Intraday Data Research Articles

Order Flows and Financial Investor Impacts in Commodity Futures Markets

Abstract Using intraday data, we document statistically strong, but temporary, impacts of commodity index trade flows on commodity futures prices. We also examine the previously documented positive returns around the issuance of commodity-linked notes and find that these returns are an order of magnitude too large to be caused by the small trades necessary to hedge the notes. We provide new evidence that they are instead the result of endogenous issuance. Our results provide novel support for commodity financialization but highlight the importance of measuring the magnitude of financial investment, since even large financial flows have economically modest impacts on prices.

Large dynamic covariance matrices: Enhancements based on intraday data

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.

Total and Net-Directional Connectedness of Cryptocurrencies During the Pre- and Post-COVID-19 Pandemic

This paper presents how volatility propagates through the cryptocurrency market. Our paper provides evidence for volatility connectedness on cryptocurrencies. The different econometric techniques, including the stochastic volatility (SVOL) model and time-varying parameter VAR models using a quasi-Bayesian local likelihood (QBLL), are applied to measure the volatility of the cryptocurrency market. Using high-frequency, intra-day data of the largest cryptocurrencies over 2018–2021, we detect the great volatility of the cryptocurrency market are the beginning of 2019, the beginning of 2020, and throughout the year of 2021. The total connectedness values suggest that the cryptocurrency market becomes volatile as the new strains of the COVID-19 appear at the end of 2021. However, by using directional connectedness, we reveal that there are negative and positive spillovers from a specific cryptocurrency to other cryptocurrencies. The great fluctuations in the period before the COVID-19 health crisis stem from the positive resonance (symmetric) between the volatility of each cryptocurrency, while this health crisis leads to substantially positive and negative spillovers (asymmetric) of cryptocurrencies, and this makes market volatility weaker than it actually is.

Improved Dividend Estimation from Intraday Quotes.

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

Practical Applications of The Low-Risk Anomaly: How Much Is a Good Risk Estimate Worth?

In <b><i>The Low-Risk Anomaly: How Much Is a Good Risk Estimate Worth?</i></b>, from the November 2020 issue of <i>The Journal of Portfolio Management</i>, <b>Tony Barchetto</b> of <b>Salt Financial</b>, <b>Razvan Pascalau</b> of <b>SUNY Plattsburgh</b>, and <b>Ryan Poirier</b> of <b>Salt Financial</b> introduce a new method to exploit the low-risk anomaly. They propose a hybrid model using several lookback periods to measure volatility, thus producing a better measure of volatility than used by the CAPM. This multi-interval technique is a variation on Corsi (2009), who used intraday variance to write the HAR-RV model. Because intraday data are hard to come by, the authors propose a multi-interval approach using more widely available data. The authors find that using a variety of lookback periods results in higher alpha. This is the advantage of their model over standard HAR-RV and CAPM. The authors experimented with real data from December 1967 to June 2018 to test the forecasting abilities of their model on the $75 billion low-risk market. For every 60-day, 252-day, and 60-month lookback period, their model predicted that the lowest-risk portfolios would incur the highest alpha proportions as compared with riskier portfolios. Their model proved accurate when compared with the realized volatility data, or observed beta values, from those same lookback periods. By the authors’ calculation, somewhere between $420 million and $1.9 billion could have been gained from this market if their risk assessment model had been employed.

Asymmetric Volatility Connectedness Among G7 Stock Markets

This paper investigates asymmetries in volatility connectedness among the G7 stock markets. Using daily realized semi-volatility indices, obtained from intra-day data, we provide ample evidence for the asymmetric volatility connectedness. We find that the impact of bad volatility strictly dominates that of good volatility in generating connectedness across financial markets. In particular, the global financial crisis and the European debt crisis have witnessed most influential episodes of volatility connectedness. We also discuss that the effect of the US stock market on other countries has been largely due to bad volatility.

A Structural Study of Fixed Income Securities with Intraday Data

A Structural Study of Fixed Income Securities with Intraday Data

Does Oil Connect Differently with Prominent Assets During War? Evidence from Intra-Day Data During the Russia-Ukraine Saga

Does Oil Connect Differently with Prominent Assets During War? Evidence from Intra-Day Data During the Russia-Ukraine Saga

Pair trading of Commodity Futures in China through the lens of intraday data A Machine Learning Framework and Empirical Analysis

Pair trading of Commodity Futures in China through the lens of intraday data A Machine Learning Framework and Empirical Analysis

Predicting Option Prices and Volatility with High Frequency Data Using Neural Network

Neural network utilizes the huge amount of data for analysis and prediction. This paper predicts option prices and volatility using neutral network based on high frequency intraday data. We focus on short term prediction because option prices and volatility in fact are very volatile and almost impossible to predict. We find that neural network is able to predict option prices and volatility by using predictors constructed from the prices of option and its underlining index, especially in short term which is what practitioners care about more in practice.

How External Trends and Internal Components Decomposition Method Improve the Predictability of Financial Time Series?

Accurately predicting stock market returns can pay off economically not by yielding significant profit but rather by preventing the loss of a large sum of money. Therefore, stocks with a high degree of predictability gain more attention. This study is intended to investigate the impact of decomposing returns on improving predictability. The decomposition method separates the return’s internal components from their external trend. The approximate entropy technique is then applied to quantify their randomness amounts. The results reveal that the decomposition method improved the predictability of returns from S & P 500, Nasdaq 100, SSE and SZSE 500 stocks. Furthermore, the outcomes show that using the return’s absolute value can further enhance its performance. Moreover, this study shows that S & P 500 intraday data are more predictable than their daily data. These findings propose incorporating the decomposition method in the prediction process to improve the predictability and maximise the investor’s profit and minimise their risk.

The Impact of COVID-19 on Stock Market in China

This paper studies the impact of the COVID-19 on the stock ambiguity, risks, liquidity, and stock prices in China stock market, before and after the outbreak of COVID-19 during the Chinese Spring Festival holidays in 2020. We measure stock ambiguity using the intraday trading data. The outbreak of COVID-19 has a significant impact on the average stock ambiguity, risk, and illiquidity in China and induces structural break in the market average ambiguity. However, the equity premium and liquidity premium change little during the same period. The market average stock ambiguity and risks decrease, and stock liquidity improves to pre-pandemic levels as the pandemic is under control in China. The market average stock ambiguity and risks in China increase again when the confirmed new cases in the U.S. surge in the second half of 2020. We also find a “flight-to-liquidity” phenomenon, and the equally-weighted (value-weighted) 20-trading-day liquidity premium declined significantly to about –4.42% (–6.48%) during the fourth quarter of 2020.

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.

Does the short squeeze lead to market abnormality and antileverage effect? Evidence from the Gamestop case

PurposeThis study examines the Gamestop (GME) short squeeze in early 2021. Using intraday data for the period 4/1/2021–5/2/2021, the author provides empirical evidence that the GME stock price exhibited abnormal behavior.Design/methodology/approachThe author uses the popular Runs test to show that the GME returns were not randomly distributed, which is an indication of a violation of the Efficient Market Hypothesis (EMH). The main objective of the paper is to provide new quantitative evidence that stock returns are abnormal when short squeeze conditions emerge. The author employs the asymmetry Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models (the Exponential GARCH (EGARCH) and the Threshold GARCH (TGARCH)) and provides evidence that an exceptional time series feature emerged during the examined period: the antileverage effect.FindingsThe results show that the GME returns were not randomly distributed during the examined period and the asymmetry GARCH models indicate that, in contrast to what the time series normally show, volatility increased when the GME prices increased.Research limitations/implicationsThis paper presents a new/alternative approach for the study of EMH and abnormal returns in financial markets. Further studies on market performance during similar short squeeze conditions should be carried out in order to obtain empirical evidence for the antileverage effect abnormality.Practical implicationsThis paper could be useful for scholars who examine the EMH in financial markets because it suggests an additional method for testing abnormalities. It also presents a useful tool that allows practitioners to monitor for indications of abnormality in the stock market during a short squeeze, since the emergence of the antileverage abnormality could function as such an indication. Additionally, the outcome of this analysis could be useful for regulators because coordination among investors is easier than ever in the Internet era and such events may happen again in the future; even under normal (not short squeeze) conditions and lead to market instability.Originality/valueThis research differs from other studies that examine the GME case because it presents a new way to quantitatively present the abnormal performance of the stock markets for reasons that could be linked with the emergence of short squeeze conditions.

Social norms and information asymmetry: the effect of religion on informed trading

ABSTRACT This study investigates whether social norms impede informed trading. We use intraday data from Boursa Kuwait for January 2015 to December 2018, which clearly identifies institutions' religious identity. Consistent with our hypothesis, we find that the Islamic values of equality and trust lead to lower information asymmetry. Specifically, stocks of Islamic institutions experience lower informed trading in comparison to the stocks of non-Islamic institutions.

Price efficiency and safe-haven property of Bitcoin in relation to stocks in the pandemic era

PurposeWhen investors experience extreme uncertainty, they seek “safe havens” to reduce their risk, to limit their losses and to protect the value of their portfolios. The purpose of this paper is to examine the safe-haven properties of Bitcoin compared to the stock market.Design/methodology/approachBased on intraday data, this study compares the price efficiencies of Bitcoin and Morgan Stanley Capital Index (MSCI) using Multifractal Detrended Fluctuation Analysis for the second half of 2020. This study then evaluates Bitcoin’s safe-haven property using Detrended Partial-Cross-Correlation Analysis (DPCCA).FindingsThis study finds that the price efficiency of Bitcoin is lower than that of MSCI. Further, Bitcoin was not a safe haven at any time for the MSCI index. The net cross-correlations between Bitcoin and MSCI are weak and they vary at different time scales.Research limitations/implicationsThe behavior of market prices varies over time. Therefore, it is important to replicate this study for other time periods.Social implicationsThe paper sheds light on the price behavior of Bitcoin during a period of instability. The results suggest that the construction of portfolios should differ based on the time horizons of the investors.Originality/valueThe authors compare Bitcoin against a global equity index instead of a specific country index or commodity. They also demonstrate the applicability of DPCCA in finance research.

Online refinement of day‐ahead forecasting using intraday data for campus‐level load

Day-ahead forecasting for campus-level load is important for better energy management, but especially difficult to be accurate, compared to large-scale loads such as cities or regions. This is because irregular and unpredictable behavior of individual loads in small-scale loads cannot be fully smoothed out and thus pose a negative impact on forecasting accuracy. This paper presents an online refinement strategy for day-ahead forecasting using intraday data for a campus-level load, focusing on self-adapting correction of forecasting considering load pattern. First, according to the load pattern forecasted by Classification and Regression Trees (CART) one day ahead, the correction parameter group is assigned to the correction model. If the correction conditions are met, the forecasting results for the rest of the day will be updated. After all-day load data is achieved, the actual load pattern can be identified and used to train CART model. To confirm the effectiveness of the proposed strategy, the model is evaluated by two datasets, a real-life dataset of an industrial park in Shanghai China and a public dataset of an institute campus in Delhi, India. The results demonstrate that the proposed strategy can improve short-term forecasting accuracy.

Performance of intraday technical trading in China’s gold market

This paper investigates the predictive power of intraday technical trading in China’s gold market. We conduct this study by employing a 5-min intraday data from the most active futures contract listed on the Shanghai Futures Exchange covering the period spanning from 12th March 2018 to 10th March 2021. Our empirical evidence suggests that after comprehensively considering data snooping, transaction costs, market conditions, and frequencies of portfolio rebalancing, investors are unlikely to choose technical trading rules that persistently yield attractive out-of-sample performance, indicating that the predictive power of intraday technical trading in China’s gold market is illusory.

Intraday volume-return nexus in cryptocurrency markets: Novel evidence from cryptocurrency classification

This paper analyses the volume-return relationships across the top 30 most traded cryptocurrencies from February 2018 to July 2019 using high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which the cryptocurrency is built, and consensus algorithm, among others. We identify significant bidirectional causalities between trading volume and returns at different high-frequency intervals; however, those linkages are weakening with decreasing data frequencies. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency markets providing useful insights from cryptocurrency categorisation.

Macroeconomic news announcements and market efficiency: Evidence from the U.S. Treasury market

• This paper investigates the impact of scheduled macroeconomic news announcements on the U.S. Treasury market’s efficiency. • This paper employs a robust method to construct market inefficiency measures using intraday data and controlling for microstructure noise. • The U.S. Treasury market is less efficient in the five-minute interval before news arrival. • Investor heterogeneity provides a possible explanation for the decreased market efficiency before scheduled news announcements. We investigate the impact of scheduled macroeconomic news announcements on the U.S. Treasury market’s efficiency. Using intraday data and controlling for microstructure noise, we employ a robust method to construct market inefficiency measures. We find that the U.S. Treasury market is less efficient in the five-minute interval before news arrival. Our findings are robust for different sample periods, macroeconomic news announcements, and market inefficiency measures. We find that investor heterogeneity provides a possible explanation for the decreased market efficiency before scheduled news announcements.