasting performance. Our results show that the jumps of the TOD test (Bollerslev et al., 2013) have satisfactory performance over the medium-term and especially the short-term forecasting horizons. Most importantly, the jump components from the intersection of multiple intraday tests further improve the forecasting performance. A variety of further discussions, including models controlling for stock market effects and considering periods of high (low) volatility and the COVID-19 pandemic period, confirm the conclusions. This paper attempts to shed light on oil futures volatility prediction from the perspective of jump test selection.

Time-frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic.

Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1-7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.

On smoothing of data using Sobolev polynomials

Data smoothing is a method that involves finding a sequence of values that exhibits the trend of a given set of data. This technique has useful applications in dealing with time series data with underlying fluctuations or seasonality and is commonly carried out by solving a minimization problem with a discrete solution that takes into account data fidelity and smoothness. In this paper, we propose a method to obtain the smooth approximation of data by solving a minimization problem in a function space. The existence of the unique minimizer is shown. Using polynomial basis functions, the problem is projected to a finite dimension. Unlike the standard discrete approach, the complexity of our method does not depend on the number of data points. Since the calculated smooth data is represented by a polynomial, additional information about the behavior of the data, such as rate of change, extreme values, concavity, etc., can be drawn. Furthermore, interpolation and extrapolation are straightforward. We demonstrate our proposed method in obtaining smooth mortality rates for the Philippines, analyzing the underlying trend in COVID-19 datasets, and handling incomplete and high-frequency data. © 2022 the Author(s), licensee AIMS Press.

The impact of COVID-19 on commodity options market: Evidence from China.

Considering the severe economic impact of COVID-19, this study examines COVID-19's influence on the Chinese commodity market. The literature shows that COVID-19's influence in China during its abatement period has not been well investigated. We address this issue by the intraday analysis of the volatility from 16 commodity options contracts in the Chinese commodity options market over the period 2019-2021. We demonstrate that while the pandemic eased in China after its initial outbreak, it still significantly affected the volatility of Chinese agricultural commodities options. In contrast, its impacts on the volatility of options for petrochemicals, ores, and metals are negligible. This pattern reflects the role of pandemic-led supply disruptions affecting agricultural commodity prices as necessities, contributing to higher price volatility relative to non-agricultural commodities, which are less volatile.

Realized higher-order moments spillovers between commodity and stock markets: Evidence from China

Using 5-min data of Chinese stock market index and eight Chinese commodity futures (soybean, wheat, corn, gold, silver, copper and aluminum, crude oil) from March 26, 2018 to October 22, 2020, we analyze the dynamic spillover connectedness of returns and realized moments, including realized volatility, realized skewness, and realized kurtosis, during various shock periods via a time-varying parameter vector autoregression (TVP-VAR) connectedness approach. The results show that spillover effects between stock and commodity markets intensify during shock periods such as ‘Trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger;however, higher-order moment spillovers contain additional information of stock-commodity spillovers that cannot be observed from volatility spillovers. Shocks from the silver market influence all three realized moments of the entire financial markets. Soybean, corn, aluminum, and oil markets are easily affected by other markets. The contribution of wheat to the system of spillovers between stock and commodity markets is only observed at higher-order moments. Further analyses involving OLS and quantile regressions show that total spillovers are generally affected by the US stock market and economic uncertainties as well as the COVID epidemic. We construct daily realized volatility, skewness, and kurtosis using 5-min data of eight Chinese commodity futures and the Chinese stock market index from March 26, 2018 to October 22, 2020, then analyse the dynamic spillovers of realized moments among these markets. The results show that the spillover effects between commodity and stock markets intensify during shock periods such as ‘trade disputes between China and the United States’ and ‘COVID-19’. Volatility spillovers are relatively stronger than spillovers in skewness or spillovers in kurtosis;however, spillovers in higher-order moments seem to contain additional information. Shocks from the silver market influence realized moments of other markets. Soybean, corn, aluminium, and oil markets are affected by other markets. The contribution of wheat as a net transmitter to the system of spillovers between stock and commodity markets is only observed at higher-order realized moments. The results from OLS and quantile regressions show that the total spillovers are generally affected by the US stock market, economic uncertainties, and the COVID-19 outbreak.

The Size of Good and Bad Volatility Shocks Does Matter for Spillovers

Based on the rationale that the propagation of stock volatility shocks within the system can be affected by the size of shocks, we apply a tail-based approach of spillovers based on the variance decomposition of a quantile vector autoregression model. The analysis involves the decomposition of the realized variance into positive and negative realized semivariances using 5-min data on six major stock market indices from the US, Eurozone, UK, Japan, China, and India for the period February 14, 2000 - September 30, 2021. The results show that the propagation of volatility shocks within the system is not only shaped by the sign of the shocks (e.g., good versus bad volatility) but also by the shock size. For each good and bad volatility spillover, we detect a heterogeneity resulting from the difference in the size of spillovers between the upper and middle quantiles and thus reveal a relative intensity effect, as measured by the Relative Intensity of Shock Spillover (RISS) measure that we propose herein. This points to the necessity to go beyond studying spillovers of average shocks and employ tail-based models capable of uncovering the heterogeneous and intensity effects of the shock size. Otherwise, these features will remain hidden, leading to suboptimal inferences and policy implications.

Uncertainty index and stock volatility prediction: evidence from international markets.

This study investigates the predictability of a fixed uncertainty index (UI) for realized variances (volatility) in the international stock markets from a high-frequency perspective. We construct a composite UI based on the scaled principal component analysis (s-PCA) method and demonstrate that it exhibits significant in- and out-of-sample predictabilities for realized variances in global stock markets. This predictive power is more powerful than those of two commonly employed competing methods, namely, PCA and the partial least squares (PLS) methods. The result is robust in several checks. Further, we explain that s-PCA outperforms other dimension-reduction methods since it can effectively increase the impacts of strong predictors and decrease those of weak factors. The implications of this research are significant for investors who allocate assets globally.

A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic.

During the COVID-19 pandemic, policymakers needed to assess the impact of large monetary and fiscal policy interventions in as close to real time as possible-yet existing survey-based indicators are usually released monthly or quarterly. The use of high-frequency data to track economic activity has become widespread. This paper constructs a near real-time economic activity indicator for the Brazilian economy during the COVID-19 pandemic. Brazil's integrated national electricity sector, which covers over 98% of the population, allows us to construct an economic activity indicator based solely on electricity consumption data that are available at near real time and accounts for activity in the large informal sector of the economy. We construct our indicator by isolating the variability in electricity consumption that is not related to economic activity, then measure how well monthly and quarterly versions of our indicator track against standard economic indicators. The results show strong correlation with standard indicators, notably during economic shocks.

Equity clusters through the lens of realized semicorrelations

We rely on newly-developed realized semicorrelations constructed from high-frequency returns together with hierarchical clustering and cross-validation techniques to identify groups of individual stocks that share common features. Implementing the new procedures based on intraday data for the S&P 100 constituents spanning 2019-2020, we uncover distinct changes in the “optimal” groupings of the stocks coincident with the onset of the COVID-19 pandemic. Many of the clusters estimated with data post-January 2020 evidence clear differences from conventional industry type classifications. They also differ from the clusters estimated with standard realized correlations, underscoring the advantages of “looking inside” the correlation matrix through the lens of the new realized semicorrelations. © 2021 Elsevier B.V.

Measuring Leverage Effect of Covid 19 on Stock Price Volatility of Energy Companies Using High Frequency Data

The uprising of the pandemic COVID-19 has paralysed the whole Indian economy, and as a result the Indian stock market is severely affected too. The widely inclusive lockdown articulated on 24th March 2020 by the Prime Minister as a careful step against COVID-19, trailed by ensuing augmentations, has brought about a halt of all financial movement in the country. The objective of the study is to frame different asymmetric price volatility models for Selected Companies under Energy Sector using 1-minute closing price from 15th October 2019 to 15th May 2020 to captivate the leverage effect of the pandemic. The asymmetric terms in the selected asymmetric models are providing sufficient proof that the stock price volatility of three companies out of six under NIFTY Energy i.e., BPCL, Power grid and Indian Oil Corporation are unfavourably influenced by the pandemic. The forecasting graphs for volatility of four companies have been plotted, reveals that there is consistency in the stock price returns of all these four companies but the graph of predicted variance of Indian Oil Corporation reveals that the volatility has been fluctuating drastically with many high peak variances or fluctuations during the two days of forecasted period.

Weekly economic activity: Measurement and informational content

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables;and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators.