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In this paper, we propose an estimator of power spot volatility of order p through Edgeworth expansion. We provide a precise description of how to compute the expansion and the first four cumulants are given in an explicit form. We also construct feasible confidence intervals (one-sided and two-sided) for the pth power spot volatility estimator by using Edgeworth expansion. A Monte Carlo simulation study shows that the confidence intervals and probability density curve based on Edgeworth expansion perform better than the conventional case based on Normal approximation. [ABSTRACT FROM AUTHOR]

We develop a statistical inference procedure for the ubiquitous calendar effects in microstructure noise using high frequency data. This is, to the best of our knowledge, the first inference theory ever built for noise calendar effect under the general semi‐martingale‐plus‐noise setup for prices contaminated with non‐stationary, endogenous, and serially dependent microstructure noise. We devise a noise‐calendar‐effect estimator by an appropriately scaled average of high‐frequency returns that precede a time of day across a large number of trading days. Feasible central limit theorem for the estimator is established under a joint infill and long‐span asymptotics. Monte Carlo simulations corroborate our theoretical findings. An empirical study on the high‐frequency data of the e‐mini S&P 500 futures and a Chinese stock demonstrates that the noise calendar effect has undergone significant changes over time for the latter, yet remains stable for the former. [ABSTRACT FROM AUTHOR]

Most existing studies on volatility forecasting have focused on interday characteristics and ignored intraday characteristics of high‐frequency data, especially the asymmetric impact of positive and negative jumps on volatility. In this paper, 5‐min high‐frequency data are used to construct realized volatility which is decomposed into continuous components and jump components with positive and negative directions. Then, this information is combined with the long short‐term memory model for the realized volatility prediction. The empirical analysis demonstrates that negative jumps resulting from negative news have a more significant impact on market volatility than positive jumps. Additionally, the long short‐term memory model, which incorporates positive and negative jump volatility, outperforms traditional econometric and machine learning models in predicting out‐of‐sample volatility. Furthermore, applying the prediction results to value at risk yields a better measurement effect than the generalized autoregressive conditional heteroskedasticity model. [ABSTRACT FROM AUTHOR]

In this paper, we focus on filtering and parameter estimation in stochastic volatility models when observations arise from high-frequency data. We are particularly interested in rough volatility models where spot volatility is driven by fractional Brownian motion with Hurst index $ H H < 1 2 . Since volatility is not directly observable, we rely on particle filtering techniques for statistical inference regarding the current level of volatility and the parameters governing its dynamics. In order to obtain numerically efficient and recursive algorithms, we use the fact that a fractional Brownian motion can be represented through a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes). We analyze the performance of our approach on simulated data and we compare it to similar studies in the literature. The paper concludes with an empirical case study, where we apply our methodology to high-frequency data of a liquid stock. [ABSTRACT FROM AUTHOR]

This article introduces a dynamic minimum variance portfolio (MVP) model using nonlinear volatility dynamic models, based on high-frequency financial data. Specifically, we impose an autoregressive dynamic structure on MVP processes, which helps capture the MVP dynamics directly. To evaluate the dynamic MVP model, we estimate the inverse volatility matrix using the constrained l 1 -minimization for inverse matrix estimation (CLIME) and calculate daily realized non-normalized MVP weights. Based on the realized non-normalized MVP weight estimator, we propose the dynamic MVP model, which we call the dynamic realized minimum variance portfolio (DR-MVP) model. To estimate a large number of parameters, we employ the least absolute shrinkage and selection operator (LASSO) and predict the future MVP and establish its asymptotic properties. Using high-frequency trading data, we apply the proposed method to MVP prediction. [ABSTRACT FROM AUTHOR]

We generate new evidence on disagreement among traders in the S&P 500 options market from high-frequency intraday price and volume data. Inference on disagreement is based on a model where investors observe public information but agree to disagree on its interpretation; disagreement among investors is captured by the volume–volatility elasticity. For options, there are two natural variables related to disagreement: moneyness and tenor, which we relate to disagreement about the distribution of the market index at different quantiles and times. The estimated volume–volatility elasticity equals unity for options near the money and close to expiration, which is consistent with the case of no disagreement among investors. In contrast, the elasticity estimates decrease with increases in the absolute value of moneyness, indicating investors have a higher disagreement about rare events. Likewise, the elasticity decreases with increases in tenor, implying higher investors' disagreement about more distant events. [ABSTRACT FROM AUTHOR]

We study two sources of heteroscedasticity in high-frequency financial data and estimate their contribution to overall volatility by means of a Markov switching (MS) structural VAR model. We achieve identification for all coefficients by assuming that the structural errors follow a GARCH-DCC process. Using transaction data of the EUR/USD interdealer market in 2016, we first detect three regimesof volatility. Then we show that both sources of volatility matter for the transmission of shocks, and that information is channeled to the market mostly through demand shocks. This suggests that, on the EUR/USD market, some liquidity takers (LTs) are better informed than both liquidity providers and those LTs who follow a feedback strategy. [ABSTRACT FROM AUTHOR]

We analyse a second-order SPDE model in multiple space dimensions and develop estimators for the parameters of this model based on discrete observations of a solution in time and space on a bounded domain. While parameter estimation for one and two spatial dimensions was established in recent literature, this is the first work which generalizes the theory to a general, multi-dimensional framework. Our approach builds upon realized volatilities, enabling the construction of an oracle estimator for volatility within the underlying model. Furthermore, we show that the realized volatilities have an asymptotic illustration as response of a log-linear model with spatial explanatory variable. This yields novel and efficient estimators based on realized volatilities with optimal rates of convergence and minimal variances. For proving central limit theorems, we use a high-frequency observation scheme. To showcase our results, we conduct a Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a nonparametric estimator of ruin probability in the Wiener–Poisson risk model based on high-frequency data. The estimator is constructed via the Fourier-cosine method and the threshold technique, and the convergence rate is also studied for a large sample size. Finally, we verify the effectiveness of our estimator through some simulation studies. [ABSTRACT FROM AUTHOR]

Non‐steady‐state chambers are widely employed for quantifying soil emissions of CO2, CH4, and N2O. Automated non‐steady‐state (a‐NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high‐frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post‐measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high‐resolution data obtained from an a‐NSS sampling system. Applied to a 38‐day dataset, the algorithm captured concurrent field measurements of CO2, CH4, and N2O fluxes. The automated sampling system enables the acquisition of high‐frequency data, allowing the detection of episodic gas flux events. By using shape‐constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO2 and N2O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO2, CH4, and N2O fluxes, providing both instantaneous and diel values directly from raw, high‐temporal‐resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a‐NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics. [ABSTRACT FROM AUTHOR]

The scope of this manuscript is to review some recent developments in statistics for discretely observed semimartingales which are motivated by applications for financial markets. Our journey through this area stops to take closer looks at a few selected topics discussing recent literature. We moreover highlight and explain the important role played by some classical concepts of probability and statistics. We focus on three main aspects: Testing for jumps; rough fractional stochastic volatility; and limit order microstructure noise. We review jump tests based on extreme value theory and complement the literature proposing new statistical methods. They are based on asymptotic theory of order statistics and the Rényi representation. The second stage of our journey visits a recent strand of research showing that volatility is rough. We further investigate this and establish a minimax lower bound exploring frontiers to what extent the regularity of latent volatility can be recovered in a more general framework. Finally, we discuss a stochastic boundary model with one-sided microstructure noise for high-frequency limit order prices and its probabilistic and statistical foundation. [ABSTRACT FROM AUTHOR]

In recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high‐frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models. [ABSTRACT FROM AUTHOR]

This study investigates and uses multi-kernel Hawkes models to describe a high-frequency mid-price process. Each kernel represents a different responsive speed of market participants. Using the conditional Hessian, we examine whether the numerical optimizer effectively finds the global maximum of the log-likelihood function under complicated modeling. Empirical studies that use stock prices in the US equity market show the existence of multi-kernels classified as ultra-high-frequency (UHF), very-high-frequency (VHF), and high-frequency (HF). We estimate the conditional expectations of arrival times and the degree of contribution to the high-frequency activities for each kernel. [ABSTRACT FROM AUTHOR]

This paper investigates the high‐frequency volatility modeling and prediction for crude oil futures in China, a new asset class emerging in recent years. Two volatility measures, the realized variance (RV) and realized bi‐power variations (RBV) are constructed at various frequencies by virtue of 1‐minute crude oil futures prices. The distinctive components of these volatility estimators are further identified to exploit the information contents in the in‐sample explanatory power of the realized variance dynamics and the out‐of‐sample prediction of realized variance across different horizons, leading to four new HAR‐RV‐type models. First, the empirical results show that the continuous component of the weekly realized variance, representing investors' trading behavior in the medium‐term, is the dominant factor driving up volatility trends in China's crude oil futures market over a range of market conditions. Second, the monthly jump component in realized variance presents the significant in‐sample explanatory power, and yet marginally improves prediction performance in realized variance during the two out‐of‐sample periods. Finally, these results are robust toward various market/model setups, over day‐ and night‐trading hours, and across a range of prediction horizons and relative to prediction benchmarks. [ABSTRACT FROM AUTHOR]

Widely 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. [ABSTRACT FROM AUTHOR]

Forecasting returns in financial markets is notoriously challenging due to the resemblance of price changes to white noise. In this paper, we propose novel methods to address this challenge. Employing high-frequency Brazilian stock market data at one-minute granularity over a full year, we apply various statistical and machine learning algorithms, including Bidirectional Long Short-Term Memory (BiLSTM) with attention, Transformers, N-BEATS, N-HiTS, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) to predict changes in the price ratio of closely related stock pairs. Our findings indicate that a combination of reversion and machine learning-based forecasting methods yields the highest profit-per-trade. Additionally, by allowing the model to abstain from trading when the predicted magnitude of change is small, profits per trade can be further increased. Our proposed forecasting approach, utilizing a blend of methods, demonstrates superior accuracy compared to individual methods for high-frequency data.

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures. [ABSTRACT FROM AUTHOR]

Volatility forecasting from high‐frequency data plays a crucial role in many financial fields, such as risk management, option pricing, and portfolio management. Many existing statistical models could better describe and forecast the characteristics of volatility, whereas they do not simultaneously account for the long‐term memory of volatility, the nonlinear characteristics of high‐frequency data, and technical index information during the modeling phase. The purpose of this paper is to use the prediction advantage of deep learning long short‐term memory (LSTM) model to predict the volatility fusing three classes of information, that is, high frequency realized volatility (H), technical indicators (I), and the parameters of generalized autoregression conditional heteroskedasticity(GARCH), heterogeneous autoregressive (HAR), and c, resulting in a novel LSTM‐HIT model to forecast realized volatility. We employ the extreme value theory (EVT) of a semiparametric method to estimate the quantile of standardized return and construct the LSTM‐HIT‐EVT model to forecast the value at risk (VaR). Empirical results show that the LSTM‐HIT model provides the most accurate volatility forecast among the various considered models and that the LSTM‐HIT‐EVT model yields forecasts more accurate than other VaR models. [ABSTRACT FROM AUTHOR]

The variance risk premium is a critical predictor of expected returns. However, numerous studies indicate that expected returns depend strongly on the state of the economy. Herein, we examine the effect of the variance risk premium in different market states by using cross – sectional regression and predictability of returns. Our empirical results show that the variance risk premium is a significantly priced factor in bull markets. Additionally, predicted return horizons are shorter in bear markets than in bull markets. Compared with that in bull markets, the predictive ability of the variance risk premium diminishes more rapidly in bear markets when the horizon period is lengthened. [ABSTRACT FROM AUTHOR]

With the occurrence of the global financial crisis in 2008, the U.S. unconventional monetary policy affected the Chinese market. Based on a monthly data sample from 2008M1 to 2015M12, in this paper we identify U.S. and Chinese monetary policy shocks by using a structural vector autoregression (SVAR) model with multi-external instrumental variables along with principal component analysis (PCA) combined with high-frequency financial market data. The empirical results show that the unconventional monetary policies had a negative effect on China's inflation and output due to the signal effect, and China's stock and commodity markets increased in the short term. During the same period, China's monetary policy had a greater impact on the domestic economy and financial markets. The conclusion of this paper provides a significant reference for relevant departments to make decisions amidst the new wave of unconventional U.S. monetary policies due to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper introduces a novel multi‐variate volatility model that can accommodate appropriately defined network structures based on low‐frequency and high‐frequency data. The model offers substantial reductions in the number of unknown parameters and computational complexity. The model formulation, along with iterative multi‐step‐ahead forecasting and targeting parameterization are discussed. Quasi‐likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation studies are carried out to assess the performance of parameter estimation in finite samples. Furthermore, a real data analysis demonstrates that the proposed model outperforms the existing volatility models in prediction of future variances of daily return and realized measures. [ABSTRACT FROM AUTHOR]

This study examines the impact of political risk on financial markets by leveraging high-frequency (minute-by-minute) price data and precise event timestamps from media outlets' Twitter feeds during Pedro Castillo's failed coup attempt in Peru. Unlike previous research that relies on low-frequency data and protracted political changes, our analysis demonstrates that daily closing prices may misleadingly suggest negligible impact. In contrast, high-frequency data reveal that markets promptly and accurately incorporated news of the coup attempt and, in turn, its failure into asset prices. Our analysis shows that breakdowns in democratic governance negatively affect asset prices, while the restoration of the rule of law, in the form Congressional checks on the Executive branch, boosts them. Moreover, our analyses suggest that domestic companies and sectors with less mobile assets are more vulnerable to these political risks. Our findings underscore the crucial role of high-frequency data in accurately capturing how institutions and political risk affects equity markets. [ABSTRACT FROM AUTHOR]

We investigate information processing in the stochastic process driving stock's volatility (volatility discovery). We apply fractionally cointegration techniques to decompose the estimates of the market-specific integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component. The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identification of the integrated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-fill asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identifies a distinct information process than that based on the price discovery analysis. [ABSTRACT FROM AUTHOR]

This article studies high-frequency econometric methods to test for a jump in the spread of bond yields. We propose a coherent inference procedure that detects a jump in the yield spread only if at least one of the two underlying bonds displays a jump. Ignoring this inherent connection by basing inference only on a univariate jump test applied to the spread tends to overestimate the number of jumps in yield spreads and puts the coherence of test results at risk. We formalize the statistical approach in the context of an intersection union test in multiple testing. We document the relevance of coherent tests and their practicability via simulations and real data examples. [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]

We develop a test for deciding whether the linear spaces spanned by the factor exposures of a large cross‐section of assets toward latent systematic risk factors at two distinct points in time are the same. The test uses a panel of asset returns in local windows around the two time points. The asymptotic setup is of joint type: the number of assets and the number of return observations per asset increase asymptotically while the length of both time windows shrinks. We estimate the factor exposures, up to rotation, over the two periods using classical principal component analysis and evaluate their projection discrepancy, which is rotation invariant. This projection discrepancy is then centered with one between factor exposures computed over a partition of the pooled return data into odd and even increments. We derive the limit distribution of the statistic under the null hypothesis and develop an easy‐to‐implement bootstrap for constructing the critical region of the test. The test is applied to intraday financial data to determine whether the linear span of assets' systematic risk exposures differ during a trading day or after a release of important economic information. [ABSTRACT FROM AUTHOR]

The volatility of financial asset returns displays pronounced variation over the trading day. Our goal is nonparametric inference for the average intraday volatility pattern, viewed as a function of time-of-day. The functional inference is based on a long span of high-frequency return data. Our setup allows for general forms of volatility dynamics, including time-variation in the intraday pattern. The estimation is based on forming local volatility estimates from the high-frequency returns over overlapping blocks of asymptotically shrinking size, and then averaging these estimates across days in the sample. The block-based estimation of volatility renders the error in the estimation due to the martingale return innovation asymptotically negligible. As a result, the centered and scaled calendar volatility effect estimator converges to a Gaussian process determined by the empirical process error associated with estimating average volatility across the trading day. Feasible inference is obtained by consistently estimating the limiting covariance operator. Simulation results corroborate our theoretical findings. In an application to S&P 500 futures data, we find evidence for a shift in the intraday volatility pattern over time, including a more pronounced role for volatility outside U.S. trading hours in the latter part of the sample. for this article are available online. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical results demonstrate that the LSTM technique can predict both Ethereum and Bitcoin prices. However, the performance of this algorithm decreases as the number of hidden units and epochs grows, with 100 hidden units and 200 epochs delivering maximum forecast accuracy. Furthermore, the performance study demonstrates that the LSTM approach gives more accurate forecasts for Ethereum than for Bitcoin prices, indicating that Ethereum is more prominent than Bitcoin. Moreover, the increased accuracy of forecasting the Ethereum price made it more reliable than Bitcoin during the COVID-19 coronavirus crisis. As a result, cryptocurrency traders might focus on trading Ethereum to increase their earnings during a crisis. [ABSTRACT FROM AUTHOR]

Forecasting returns in financial markets is notoriously challenging due to the resemblance of price changes to white noise. In this paper, we propose novel methods to address this challenge. Employing high-frequency Brazilian stock market data at one-minute granularity over a full year, we apply various statistical and machine learning algorithms, including Bidirectional Long Short-Term Memory (BiLSTM) with attention, Transformers, N-BEATS, N-HiTS, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) to predict changes in the price ratio of closely related stock pairs. Our findings indicate that a combination of reversion and machine learning-based forecasting methods yields the highest profit-per-trade. Additionally, by allowing the model to abstain from trading when the predicted magnitude of change is small, profits per trade can be further increased. Our proposed forecasting approach, utilizing a blend of methods, demonstrates superior accuracy compared to individual methods for high-frequency data. [ABSTRACT FROM AUTHOR]

Abstract As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors’ collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies’ investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies.

Firms increasingly follow an "always on" philosophy, publishing multiple pieces of firm-generated content (FGC) every day. Current methodologies used in marketing are unfit to unbiasedly capture the impact of FGC disseminated intermittently throughout the day on stock markets characterized by ultra-high-frequency trading. They also neither distinguish between the permanent (i.e., long-term) and temporary (i.e., short-term) price impacts nor identify FGC attributes capable of generating these price impacts. In this study, the authors define price impact as the impact on the variance of stock price. Employing a market microstructure approach to exploit the variance of high-frequency changes in stock price, the authors estimate the permanent and temporary price impacts of the firm-generated Twitter content of S&P 500 information technology firms. The authors find that firm-generated tweets induce both permanent and temporary price impacts, which are linked to tweet attributes of valence and subject matter. Tweets reflecting only valence or subject matter concerning consumer or competitor orientation result in temporary price impacts, whereas those embodying both attributes generate permanent price impacts. Negative-valence tweets about competitors generate the largest permanent price impacts. Building on these findings, the authors offer suggestions to marketing managers regarding the design of intraday FGC. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess whether the availability of high-frequency data enhances the accuracy of extreme market risk estimation in comparison to low-frequency data by using Value-at-risk (VaR) and Expected shortfall (ES). The sample data used for analysis comprised the daily closing stock prices and 5-minute intraday stock prices of DJIA, FTSE100, BOVESPA, and MERVAL Index from 2014 to 2022. The data analysis was done to compare the performance of two-stages hybrid methods called conditional EVT that combined the GARCH, RV and HAR specification models with the EVT approach. To assess the accuracy of the VaR forecasts, out-of-sample VaR forecast was backtested by using unconditional coverage (UC) and conditional coverage (CC) tests. The VaR backtesting procedure also incorporated the utilization loss function which are the regulatory loss function (RLF) and the firm’s loss function (FLF). The accuracy of the forecasted ES was backtested by using the generalized breach indicator (GBI) method. The findings of this research emphasized that high-frequency conditional EVT, incorporating the HAR specification outperformed the low-frequency conditional EVT in predicting market risk during periods characterized by extreme returns. Based on the VaR and ES measure, the HAR-EVT typed models are the best performance model compared to the GARCH-EVT and RV-EVT typed models during both crisis and non-crisis periods. This research study contributes to the current literature on the forecasting ability of risk models by concentrating on the hybrid model of long-memory models (FIEGARCH, RV-FIEGARCH and HAR-FIEGARCH) for with the EVT approach.

AbstractDifferences in the behaviour of asset prices depending on data frequency have not been thoroughly investigated in the literature despite their possible importance. In particular, high-frequency data might contain more information about financial assets because they are updated more rapidly in response to news. This paper explores persistence in high-frequency data (and also daily and monthly ones) in the case of the EuroStoxx 50 futures prices over the period from 2002 to 2018 (720 million trade records) using R/S analysis and the Hurst exponent as a measure of persistence. The results show that persistence is sensitive to the data frequency. More specifically, monthly data are highly persistent, daily ones follow a random walk, and intraday ones are anti-persistent. In addition, persistence varies over time. These findings imply that the Efficient Market Hypothesis (EMH) only holds in the case of daily data, whilst it is possible to make abnormal profits using trading strategies based on reversal strategies at the intraday frequency.

The mechanisms driving catchment nitrogen storage and release operate at multiple spatiotemporal scales. Consequently, analyses grounded in different observational timescales can yield discrepant interpretations of underlying mechanisms. To assess the consistency of nitrate export patterns between event‐ and inter‐annual scales, we evaluated multiple years of high‐frequency observations of nitrate concentrations (C) and discharge (Q) including 3,480 discrete discharge events from 28 dominantly agricultural catchments. We observed consistent and often drastic divergence between long‐term and median event‐specific C‐Q patterns. Most catchments showed long‐term enrichment patterns (positive C‐Q slope), but events were, on average, more chemostatic (close‐to‐zero C‐Q slopes). C‐Q slope variability was high for small events but decreased with event magnitude, approaching chemostatic patterns during the largest storms, yielding compelling evidence against nitrate source limitation. We conclude that nitrate export patterns observed at different temporal scales and event magnitudes are controlled by different processes, therefore embedding complementary information. Plain Language Summary: Storage and mobilization of nitrogen in the landscape ultimately shape nitrate concentration dynamics in streams and their transport to downstream receiving waters. However, the temporal resolution of data analyses (e.g., long‐term or high‐flow events) can affect the interpretation of nitrate export from catchments and underlying mechanisms. To test whether nitrate export patterns differ between event and long‐term time scales, we evaluated several years of daily observations of nitrate concentration and discharge, including 3,480 high‐flow events from 28 dominantly agricultural catchments. We observed consistent and often drastic divergence between long‐term and average event‐specific export patterns. The variability in export patterns between events decreased with event magnitude toward patterns that indicate no nitrogen source limitation. Nitrate export patterns are regulated by different controls depending on the temporal scale of analysis, which must be considered to avoid incomplete and misleading interpretations about the underlying mechanisms. Key Points: Long‐term and event‐scale nitrate export patterns diverge, pointing at complementary drivers of catchment functioning across time scalesEvent‐scale C‐Q slopes decreased in variability with event magnitude and showed no signs of source limitation at high‐magnitude eventsMulti‐catchment high‐frequency data revealed the crucial role of time scale and event magnitude for interpreting nitrate export patterns [ABSTRACT FROM AUTHOR]

Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundations to accurately characterise extremes. This motivates the development of extreme value models for extreme event forecasting. In this paper, a dynamic extreme value model is proposed for forecasting volcanic eruptions. This is inspired by one recently introduced for financial risk forecasting with high-frequency data. Using a case study of the Piton de la Fournaise volcano, it is shown that the modelling framework is widely applicable, flexible and holds strong promise for natural hazard forecasting. The value of using EVT-informed thresholds to identify and model extreme events is shown through forecast performance, and considerations to account for the range of observed events are discussed. [ABSTRACT FROM AUTHOR]

We investigate the statistical evidence for the use of 'rough' fractional processes with Hurst exponent H < 0.5 for modeling the volatility of financial assets, using a model-free approach. We introduce a non-parametric method for estimating the roughness of a function based on discrete sample, using the concept of normalized p-th variation along a sequence of partitions. Detailed numerical experiments based on sample paths of fractional Brownian motion and other fractional processes reveal good finite sample performance of our estimator for measuring the roughness of sample paths of stochastic processes. We then apply this method to estimate the roughness of realized volatility signals based on high-frequency observations. Detailed numerical experiments based on stochastic volatility models show that, even when the instantaneous volatility has diffusive dynamics with the same roughness as Brownian motion, the realized volatility exhibits rough behaviour corresponding to a Hurst exponent significantly smaller than 0.5. Comparison of roughness estimates for realized and instantaneous volatility in fractional volatility models with different values of Hurst exponent shows that, irrespective of the roughness of the spot volatility process, realized volatility always exhibits 'rough' behaviour with an apparent Hurst index H ^ < 0.5 . These results suggest that the origin of the roughness observed in realized volatility time series lies in the estimation error rather than the volatility process itself. [ABSTRACT FROM AUTHOR]

Jumps and market microstructure noise are stylized features of high-frequency financial data. It is well known that they introduce bias in the estimation of volatility (including integrated and spot volatilities) of assets, and many methods have been proposed to deal with this problem. When the jumps are intensive with infinite variation, the efficient estimation of spot volatility under serially dependent noise is not available and is thus in need. For this purpose, we propose a novel estimator of spot volatility with a hybrid use of the pre-averaging technique and the empirical characteristic function. Under mild assumptions, the results of consistency and asymptotic normality of our estimator are established. Furthermore, we show that our estimator achieves an almost efficient convergence rate with optimal variance when the jumps are either less active or active with symmetric structure. Simulation studies verify our theoretical conclusions. We apply our proposed estimator to empirical analyses, such as estimating the weekly volatility curve using second-by-second transaction price data. [ABSTRACT FROM AUTHOR]

As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors' collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies' investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies. [ABSTRACT FROM AUTHOR]

Introduction: Realized volatility analysis of assets in the Brazilian market within a multivariate framework is the focus of this study. Despite the success of volatility models in univariate scenarios, challenges arise due to increasing dimensionality of covariance matrices and lower asset liquidity in emerging markets. Methods: In this study, we utilize intraday stock trading data from the Brazilian Market to compute daily covariance matrices using various specifications. To mitigate dimensionality issues in covariance matrix estimation, we implement penalization restrictions on coefficients through regressions with shrinkage techniques using Ridge, LASSO, or Elastic Net estimators. These techniques are employed to capture the dynamics of covariance matrices. Results: Comparison of covariance construction models is performed using the Model Confidence Set (MCS) algorithm, which selects the best models based on their predictive performance. The findings indicate that the method used for estimating the covariance matrix significantly impacts the selection of the best models. Additionally, it is observed that more liquid sectors demonstrate greater intra-sectoral dynamics. Discussion: While the results benefit from shrinkage techniques, the high correlation between assets presents challenges in capturing stock or sector idiosyncrasies. This suggests the need for further exploration and refinement of methods to better capture the complexities of volatility dynamics in emerging markets like Brazil. [ABSTRACT FROM AUTHOR]

This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place on that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, replicating real-world scenarios that could precipitate mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers. [ABSTRACT FROM AUTHOR]

This article extends the solution proposed by Aït-Sahalia, Fan, and Li for the leverage effect puzzle, which refers to a fact that empirical correlation between daily asset returns and the changes of daily volatility estimated from high frequency data is nearly zero. Complementing the analysis in Aït-Sahalia, Fan, and Li via the Heston model, we work with a generic semi-nonparametric stochastic volatility model via an operator-based expansion method. Under such a general setup, we identify a new source of bias due to the flexibility of variance dynamics, distinguishing the leverage effect parameter from the instantaneous correlation parameter. For estimating the leverage effect parameter, we show that the main results on analyzing the various sources of biases as well as the resulting statistical procedures for biases correction in Aït-Sahalia, Fan, and Li hold true and are thus indeed theoretically robust. For estimating the instantaneous correlation parameter, we developed a new nonparametric estimation method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We analyze the multiscale behaviors of high-frequency intraday prices, with a focus on how asset prices are correlated over different timescales. The multiscale approach proposed in this paper is designed for the analysis of high-frequency intraday prices. It incorporates microstructure noise into the stochastic price process. We consider a noisy fractional Brownian motion model and illustrate its various statistical properties. This leads us to introduce new latent correlation and noise estimators. New numerical algorithms are developed for model estimation using empirical high-frequency data. For a collection of stocks and exchange-traded funds, examples are provided to illustrate the relationship between multiscale correlation and sampling frequency as well as the evolution of multiscale correlation over time. [ABSTRACT FROM AUTHOR]

Groundwater level is the most important parameter in any study involving the evaluation, development and management of groundwater resources. Systematic monitoring of groundwater levels, which commenced with the establishment of the Central Ground Water Board (CGWB), has been of immense use in addressing several challenges like prioritization of areas for groundwater recharge, delineating areas prone to waterlogging, estimation of storage change in the aquifers, estimation of groundwater flow, etc. In a major boost to strengthen groundwater monitoring in the country, the Government of India has sanctioned a special project under which CGWB has envisaged to construct 9000 purpose-built wells (piezometers) in identified priority areas, which will be equipped with digital water-level recorders (DWLRs) and telemetry devices for acquisition and transmission of groundwater levels at increased frequency. The intended uses of the long-term high-frequency data include monitoring short-term and long-term changes in the groundwater levels, groundwater storage and recharge to the aquifers, monitoring the effects of climatic variability, estimating transboundary flow, assessing regional effects of groundwater development, quantifying impacts of water conservation and artificial recharge projects, and improved understanding of groundwater and surface water interactions. High-frequency groundwater level data also have the potential for steering multiinstitutional collaborative research projects in the country, particularly for studying the impact of groundwater extraction on land subsidence, the relationship between groundwater levels and tectonic disturbances, and climate change impacts on the groundwater regime. [ABSTRACT FROM AUTHOR]

The portmanteau test is an effective tool for testing the goodness of fit of models. Motivated by the fact that high-frequency data can improve the estimation accuracy of models, a modified portmanteau test using high-frequency data is proposed for ARCH-type models in this paper. Simulation results show that the empirical size and power of the modified test statistics of the model using high-frequency data are better than those of the daily model. Three stock indices (CSI 300, SSE 50, CSI 500) are taken as an example to illustrate the practical application of the test. [ABSTRACT FROM AUTHOR]