Intraday Data Research Articles

Stock market volatility and multi-scale positive and negative bubbles

We study whether booms and busts in the stock market of the United States (US) drives its volatility. Given this, first, we employ the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to identify both positive and negative bubbles in the short-, medium, and long-term. We successfully detect major crashes and rallies during the weekly period from January 1973 to December 2020. Second, we utilize a nonparametric causality-in-quantiles approach to analyze the predictive impact of our bubble indicators on daily data-based weekly realized volatility (RV). This econometric framework allows us to circumvent potential misspecification due to nonlinearity and instability, rendering the results of weak causal influence derived from a linear framework invalid. The MS-LPPLS-CIs reveal strong evidence of predictability for RV over its entire conditional distribution. We observe relatively stronger impacts for the positive bubbles indicators, with our findings being robust to an alternative metric of volatility, namely squared returns, and weekly realized volatilities derived from 5 (RV5)- and 10 (RV10)-minutes interval intraday data. Furthermore, we detect evidence of predictability for RV5 and RV10 of nine other developed and emerging stock markets. In addition, we also find strong evidence of causal feedbacks from RV5 and RV10 on to the MS-LPPLS-CIs of the 10 countries considered. Finally, time-varying connectedness of the RVs of the G7 stock markets is also shown to be strongly (positively) predicted by the connectedness of the six bubbles indicators. Our findings have significant implications for investors and policymakers.

Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets

Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability.

Risks of heterogeneously persistent higher moments

Using intraday data for the cross-section of individual stocks, we show that both transitory and persistent fluctuations in realized market and average idiosyncratic volatility, skewness and kurtosis are differentially priced in the cross-section of asset returns, implying a heterogeneous persistence structure of different sources of higher moment risks. In particular, we find that both idiosyncratic transitory shocks to volatility and idiosyncratic persistent shocks to skewness share strong commonalities that are relevant to investors.

Realized Volatility Spillover Connectedness among the Leading European Currencies after the End of the Sovereign-Debt Crisis: A QVAR Approach

This paper examines the time-varying spillover effects and connectedness between the euro and other EU and non-EU currencies after the end of the sovereign-debt crisis. We employ the Quantile Vector Autoregression connectedness approach using intraday data for seven currencies (the euro, the British pound, the Swiss franc, the Polish zloty, the Hungarian forint, the Czech koruna, and the Norwegian krone) spanning from 1 January 2016 to 30 November 2022. The results indicate that, almost in all quantiles, the currencies of Eastern European Group countries (i.e., Czech Republic, Hungary, and Poland) are net contributors of information spillovers to other currencies, while currencies of non-EU countries (Switzerland, UK, and Norway) are net takers. Further, we find that the euro is the highest transmitter of net information spillovers to all other currencies until 2021. Interestingly, after 2021, the euro changes to net information spillover taker from all other currencies; highlighting that external shocks (e.g., COVID-19, the energy crisis) have significant risk spillover effects on the European currency market. Policymakers and market participants could benefit from knowing which currency drives developments to avoid unexpected consequences.

Macroeconomic news and price jumps: evidence from ETFs and LOFs in China

PurposeWe examine the relationship between macroeconomic news and fund price jumps, using high-frequency 5-min intraday data for Exchange Traded Funds (ETFs) and Listed Open-end Funds (LOFs) from 2019 to 2020.Design/methodology/approachWe utilize the non-parametric jump test known as the LM method to detect fund price jumps. In addition, we perform Logistic regression to analyze the relationship between macroeconomic news and fund price jumps. Moreover, we use multiple linear regression to explore the relationship between fund price jumps and subsequent returns.FindingsThe probability of price jumps increases by 22.56% when macroeconomic news is released. Moreover, the returns associated with news-driven price jumps display a reversal pattern, and there is an asymmetric relationship in subsequent returns following macroeconomic shocks. Specifically, funds tend to exhibit lower returns after news-driven price jumps compared to those that are not influenced by news events.Research limitations/implicationsIn today's digital age, investors have unprecedented access to a wealth of information through the Internet and various communication platforms. News and market data can be instantly accessed and disseminated, allowing for swift dissemination of information to investors worldwide. However, despite this enhanced accessibility, investors continue to exhibit overreactions or underreactions to new information.Practical implicationsMacroeconomic news release provide crucial insights into the overall health and performance of the economy. By monitoring and analyzing these indicators, investors can gain valuable information that can guide their investment decisions. Furthermore, by fostering a transparent and reliable information disclosure systems, governments can play a critical role in ensuring the stability and transparency of the funds market.Originality/valueThe paper utilizes 5-min high-frequency data from funds and incorporates a comprehensive macroeconomic news information database. These methodological choices enhance the precision and reliability of the analysis, allowing for a more nuanced understanding of the relationship between macroeconomic news releases and fund price jumps.

Exploring calendar effects: the impact of WASDE releases on grain futures market volatility

ABSTRACT The monthly issuance of the World Agriculture Supply and Demand Estimates (WASDE) by the United States Department of Agriculture (USDA) plays a pivotal role in shaping market dynamics through its comprehensive projections and assessments of agricultural commodities. This paper examines the influence of WASDE reports on the intraday volatility of grain futures. We analyse the calendar effect on volatility, focusing on the diurnal patterns on WASDE release days compared to other trading days. This study leverages high-frequency intraday data to explore the immediate and sustained market reactions to the reports, aiming to identify distinct volatility patterns and their implications for traders and market analysts. Our findings confirm the presence of unique diurnal volatility patterns on WASDE release days, significantly diverging from non-release days. This research contributes to the literature by providing refined insights into how public information releases affect market volatility, offering valuable perspectives for understanding and anticipating market movements in response to agricultural reports.

Functional volatility forecasting

AbstractWidely used volatility forecasting methods are usually based on low‐frequency time series models. Although some of them employ high‐frequency observations, these intraday data are often summarized into low‐frequency point statistics, for example, daily realized measures, before being incorporated into a forecasting model. This paper contributes to the volatility forecasting literature by instead predicting the next‐period intraday volatility curve via a functional time series forecasting approach. Asymptotic theory related to the estimation of latent volatility curves via functional principal analysis is formally established, laying a solid theoretical foundation of the proposed forecasting method. In contrast with nonfunctional methods, the proposed functional approach fully exploits the rich intraday information and hence leads to more accurate volatility forecasts. This is confirmed by extensive comparisons between the proposed method and those widely used nonfunctional methods in both Monte Carlo simulations and an empirical study on a number of stocks and equity indices from the Chinese market.

Identification of monetary surprises using intraday data

Identification of monetary surprises using intraday data

How connected is the oil-bank network? Firm-level and high-frequency evidence

By introducing a new generalized forecast error variance decomposition (GFEVD) approach that splits the same into its contemporaneous and lagged components, we investigate the risk spillover effects of different order moments, derived from intraday data, for the top 10 banks and top 10 oil and gas companies in the U.S., covering the period from December 29, 2017 to December 30, 2022. The study finds that, first, the dynamic total connectedness of all order moments is heterogeneous over time driven by economic events. Second, except realized volatility spillovers, the vast majority of overall spillovers are attributable to contemporaneous spillovers, while only a tiny fraction is associated with lagged spillovers. Finally, realized skewness (crash risk) and realized kurtosis (extreme events) in banks and oil and gas companies originate mainly from intra-industry rather than inter-industry transmission.

Hierarchical modeling of irregularly spaced financial returns

Volatility modeling is crucial in finance, especially when dealing with intraday transaction‐level asset returns. The irregular and high‐frequency nature of the data presents unique challenges. While stochastic volatility (SV) models are widely used for understanding patterns in volatility of daily stock returns which constitute regularly spaced time series, new classes of models must be introduced for analyzing volatility in irregularly spaced intraday data. Specifically these models must accommodate the random gaps between successive transactional events. By modeling the gaps using autoregressive conditional duration (ACD) models, we describe a hierarchical irregular SV autoregressive conditional duration (IR‐SV‐ACD) model for estimating and forecasting intertransaction gaps and the volatility of log‐returns. We carry out the analysis in the Bayesian framework via the Hamiltonian Monte Carlo (HMC) algorithm with No‐U‐turn sampler (NUTS) in R using the cmdstanr package. The fits and forecasts are obtained using Monte Carlo averages based on the posterior samples. We illustrate this approach using simulation studies and real data analysis for intraday prices available at microseconds level of health stocks traded on the New York Stock Exchange (NYSE). The log‐returns and gaps are calculated for the stocks and are used for modeling.

Investor behavior in times of conflict: A natural experiment on the interplay of geopolitical risk and defense stocks

We examine the connectedness of aerospace and defense companies and their relation to measures of geopolitical risk. With hierarchical clustering, we find stable and localized company clusters. Increasing geopolitical risk leaves these clusters intact but strengthens inter-cluster connectedness. Focusing on intraday data, we find that for most companies, instantaneous news arrival in form of jumps impacts realized volatility significantly. Further, we show that different measures of geopolitical uncertainty (GPR and COVOL) have differing impact on short-term predictions of intraday volatility, underlying the importance to distinguish between different sources of uncertainty. We provide evidence that investors react instantaneously to increases in geopolitical risk with some persistence of these shocks. The COVOL index holds significant informational content for short- and medium-term predictions of realized volatility of global aerospace and defense companies.

Variance Decomposition and Cryptocurrency Return Prediction

Abstract This article examines how realized variances predict cryptocurrency returns in the cross section using intraday data. We find that cryptocurrencies with higher variances exhibit lower returns in subsequent weeks. Decomposing total variances into signed jump and jump-robust variances reveals that the negative predictability is attributable to positive jump and jump-robust variances. The negative pricing effect is more pronounced for smaller cryptocurrencies with lower prices, less liquidity, more retail trading activities, and more positive sentiment. Our results suggest that cryptocurrency markets are unique because retail investors and preferences for lottery-like payoffs play important roles in the partial variance effects.

Forecasting of clean energy market volatility: The role of oil and the technology sector

This study is the first to explore whether the well-known relationship between the clean energy sector, oil prices, and technology stocks can be leveraged to enhance the accuracy of realized volatility forecasts for individual clean energy sub-sectors. Based on intraday data and various decompositions of daily realized volatility, we account for the heterogeneity across clean energy sub-sectors using the dynamic common correlated effect heterogeneous autoregressive (DCCE-HAR) model. Our findings reveal that, in the short term, price variations in technology shares are more informative for future clean energy volatility than fluctuations in oil prices. In an out-of-sample analysis, we individually forecast the volatility of each clean energy sub-index using Lasso, Ridge, and random forest approaches. We identify sub-indices that systematically benefit from technology sector price variation (e.g. Smart Grid, Operators, Energy Management), sub-indices that benefit from oil price variation (e.g. Bio Fuel, Wind and Geothermal), while also sub-indices that show limited sensitivity to price variation in the technology and oil markets.

Fresh air eases work—the effect of air quality on individual investor activity

Abstract This article shows that contemporaneous and lagged air pollution negatively affects the likelihood of German individual investors to log in and trade in their brokerage accounts, using intraday data and controlling for investor-, weather-, traffic-, and market-specific factors. A 1 SD increase in air pollution leads to a 1.3 percent reduction in the probability of logging in, which is larger than the response to a 1 SD increase in sunshine. We argue that changes in air pollution affect productivity in cognitively demanding tasks, such as trading. Our results are robust to macroeconomic productivity shocks, nonlinearities, or measurement error.

The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks

AbstractWe use a vector autoregressive model with functional shocks, capturing the shift of the entire term structure of interest rates on monetary policy announcement dates, to empirically evaluate the effects of conventional and unconventional monetary policy decisions on the Real Estate Investment Trusts (REITs) markets of the United States (US). Using 5-min interval intraday data, we analyze not only the impact on REITs returns, but also its realized variance (RV), realized jumps (RJ), realized skewness (RSK), and realized kurtosis (RKU) over the daily period of September 2008 to June 2021. While the effects of conventional monetary policy shocks on the moments of REITs returns tend to conform with economic theories, the same is not necessarily the case with unconventional monetary policy shocks. In addition, though monetary policy shocks have the most persistent and strongest effects on RJ, the extreme behaviour of the REITs market is also observed through RSK and RKU. Moreover, when we look into 10 REITs sectors, there is indeed heterogeneity in terms of the strength of the effect, but not so much in terms of the sign of responses of the various moments compared to the overall market. Our results have important implications for REITs market participants, given its exponential growth as an asset class.

Central bank asset purchases and lending: Impact on search frictions

We investigate the impact of two central bank policies, asset purchases and asset lending, on the search frictions in the government bond market in Japan. We build a search-theoretic model to explore the impact of a central bank’s securities lending facility (SLF) by introducing a central bank as a lender. We test model predictions using intraday data from an electronic platform for Japanese government bonds. First, we find large-scale asset purchases (LSAPs) increase order imbalance in the repurchase agreement (repo) market. Threshold analysis reveals that asset purchase amounts exceeding 0.18% of the outstanding, which corresponds to 38.98% of our sample, cause a significantly higher imbalance. Second, the SLF has a floor effect on the repo rate by affecting dealers’ choices between the repo market and the SLF. Third, the novel friction measures we test show that LSAPs and the SLF have opposite influences on bargaining power in the repo market.

Implementing Intraday Model-Free Implied Volatility for Individual Equities to Analyze the Return–Volatility Relationship

We implement the VIX methodology on intraday data of a large set of individual equity options. We thereby consider approaches based on monthly option contracts, weekly option contracts, and a cubic spline interpolation approach. Relying on 1 min, 10 min, and 60 min model-free implied volatility measures, we empirically examine the individual equity return–volatility relationship on the intraday level using quantile regressions. The results confirm a negative contemporaneous link between stock returns and volatility, which is more pronounced in the tails of the distributions. Our findings hint at behavioral biases causing the asymmetric return–volatility link rather than the leverage and volatility-feedback effects.

Performance of crypto-Forex portfolios based on intraday data

This study identifies and assesses the diversification benefits of including large-cap and highly liquid cryptocurrencies into portfolios comprised of major fiat currencies quoted against the USD. We employ hourly data over the period from January 01, 2019 to May 31, 2021. We identify hedging properties across crypto-currency pairs based on intraday volatility fitting through multiplicative component Generalized Autoregressive Conditional Heteroscedasticity (mcGARCH) models and the estimation of Dynamic Conditional Correlation (DCC) Skew Student Copulas. We find that the optimal diversified crypto-Forex portfolio outperforms the actively and passively managed Forex portfolios based on both total risk and downside and upside risk performance measures. Outperformance is robust to different market conditions and optimization methods.

Does public information facilitate price consensus? Characterizing USDA announcement effects using realized volatility

The provision of public information in commodity markets is justified in part by the idea that public information generates consensus among market participants about the fundamental value of the commodity and reduces price volatility. Significant reductions in options-implied volatility following report releases have been presented as evidence of this market-calming effect. We scrutinize this finding in more detail by comparing implied volatility to realized volatility measures from intraday price data. We show that while implied volatility does indeed fall after report releases, realized volatility does not decrease. We measure realized volatility using intraday data and find evidence of much higher volatility on report days only within minutes of the report release. This pattern is consistent with changes in implied volatility being driven by the resolution of uncertainty about the information contained in the report, rather than changes in volatility expectations that may reflect the consensus among traders about forthcoming price volatility.

Dollar’s role in institutional and media impact on stablecoins

Investigating the impact of heterogeneous announcements on stablecoin transaction volumes, an event study is conducted on stablecoins using intraday data. The assumption is that the backed-asset creates a channel through which announcements exert an effect on cryptocurrency transaction volumes. Results indicate that decentralized stablecoins are more sensitive to institutional announcements than both USDC and USDT, suggesting potential policy implications as international coordination or monitoring. The media are found to be worth watching concerning USDT and can play an active role in shaping trading volume over a short time period.