Intraday Data Research Articles

It is Imperative to Perform Event Studies Only with High-Frequency Intraday Data for Securities Litigations

It is Imperative to Perform Event Studies Only with High-Frequency Intraday Data for Securities Litigations

Linear regression estimation using intraday high frequency data

<abstract><p>Intraday high frequency data have shown important values in econometric modeling and have been extensively studied. Following this point, in this paper, we study the linear regression model for variables which have intraday high frequency data. In order to overcome the nonstationarity of the intraday data, intraday sequences are aggregated to the daily series by weighted mean. A lower bound for the trace of the asymptotic variance of model estimator is given, and a data-driven method for choosing the weight is also proposed, with the aim to obtain a smaller sum of asymptotic variance for parameter estimators. The simulation results show that the estimation accuracy of the regression coefficient can be significantly improved by using the intraday high frequency data. Empirical studies show that introducing intraday high frequency data to estimate CAPM can have a better model fitting effect.</p></abstract>

Forecasting the High-Frequency Exchange Rate Volatility with Smooth Transition Exponential Smoothing

Smooth Transition Exponential Smoothing (STES) is a popular exponential smoothing method for volatility forecasting; whereby the success of the STES model lies in the choice of the transition variable. In this paper, three realized variance (RV), daily, weekly and monthly RV were used as the transition variables in STES methods to evaluate the performance of intraday data. While daily squared return is a noisy series, squared residual and daily RV were employed as the proxy for actual volatilities in this study. With five series of exchange rates, a comparative analysis was conducted for Ad Hoc methods, Generalised Autoregressive Conditional Heteroscedastic (GARCH) models, and STES methods using various RV combinations. The empirical results showed that when daily RV was used as proxy for actual volatility, the traditional STES models and STES models with RV as the transition variables outperformed Ad Hoc methods and GARCH models under the RMSE evaluation criteria. Similar promising results were also observed for traditional STES models and STES models with RV as the transition variables under MAE evaluation. The MCS results generally reaffirmed the results from both the MAE and RMSE evaluation criteria.

Information flows and the law of one price

This paper explores the role of information flows for the law of one price in an almost frictionless environment. Specifically, we examine whether the volume and content of social media messages are related to the exchange rate pass-through (ERPT) to prices of dual-listed stocks. Our sample includes 37 million tweets mentioning the name of a stock cross-listed in the United Kingdom (UK) and the United States (US) from 2015 to 2018. Using a high-frequency intraday data sample, we observe a negative (positive) link between the ERPT and volume (agreement) of tweets. The findings suggest that large information flows and a high degree of disagreement add extra frictions for the law of one price. In addition, there is an asymmetric pattern of the pass-through, notwithstanding that there are no import/export or geographically-related frictions. This presents further evidence of the importance of information flows in understanding the law of one price.

Causality between Arbitrage and Liquidity in Platinum Futures

Arbitrage and liquidity are interrelated. Liquidity facilitates arbitrageurs’ trading on deviations from the law of one price. However, whether arbitrage opportunity leads to an increase or decrease in liquidity depends on the cause of the deviation. A demand shock leads to greater liquidity, while asymmetric information is toxic to liquidity. We examine how arbitrage and liquidity influence each other in the world’s largest platinum futures markets on exchanges in New York and Tokyo. The markets provide an interesting institutional setting because the futures are based on an identical underlying commodity but exhibit different liquidity characteristics both intraday and over their lifespans. Using intraday data, we find that deviation in currency-adjusted futures prices leads, on average, to an immediate increase in liquidity, suggesting that demand shocks are the dominant driver of arbitrage opportunities. Less actively traded futures experience a greater liquidity effect. Arbitrageurs improve liquidity in both New York and Tokyo by acting as discretionary liquidity traders and cross-sectional market-makers.

Forecasting Stock Market Volatility

Volatility as a measure of investment risk is widely accepted by academic researchers and industry professionals and has become ubiquitous in investment analysis. Furthermore, it is among the few financial variables that exhibit predictable time variation. Hence, there is an extensive amount of literature describing volatility models and assessing their forecasting power. This article provides a discussion of the prominent models and compares them in a unified notation framework. The empirical analysis shows that it is hard to outperform even simple trailing variance–type models. Autoregressive conditional heteroskedasticity (ARCH), generalized ARCH (GARCH), implied volatility, asymmetric, and seasonal models hardly improve forecasts despite added complexity. In this study, only momentum-based and intraday data–based models improved predictive accuracy significantly.

Impacts of relatively rational and irrational investor sentiment on realized volatility

AbstractWe adopt intraday data in this study to facilitate an exploration of the influences of relatively rational and irrational investor sentiment on volatility within the Taiwan stock markets. Following the decomposition of daily trading volume within the Taiwan Stock Exchange Capitalization Weighted Stock Index (TWSE) into two subsets, comprising the trading volume of institutional investors and individual investors, we go on to investigate the influence of each subset on realized volatility. We reveal that the relatively rational sentiment of institutional investors plays a stabilizing role in future volatility, whereas the relatively irrational sentiment of individual investors tends to exacerbate such volatility. Therefore, we suggest that our modified model, which takes into account the relatively rational and irrational sentiment of investors, is capable of more accurately predicting volatility than the benchmark model.

Parallel computing in finance for estimating risk-neutral densities through option prices

Option pricing is one of the most active Financial Economics research fields. Black-Scholes-Merton option pricing theory states that risk-neutral density is lognormal. However, markets' pieces of evidence do not support that assumption. More realistic assumptions impose substantial computational burdens to calculate option pricing functions. Risk-neutral density is a pivotal element to price derivative assets, which can be estimated through nonparametric kernel methods. A significant computational challenge exists for determining optimal kernel bandwidths, addressed in this study through a parallel computing algorithm performed using Graphical Processing Units. The paper proposes a tailor-made Cross-Validation criterion function used to define optimal bandwidths. The selection of optimal bandwidths is crucial for nonparametric estimation and is also the most computationally intensive. We tested the developed algorithms through two data sets related to intraday data for VIX and S&P500 indexes.

Forecastability of Agricultural Commodity Futures Realised Volatility with Daily Infectious Disease-Related Uncertainty

Given the food supply chain disruption from COVID-19 lockdowns around the world, we examine the predictive power of daily infectious diseases-related uncertainty (EMVID) on commodity traded futures within the agricultural bracket, sometimes known as the softs, using the heterogeneous autoregressive realised variance (HAR-RV) model. Considering the short-, medium-, and long-run recursive out-of-sample estimation approach, we estimate daily realised volatility by using intraday data within the 5 min interval for 15 agricultural commodity futures. During the COVID-19 episode, our results indicated that EMVID plays an important role in predicting the future path of agricultural commodity traded futures in the short, medium, and long run, i.e., h = 1, 5, and 22, respectively. According to the MSE-F test, these results are statistically significant. These results contain important implications for investors, portfolio managers, and speculators when faced with investment risk management and strategic asset allocation during infectious disease-related uncertainty.

Forecasting international REITs volatility: the role of oil-price uncertainty

We forecast realized variance (RV) of Real Estate Investment Trusts for 10 leading markets and regions, derived from 5-minutes-interval intraday data, based on the information content of two alternative metrics of daily oil-price uncertainty. Based on the period of the analysis covering January 2008 to July 2020, and using variants of the popular MIDAS-RV model, augmented to include oil market uncertainties, captured by its RV (also derived from 5-minute intraday data) and implied volatility (i.e. the oil VIX), we report evidence of significant statistical and economic gains in the forecasting performance. The result is robust to the size of the forecasting samples, including that of the COVID-19 period, lag-length, nonlinearities, asymmetric effects, and forecast horizon. Our results have important implications for investors and policymakers.

Large-scale and rooftop solar generation in the NEM: A tale of two renewables strategies

Solar generation has set a world record by supplying 100% of electricity demand in (a region of) Australia’s National Electricity Market. Solar output variability coupled with the impact of rooftop solar on demand as a behind-the-meter resource poses challenges to electricity price stability. Using 30-minute intraday data from 2015 to 2021, we find that, on average, large-scale and rooftop solar generation depress the level of spot prices and positively impact volatility. Further, solar generation increases electricity prices in the early morning and in the evening due to the high cost of fossil fuel generators used during off-peak solar generation periods. While large-scale solar generation typically smooths volatility, rooftop solar tends to increase it, reflecting the dominance of axis-tracking systems in the former and north-facing systems in the latter. Solar generation’s impact on electricity prices differs substantially across seasons. These results stress the need for policy adjustment to increase the correlation between solar output and electricity demand through small-scale renewable energy schemes and state-based policies, rooftop solar curtailment, dynamic feed-in tariffs, and two-sided market reform.

The influence of oil price uncertainty on stock liquidity

AbstractUsing high‐frequency intraday data, this study provides strong empirical evidence that elevated oil price uncertainty has a significant and negative influence on stock liquidity. More specifically, the results suggest that large oil‐related corporations are most affected, followed by small‐listed firms more generally. Further analysis reveals that liquidity providers widen the bid‐ask spreads to protect themselves during periods of high oil price uncertainty for large‐listed firms, particularly those in the oil industry. These findings are robust to various measures of oil price uncertainty, different market conditions, structural break analysis and show the influence of oil price movements extends to stock liquidity.

Market versus limit orders of speculative high-frequency traders and price discovery

The Korea Composite Stock Price Index (KOSPI) 200 futures market is one of the largest and most liquid index derivatives markets globally. We utilize high-quality intraday data on KOSPI 200 futures and find that high-frequency traders’ (HFTs’) market orders contribute much more to price discovery than their limit orders, as opposed to the findings of Brogaard, Hendershott, and Riordan (2019) in the Canadian equity market. To explain this phenomenon, we suggest that HFTs in the KOSPI 200 futures market are more speculative traders rather than market makers, which makes market orders more informative.

Liquidity in the global currency market

We study the liquidity of the global currency market by analyzing the price impact of trading volume. We analyze a decade of CLS intraday data representative of global foreign exchange (FX) trading by developing a refinement of the popular Amihud (2002) illiquidity measure that we call realized Amihud, which is the ratio between realized volatility and trading volume. Inversely related to market depth, price impact increases with transaction costs, money market stress, uncertainty, and risk aversion. Furthermore, we analyze whether and how liquidity begets price efficiency by looking at violations of the “triangular” no-arbitrage condition. We find that dollar-based currencies offer a lower trading impact supporting price efficiency.

An Investor’s Guide to Crypto

The authors provide practical insights for investors seeking exposure to the growing cryptocurrency space. Today, crypto is much more than just bitcoin, which historically dominated the space but accounted for just a 31% share of total crypto trading volume in June 2022. The authors discuss a wide variety of tokens, highlighting both their functionality and their investment properties. The authors critically compare popular valuation methods, and they contrast buy-and-hold investing with more active styles. The authors only deem return data from 2017 representative, but the use of intraday data boosts statistical power. Underlying crypto performance has been notoriously volatile, but volatility-targeting methods are effective at controlling risk, and trend-following strategies have performed well. Crypto assets display a low correlation with traditional risky assets in normal times, but the correlation also rises in the left tail of these risky assets. Finally, the authors detail important custody and regulatory considerations for institutional investors.

The connectedness and risk spillovers between bitcoin spot and futures markets: evidence from intraday data.

This paper examines the dynamic relation between Bitcoin spot and futures markets during the Covid-19 pandemic. Using hourly data from 2020 combined with quantile impulse response analysis and predictability in the distribution test, we attempt to ascertain whether spot or futures markets lead in the price discovery process under a variety of market conditions. Granger predictability based on the left tail, the right tail, and the center of the distribution show bidirectional predictability between spot and futures markets suggesting significant feedback effects following normal and extreme gains/losses where neither market dominates in price discovery. Using a CAViaR model and the associated impulse response functions with estimates for dynamic tail dependence, we document spillovers between quantiles of spot and futures returns. Estimates of impulse response functions at various risk levels show the futures market has an edge in influencing the spot market and figures more prominently in the price discovery process.

How does the exchange-rate regime affect dual-listed share price parity? Evidence from China’s A- and H-share markets

This paper examines the impact of exchange-rate regime change on the price disparity of China’s dual-listed stocks. We use four years of synchronous intraday data of 26 pairs of dual-listed RMB-denominated A-shares and their corresponding HKD-denominated H-shares. The sample period covers the 2005 and 2008 changes in the exchange rate regime. During that time, the Chinese authorities strictly prohibited short selling of stocks and tightly regulated capital flows. In contrast to the existing general findings, we find that the law of one price can be strengthened for dual-listed stocks (DLSs) in segmented capital markets under a flexible exchange rate regime; the disparity between the DLSs is reduced under the managed float compared to the pegged regime. Moreover, we find that the magnitude of the H-share discount is positively related to the expected RMB appreciation under managed float; however, under the pegged regime the relationship is negative.

Stock return comovement when investors are distracted: More, and more homogeneous

We study how investors re-allocate attention when the opportunity cost of acquiring information suddenly increases. Using intraday data for more than 750 stocks in 19 countries, we find that investors who are distracted by FIFA World Cup soccer matches shift attention away from firm-specific and global news. When movements in global stock markets are large, pricing of global news reverts back to normal, but domestic firm-specific news keep being priced less, leading to increased comovement with the national market. This increase is stronger for stocks that normally comove less with the national market, leading to a convergence in betas across stocks.

Uncertainty and realized jumps in the pound-dollar exchange rate: evidence from over one century of data

Abstract We study the importance of economic uncertainty so as to predict realized jumps (hereafter jumps) in the pound-dollar exchange rate. The empirical analysis covers the time period from February 1900 to May 2018 on a monthly basis, incorporating several market states, including various booms and crashes. First, we apply a standard linear Granger causality test in order to identify causal effects from economic uncertainty to jumps. We show that the standard linear Granger causality test fails to capture such casual effects. Providing the misspecification of the linear model, we next make use of a nonparametric causality-in-quantiles test. This test allows us to take into account the substantial evidence of nonlinearity along with the structural breaks between economic uncertainty and jumps. In applying this data-driven robust procedure, we find strong evidence of uncertainty causing jumps of the dollar-pound exchange rate. These results are robust over the entire conditional distribution of jumps, exhibiting the strongest impact at the lowest conditional quantiles considered. In addition, our results are generally found to be robust to alternative measures of uncertainty, jumps generated at a daily frequency based on shorter samples of intraday data, and across three other dollar-based exchange rates.

Pricing efficiency and asymmetric multifractality of major asset classes before and during COVID-19 crisis

We examine the impact of COVID-19 pandemic crisis on the pricing efficiency and asymmetric multifractality of major asset classes (S&P500, US Treasury bond, US dollar index, Bitcoin, Brent oil, and gold) within a dynamic framework. Applying permutation entropy on intraday data that covers between April 30, 2019 and May 13, 2020, we show that efficiency of all sample asset classes is deteriorated with the outbreak, and in most cases this deterioration is significant. Results are found to be robust under different analysis schemes. Brent oil is the highest efficient market before and during crisis. The degree of efficiency is heterogeneous among all markets. The analysis by an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach shows evidence of asymmetric multifractality in all markets which rise with the scales. The inefficiency is higher during downward trends before the pandemic crisis as well as during COVID-19 except for gold and Bitcoin. Moreover, the pandemic intensifies the inefficiency of all markets except Bitcoin. Findings reveal increased opportunities for price predictions and abnormal returns gains during the COVID-19 outbreak.