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We studied the causal link between forage condition and food security in northern Kenya and southern Ethiopia and probed the mechanisms through which the effects occur. The study utilized weekly panel survey data collected over a period of 94 weeks (March 2021–December 2022) from drought-prone pastoral regions and estimated two-stage least squares instrumental variables regression to assess causal impacts. We found that an increase in the normalized difference vegetation index (NDVI) – our proxy for forage condition – by 10% significantly reduced the likelihood of a household experiencing food insecurity by 12 percentage points. The main mechanisms through which improved forage condition increased food security were livestock productivity enhancement, reduced prices of basic food commodities (cereals, legumes, and vegetables), and utilization of better coping strategies by households. These findings suggest the need for interventions and initiatives aimed at boosting livestock productivity and reducing vulnerability to drought-induced poor forage conditions. Efforts focused on the monitoring of forage conditions and supporting market development for livestock feed are important for addressing the adverse impacts of drought and deteriorating forage conditions in the horn of Africa. Furthermore, the monitoring of drought conditions and shocks using high-frequency data has the potential for providing early warning and informing anticipatory action. [ABSTRACT FROM AUTHOR]

We examine the effects of US, German, and Japanese macroeconomic news surprises and monetary policy decisions on the intraday cyclical volatility of Bitcoin and Ethereum markets. We first document intraday seasonality specific to each day of the week and show that these patterns exhibit a slightly different volatility compared to all-day seasonality. Second, the US monetary policy news and macroeconomic surprises generate the largest effect on the seasonal volatility. Third, Ethereum seasonality is more sensitive to macroeconomic fundamentals compared to Bitcoin. These results suggest that to improve cryptocurrency pricing, portfolio management, and risk management practices associated with cryptocurrency transactions, investors should consider the interactions between day of the week effects, intraday seasonality patterns and the macroeconomics news releases. [ABSTRACT FROM AUTHOR]

We study the role of co-jumps in the interest rate futures markets. To disentangle continuous part of quadratic covariation from co-jumps, we localize the co-jumps precisely through wavelet coefficients and identify statistically significant ones. Using high frequency data about U.S. and European yield curves we quantify the effect of co-jumps on their correlation structure. Empirical findings reveal much stronger co-jumping behavior of the U.S. yield curves in comparison to the European one. Further, we connect co-jumping behavior to the monetary policy announcements, and study effect of 103 FOMC and 119 ECB announcements on the identified co-jumps during the period from January 2007 to December 2017. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) are a significant threat to freshwater ecosystems, and monitoring for changes in biomass is therefore important. Fluorescence in-situ sensors enable rapid and high frequency real-time data collection and have been widely used to determine chlorophyll-a (Chla) concentrations that are used as an indicator of the total algal biomass. However, conversion of fluorescence to equivalent Chla concentrations is often complicated due to biofouling, phytoplankton composition and the type of equipment used. Here, we validated measurements from 24 Chla and 12 phycocyanin (cyanobacteria indicator) fluorescence in-situ sensors (Cyclops-7F, Turner Designs) against spectrophotometrically (in-vitro) determined Chla and tested a data-cleaning procedure for eliminating data errors and impacts of non-photochemical quenching. The test was done across a range of freshwater plankton communities in 24 mesocosms (i.e. experimental tanks) with a 2x3 (high and low nutrient x ambient, IPCC-A2 and IPCC-A2+50% temperature scenarios) factorial design. For most mesocosms (tanks), we found accurate (r2 ≥ 0.7) calibration of in-situ Chla fluorescence data using simple linear regression. An exception was tanks with high in-situ phycocyanin fluorescence, for which multiple regressions were employed, which increased the explained variance by >16%. Another exception was the low Chla concentration tanks (r2 < 0.3). Our results also show that the high frequency in-situ fluorescence data recorded the timing of sudden Chla variations, while less frequent in-vitro sampling sometimes missed these or, when recorded, the duration of changes was inaccurately determined. Fluorescence in-situ sensors are particularly useful to detect and quantify sudden phytoplankton biomass variations through high frequency measurements, especially when using appropriate data-cleaning methods and accounting for factors that can impact the fluorescence readings. Nevertheless, corroborating these data with in-vitro Chla assessments would provide additional validation for the early warnings provided by sensor data.

Advancements in data availability, including high frequency, near real-time multiparameter sensors, laboratory analysis, and in-situ and remote observations, have driven the development of machine learning (ML) models for applications such as toxic Harmful Algal Bloom (HABs) monitoring. However, the performance of ML predictions is influenced by both model uncertainties due to inherent model structures and errors associated with input dataset measurements. For example, measurement uncertainty arises from sample collection, sensor drift and laboratory analysis and sample handling errors. While impacts of model uncertainty are commonly addressed using probabilistic approaches, the effect of measurement uncertainty is less studied due to the limited availability of detailed measurement information. This study focuses on assessing the impact of measurement uncertainty on the ML prediction of chlorophyll-a concentration as an index of HABs in a mesotrophic lake. Using randomized subsets of input measured datasets that mimic possible chlorophyll-a concentration distributions, the study built 1000 Random Forest (RF) and Support Vector Regression (SVR) models. An independent measured dataset was used to validate the ensemble models, allowing for model performance evaluation and the creation of prediction intervals to measure the propagated uncertainty. Our findings showed that the model predictions have MAE that ranged between 0.16 μg/l and 5.19 μg/l, and RMSE ranging between 0.20 μg/l and 7.39 μg/l. The highest uncertainty coverage of 0.71 was observed in the RF model without chlorophyll-a sensor values as a predictor. The study found that the training dataset sizes due to the high frequency and manually sampled nature influence how much measurement uncertainty is covered. The results of this study demonstrate how well ML models can capture various HABs patterns when given diverse measurement variables. Our findings will give researchers insightful information on how to lessen the impact of measurement uncertainty when using ML models as decision-support tools for HABs management.

A novel theoretical result on estimation of the local time and the occupation time measure of an α-stable Lévy process with $\alpha \in (1,2)$ is presented. The approach is based upon computing the conditional expectation of the desired quantities given high frequency data, which is an ${L^{2}}$-optimal statistic by construction. The corresponding stable central limit theorems are proved and a statistical application is discussed. In particular, this work extends the results of [20], which investigated the case of the Brownian motion.

We study parameter estimation for a linear parabolic second-order stochastic partial differential equation (SPDE) in two space dimensions with a small dispersion parameter using high frequency data with respect to time and space. We set two types of Q-Wiener processes as a driving noise. We provide minimum contrast estimators of the coefficient parameters of the SPDE appearing in the eigenfunctions of the differential operator of the SPDE based on the thinned data in space, and approximate the coordinate process based on the thinned data in time. Moreover, we propose an estimator of the drift parameter using the fact that the coordinate process is the Ornstein-Uhlenbeck process and statistical inference for diffusion processes with a small noise. We also give an example and simulation results for the proposed estimators. [ABSTRACT FROM AUTHOR]

A novel theoretical result on estimation of the local time and the occupation time measure of an α-stable Lévy process with α ∈ (1, 2) is presented. The approach is based upon computing the conditional expectation of the desired quantities given high frequency data, which is an L²-optimal statistic by construction. The corresponding stable central limit theorems are proved and a statistical application is discussed. In particular, this work extends the results of [20], which investigated the case of the Brownian motion. [ABSTRACT FROM AUTHOR]

The price and return time series are two distinct features of any financial asset. Hence, examining the evolution of multiscale characteristics of price and returns sequential data in time domain would be helpful in gaining a better understanding of the dynamical evolution mechanism of the financial asset as a complex system. In fact, this is important to understand their respective dynamics and to design their appropriate predictive models. The main purpose of the current work is to investigate the multiscale fractals of price and return high frequency data in Turkish stock market. In this regard, the wavelet leaders computational method is applied to each high frequency data to reveal its multi-fractal behavior. In particular, the method is applied to a large set of Turkish stocks and statistical results are performed to check for (i) presence of multi-fractals in price and return series and (ii) differences between prices and returns in terms of multi-fractals. Our statistical results show strong evidence that high frequency price and return data exhibit multi-fractal dynamics. In addition, they show evidence of distinct fractal characteristics on different scales between price and return series. Furthermore, our statistical results show evidence of differences in local fluctuation characteristics of price and return time series. Therefore, differences in local characteristics are useful to build specific predictive models for each type of data for better modeling and prediction to generate profits. Besides, we found evidence that both long-range correlations and fat-tail distributions contribute to the multifractality in Turkish stocks. This finding can be attributed to the major role played by international investors in increasing the volatility of Turkish stocks. [ABSTRACT FROM AUTHOR]

Rounding is a pivotal source of market microstructure noise which should be carefully addressed in high-frequency data analysis. This study incorporates available market information in modeling rounding errors and proposes a Rounding Error-Trading Information model from the Bayesian perspective. We assign a thresholded Gaussian process prior to the instantaneous volatility of the log-price process and adopt a fully Bayesian approach with an efficient Markov chain Monte Carlo algorithm for model inference, based on which a novel Trading Information-based estimator for the integrated volatility is provided. Simulation studies show that the proposed method can effectively recover the true latent log-prices, instantaneous volatility, and integrated volatility with multiple volatility shapes and rounding mechanisms, even under model misspecification. Extensive empirical studies show that the rounding mechanism in the Shanghai A-share market is likely to be random and the trading directions have a greater impact on the rounding results of the asset prices than the true latent log-prices, which is a consistent finding with that in the literature. [ABSTRACT FROM AUTHOR]

As hotspots of the global carbon cycle, lakes can regulate climate change while being regulated by it via a feedback loop. As ecosystem metabolism is considered to be a sentinel to these changes, we used high-frequency measurements and the inverse modeling approach to study the temporal dynamic of gross primary production (GPP), respiration (R), and net ecosystem production (NEP) at surface waters of a tropical lake, which lost about 60% of its volume in almost 10 years. This water loss and the return of the average rainfall led to an increased concentration of nitrogen, phosphorus, and dissolved organic carbon and decreased water transparency and light availability. During the post-drought period, epilimnetic GPP increased by 40% compared with the pre-drought period, and R surpassed GPP during the mixing period of the lake. We discuss that this modification results from higher carbon and nutrient concentrations, lower light availability, and a change in the lake stratification pattern. Surface water metabolism changed due to meteorological and hydrological drought, and it may continue to change under the ongoing variation in rainfall patterns, temperature, and human disturbances.

We show that the realized GARCH model yields closed-form expression for both the volatility index (VIX) and the volatility risk premium (VRP). The realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the realized GARCH model significantly outperforms conventional GARCH models. [ABSTRACT FROM AUTHOR]

We study recurrent patterns in volatility and volume for major cryptocurrencies, Bitcoin and Ether, using data from two centralized exchanges (CEXs; Coinbase Pro and Binance) and a decentralized exchange (DEX; Uniswap V2). We find systematic patterns in both volatility and volume across day-of-the-week, hour-of-the-day, and within the hour. These patterns have grown stronger over the years and are presumably related to algorithmic trading and funding times in futures markets. We also document that price formation mainly takes place on the CEXs while price adjustments on the DEXs can be sluggish. [ABSTRACT FROM AUTHOR]

We consider parameter estimation for a linear parabolic second‐order stochastic partial differential equation (SPDE) in two space dimensions driven by two types of Q$$ Q $$‐Wiener processes based on high frequency data in time and space. We first estimate the parameters which appear in the eigenfunctions of the differential operator of the SPDE using the minimum contrast estimator based on the thinned data with respect to space, and then construct an approximate coordinate process of the SPDE. Furthermore, we propose estimators of the coefficient parameters of the SPDE utilizing the approximate coordinate process based on the thinned data with respect to time. We also give some simulation results. [ABSTRACT FROM AUTHOR]

This study investigates the extent to which intraday price jumps and co-jumps in cryptocurrency markets stem from the release of macroeconomic news from the U.S., Germany and Japan. Using 5-min frequency prices for Bitcoin and Ethereum quoted against the U.S. dollar over the 2016–2019 period, we find that intraday price jumps are three times more frequent in Ethereum than in Bitcoin. More importantly, we show that jumps in Ethereum are more sensitive to macroeconomic news than jumps in Bitcoin, and that U.S. news releases exhibit higher influence on jumps in both cryptocurrencies than German and Japanese news announcements. We also find that co-jumps among Bitcoin and Ethereum are scarce and tend be associated only with a few U.S. news announcements, and in particular those related to the unemployment rate, new home sales, housing starts and Fed Beige Book. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) are a significant threat to freshwater ecosystems, and monitoring for changes in biomass is therefore important. Fluorescence in-situ sensors enable rapid and high frequency real-time data collection and have been widely used to determine chlorophyll-a (Chla) concentrations that are used as an indicator of the total algal biomass. However, conversion of fluorescence to equivalent Chla concentrations is often complicated due to biofouling, phytoplankton composition and the type of equipment used. Here, we validated measurements from 24 Chla and 12 phycocyanin (cyanobacteria indicator) fluorescence in-situ sensors (Cyclops-7F, Turner Designs) against spectrophotometrically (in-vitro) determined Chla and tested a data-cleaning procedure for eliminating data errors and impacts of non-photochemical quenching. The test was done across a range of freshwater plankton communities in 24 mesocosms (i.e. experimental tanks) with a 2x3 (high and low nutrient x ambient, IPCC-A2 and IPCC-A2+50% temperature scenarios) factorial design. For most mesocosms (tanks), we found accurate (r2 ≥ 0.7) calibration of in-situ Chla fluorescence data using simple linear regression. An exception was tanks with high in-situ phycocyanin fluorescence, for which multiple regressions were employed, which increased the explained variance by >16%. Another exception was the low Chla concentration tanks (r2 < 0.3). Our results also show that the high frequency in-situ fluorescence data recorded the timing of sudden Chla variations, while less frequent in-vitro sampling sometimes missed these or, when recorded, the duration of changes was inaccurately determined. Fluorescence in-situ sensors are particularly useful to detect and quantify sudden phytoplankton biomass variations through high frequency measurements, especially when using appropriate data-cleaning methods and accounting for factors that can impact the fluorescence readings.

Salt marshes remove terrestrially derived nutrients en route to coasts. While these systems play a critical role in improving water quality, we still have a limited understanding of the spatiotemporal variability of biogeochemically reactive solutes and processes within salt marshes. We implemented a high‐frequency sampling system to monitor sub‐hourly nitrate (NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$) concentrations in salt marsh porewater at Elkhorn Slough in central California, USA. We instrumented three marsh positions along an elevation gradient subjected to different amounts of tidal inundation, which we predicted would lead to varied biogeochemical characteristics and hydrological interactions. At each marsh position, we continuously monitored porewater NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$ concentrations at depths of 10, 30, and 50 cm and porewater levels measured at 70 cm depth over seven deployments of ~10 days each that spanned seasonal wet/dry periods common to Mediterranean climates. We quantified tidal event hysteresis between NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$ and water level to understand how NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$ concentrations and sources fluctuate across tidal cycles. In dry periods, the NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$‐porewater level relationship indicated that the NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$ source was likely estuarine surface water that flooded the transect during high tides and the salt marsh was a NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$ sink. In wet periods, the NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$‐porewater level relationship suggested the salt marsh was a source of NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$. These findings suggest that tidal and seasonal hydrologic fluxes together control NO3−$$ \mathrm{N}{\mathrm{O}}_3^{-} $$ porewater dynamics and export and influence ecological processes in coastal environments. [ABSTRACT FROM AUTHOR]

Abstract This paper deals with the rate of convergence for the central limit theorem of estimators of the drift coefficient, denoted θ, for the Ornstein-Uhlenbeck process X : = { X t , t ≥ 0 } $X := \{X_{t},t\geq 0\}$ observed at high frequency. We provide an approximate minimum contrast estimator and an approximate maximum likelihood estimator of θ, namely θ ˜ n : = 1 / ( 2 n ∑ i = 1 n X t i 2 ) $\widetilde{\theta}_{n}:= {1}/{ (\frac{2}{n} \sum_{i=1}^{n}X_{t_{i}}^{2} )}$ , and θ ˆ n : = − ∑ i = 1 n X t i − 1 ( X t i − X t i − 1 ) / ( Δ n ∑ i = 1 n X t i − 1 2 ) $\widehat{\theta}_{n}:= -{\sum_{i=1}^{n} X_{t_{i-1}} (X_{t_{i}}-X_{t_{i-1}} )}/{ (\Delta _{n} \sum_{i=1}^{n} X_{t_{i-1}}^{2} )}$ , respectively, where t i = i Δ n $t_{i} = i \Delta _{n}$ , i = 0 , 1 , … , n $i=0,1,\ldots , n $ , Δ n → 0 $\Delta _{n}\rightarrow 0$ . We provide Wasserstein bounds in the central limit theorem for θ ˜ n $\widetilde{\theta}_{n}$ and θ ˆ n $\widehat{\theta}_{n}$ .

The rapid popularization of advanced metering infrastructure (AMI) smart meters produces customer high-frequency energy consumption data. These data provide diverse options for energy economics and policy research. In this review, we examine studies applying high frequency smart meter data to explore the overall impact of household new technology adoption and COVID-19 on energy consumption patterns. We find that high frequency smart meter data boosts the accuracy of forecasting models with various data-driven algorithms. In addition, there is a lack of precise assessment and inclusive understanding of energy poverty in advanced economics. Smart meter data help expand and deepen the energy poverty research. Research on how vulnerable groups exhibit energy poverty can improve society’s understanding of energy poverty and help implement related policy assistance programs. [ABSTRACT FROM AUTHOR]

Oxygen (O2) regulates soil reduction‐oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O2 variability have prompted the use of soil moisture as a proxy for O2, as O2 diffusion into soil water is much slower than in soil air. The use of soil moisture alone as a proxy measurement for O2 could result in inaccurate O2 estimations. For example, O2 may remain high during cool months when soil respiration rates are low. We analyzed high‐frequency sensor data (e.g., soil moisture, CO2, gas‐phase soil pore O2) with a machine learning technique, the Self‐Organizing Map, to pinpoint suites of soil conditions associated with contrasting O2 regimes. At two riparian sites in northern Vermont, we found that O2 levels varied seasonally, and with soil moisture. For example, 47% of low O2 levels were associated with wet and cool soil conditions, whereas 32% were associated with dry and warm conditions. Contrastingly, the majority (62%) of high O2 conditions occurred under dry and warm conditions. High soil moisture levels did not always lead to low O2, as 38% of high O2 values occurred under wet and cool conditions. Our results highlight challenges with predicting soil O2 solely based on water content, as variable combinations of soil and hydrologic conditions can complicate the relationship between water content and O2. This indicates that process‐based ecosystem and denitrification models that rely solely on soil moisture to estimate O2 may need to incorporate other site and climate‐specific drivers to accurately predict soil O2. Plain Language Summary: Oxygen in soil is an important variable to monitor, as it affects how nutrients, like nitrogen and carbon, cycle through the soil. However, oxygen is difficult to measure in soil, since it's spatially and temporally variable. Based on the general rule that soil oxygen decreases as soil moisture increases, moisture is sometimes used to approximate soil oxygen levels. In nature, however, the relationship between these variables is not simple, and sometimes oxygen behaves in a way that is the exact opposite of this general rule. Using specialized soil sensors that record data at 15‐minute intervals, we monitored gas‐phase soil pore oxygen, soil moisture, and other soil conditions to better understand why these unexpected soil oxygen levels occur. We found that most high oxygen levels occurred during warm months, when soils were driest, and that most low oxygen levels occurred during cool months, when soils were wettest. However, we found that 38% of high oxygen levels occurred under conditions that, according to the general rule, should have resulted in low oxygen. We think these unexpected oxygen levels are caused by complex interactions between soil processes that produce and consume oxygen in soil, and we therefore caution against using the general rule to predict oxygen. Key Points: Soil water is used as a proxy for oxygen, which may lead to incorrect oxygen predictions (e.g., oxygen remains high under high moisture)A machine learning technique, the Self‐Organizing Map, was used to assess the relationship between soil moisture and oxygen in riparian soilSoil and hydrologic conditions complicated the relationship between soil water content and oxygen, which led to anomalous oxygen conditions [ABSTRACT FROM AUTHOR]

This paper deals with the rate of convergence for the central limit theorem of estimators of the drift coefficient, denoted θ, for the Ornstein-Uhlenbeck process X : = { X t , t ≥ 0 } observed at high frequency. We provide an approximate minimum contrast estimator and an approximate maximum likelihood estimator of θ, namely θ ˜ n : = 1 / (2 n ∑ i = 1 n X t i 2) , and θ ˆ n : = − ∑ i = 1 n X t i − 1 (X t i − X t i − 1 ) / (Δ n ∑ i = 1 n X t i − 1 2) , respectively, where t i = i Δ n , i = 0 , 1 , ... , n , Δ n → 0 . We provide Wasserstein bounds in the central limit theorem for θ ˜ n and θ ˆ n . [ABSTRACT FROM AUTHOR]

We deal with the change point problem in ergodic diffusion processes based on high frequency data. Tonaki et al. [12,13] studied the change point problem for the ergodic diffusion process model. However, the change point problem for the drift parameter when the diffusion parameter changes is still open. Therefore, we consider the change detection and the change point estimation for the drift parameter taking into account that there is a change point in the diffusion parameter. Moreover, we examine the performance of the tests and the estimation with numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic high-frequency dependence structure of Chinese four major agricultural commodity futures by utilizing a semi-parametric copula-based multivariate model with 5-minute high-frequency trading data. The empirical results show that the daily dependence between the agricultural commodity futures is time-varying and slightly asymmetric, and that this dependence and its asymmetry are more pronounced during the world food crisis (2007–2008) and the global financial crisis (2008–2011). Furthermore, the intraday dependence structure exhibits a lopsided inverted U-shaped pattern with relatively lower dependence level around the opening and closing time, and a peak around the mid-trading day.

In this paper, based on the Realized GARCH model, the fractional integration Realized GARCH model is proposed by combining long memory parameters with conditional variance and replacing the original realized measure with the realized measure obtained after daily, weekly and monthly weighting. Based on the 5-min high-frequency data of the SSE index, the fractional integration Realized GARCH model, Realized HAR GARCH model and Realized GARCH model are investigated for their fitting effect and predictive ability on market volatility, and Monte Carlo simulations are conducted from the error terms obeying normal distribution, t-distribution and chi-square distribution so as to compare the RMSE and MAE of the three types of models with respect to conditional variance. The empirical results show that the fractionally integrated Realized GARCH model is found to better capture the long-run correlation in volatility in certain intervals by comparing the theoretical and sample auto-correlation functions, while the overall predictive power of the model is better than the other two models. Finally, it provides technical support and suggestions for investors' risk control.

Forecasting exchange rates is a complex problem that has benefitted from recent advances and research in machine learning. The main goal of this study is to design and implement a method to improve the learning performance of artificial neural networks with large volumes of data using population-based metaheuristics. The micro-genetic training algorithm is thoroughly analyzed using profiling tools to find bottlenecks. We compare the use of a micro-genetic algorithm to predict changes in currency exchange rates on a data set containing more than 500,000 values. To find the best parameters of neural networks, we propose an improved micro-genetic training algorithm by dividing the training data into mini batches. In this case, the improved micro-genetic algorithm proved to be much faster compared to the standard genetic algorithm, while achieving the same prediction accuracy. This allows for the use of this algorithm for just-in-time predictions of high frequency data. Here, neural network models are first created and validated on an existing data set. Then, the new data values can be added to neural network models and retrained in a short time. [ABSTRACT FROM AUTHOR]

We contrast the degree (strong vs. weak), nature (interaction between more and less informed traders; MI and LI, respectively), and patterns of herding behavior (via their feedback strategies) among MI and LI traders and their speed of reaction to shifts in trading by these groups. This is achieved by analyzing individual investment records of 1,943 traders in UK spread-trading markets (2010-2012). We find that herding is far more prevalent than previous studies suggest, particularly among LI; herding activities of MI and LI are related, and the means used to distinguish MI and LI needs to be considered carefully. [ABSTRACT FROM AUTHOR]

Advancements in data availability, including high frequency, near real-time multiparameter sensors, laboratory analysis, and in-situ and remote observations, have driven the development of machine learning (ML) models for applications such as toxic Harmful Algal Bloom (HABs) monitoring. However, the performance of ML predictions is influenced by both model uncertainties due to inherent model structures and errors associated with input dataset measurements. For example, measurement uncertainty arises from sample collection, sensor drift and laboratory analysis and sample handling errors. While impacts of model uncertainty are commonly addressed using probabilistic approaches, the effect of measurement uncertainty is less studied due to the limited availability of detailed measurement information. This study focuses on assessing the impact of measurement uncertainty on the ML prediction of chlorophyll-a concentration as an index of HABs in a mesotrophic lake. Using randomized subsets of input measured datasets that mimic possible chlorophyll-a concentration distributions, the study built 1000 Random Forest (RF) and Support Vector Regression (SVR) models. An independent measured dataset was used to validate the ensemble models, allowing for model performance evaluation and the creation of prediction intervals to measure the propagated uncertainty. Our findings showed that the model predictions have MAE that ranged between 0.16 μg/l and 5.19 μg/l, and RMSE ranging between 0.20 μg/l and 7.39 μg/l. The highest uncertainty coverage of 0.71 was observed in the RF model without chlorophyll-a sensor values as a predictor. The study found that the training dataset sizes due to the high frequency and manually sampled nature influence how much measurement uncertainty is covered. The results of this study demonstrate how well ML models can capture various HABs patterns when given diverse measurement variables. Our findings will give researchers insightful information on how to lessen the impact of measurement uncertainty when using ML models as decision-support tools for HABs management. [Display omitted] • ML prediction of HABs is affected by errors in measured water quality datasets. • Chlorophyll-a prediction as index of HABs was assessed using subsets of measured data. • Ensemble RF and SVR models were used to generate chlorophyll-a prediction intervals. • Variation in datasets influences the ability of HABs ML models to reduce measurement uncertainty effect. [ABSTRACT FROM AUTHOR]

This article tests volatility jumps based on the high frequency data. Under the null hypothesis that the volatility process is a continuous semimartingale, our test statistic converges to a normal distribution, and under the alternative hypothesis where the volatility has jumps, the statistic diverges to infinity. Compared to the test statistic of Bibinger et al. (Bibinger et al. (2017). Annals of Statistics 45, 1542–1578), our proposed statistic diverges to infinity at a faster rate, and has a better power. Simulation studies confirm the theoretical results, and an empirical analysis shows that some real financial data possess volatility jumps. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) are a significant threat to freshwater ecosystems, and monitoring for changes in biomass is therefore important. Fluorescence in-situ sensors enable rapid and high frequency real-time data collection and have been widely used to determine chlorophyll- a (Chla) concentrations that are used as an indicator of the total algal biomass. However, conversion of fluorescence to equivalent Chla concentrations is often complicated due to biofouling, phytoplankton composition and the type of equipment used. Here, we validated measurements from 24 Chla and 12 phycocyanin (cyanobacteria indicator) fluorescence in-situ sensors (Cyclops-7F, Turner Designs) against spectrophotometrically (in-vitro) determined Chla and tested a data-cleaning procedure for eliminating data errors and impacts of non-photochemical quenching. The test was done across a range of freshwater plankton communities in 24 mesocosms (i.e. experimental tanks) with a 2x3 (high and low nutrient x ambient, IPCC-A2 and IPCC-A2+50% temperature scenarios) factorial design. For most mesocosms (tanks), we found accurate (r 2 ≥ 0.7) calibration of in-situ Chla fluorescence data using simple linear regression. An exception was tanks with high in-situ phycocyanin fluorescence, for which multiple regressions were employed, which increased the explained variance by >16%. Another exception was the low Chla concentration tanks (r 2 < 0.3). Our results also show that the high frequency in-situ fluorescence data recorded the timing of sudden Chla variations, while less frequent in-vitro sampling sometimes missed these or, when recorded, the duration of changes was inaccurately determined. Fluorescence in-situ sensors are particularly useful to detect and quantify sudden phytoplankton biomass variations through high frequency measurements, especially when using appropriate data-cleaning methods and accounting for factors that can impact the fluorescence readings. Nevertheless, corroborating these data with in-vitro Chla assessments would provide additional validation for the early warnings provided by sensor data., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Levi EE et al.)

In view of the magnitude and the sudden nature of the health crisis in 2020, economists and statisticians have explored new sources of data to describe the development of the financial situation of households. The bank data used in this study, an anonymised panel of La Banque Postale customers, offer the twofold advantage of being able to be used practically in real time and of recording monthly (or even daily) changes in income, spending and wealth. First, we show that while the crisis affected incomes in a limited and temporary way in 2020 for most households (regardless of income level), populations on the margins of the labour market suffered more. We then specifically study the situation of recipients of the revenu de solidarité active (RSA), the French guaranteed minimum income. Although their social security benefits did not decrease in 2020, their incomes increased less than in a normal year because more of them did not return to employment. The exceptional government support paid out in May and November 2020 only partially counterbalanced these lower employment opportunities. [ABSTRACT FROM AUTHOR]

The volatility is a measure for the uncertainty of an asset's return and is used to reflect the risk level of a financial asset. In this article, we consider the double kernel nonparametric estimator for the volatility function in a diffusion model over a finite-time span based on high frequency sampling data. Under the minimum conditions, the asymptotic mixed normality for the underlying estimator is derived. Moreover, the better finite-sample performance as variance reduction and even mean squared error reduction of the proposed estimator is verified through a Monte Carlo simulation study and an empirical analysis on overnight Shibor in China. [ABSTRACT FROM AUTHOR]

Information and communication technology have enabled the collection of high-frequency financial asset time series data. However, the high spatial and temporal resolution nature of these data makes it challenging to compare financial asset characteristics patterns and identify the risk. To address this challenge, a method for realized volatility calculation based on the functional data analysis (FDA) method is proposed. A time–price functional curve is constructed by the functional data analysis method to calculate the realized volatility as the curvature integral of the time–price functional curve. This method could effectively eliminate the interference of market microstructure noise, which could not only allow capital asset price to be decomposed into a continuous term and a noise term by asymptotic convergence, but also could decouple the noise from the discrete-time series. Additionally, it could obtain the value of volatility at any given time, which is no concern about correlations between repeated, mixed frequencies and unequal intervals sampling problems and relaxes the structural constraints and distribution setting of data acquisition. To demonstrate our methods, we analyze a per-second level financial asset dataset. Additionally, sensitivity analysis on the selection of the no equally spaced sample is conducted, and we further add noise to ensure the robustness of our methods and discuss their implications in practice, especially being conducive to more micro analysis of the volatility of the financial market and understanding the rapidly changing changes. [ABSTRACT FROM AUTHOR]

We study a class of nonparametric volatility estimators based on the Laplace transform, which are robust to the presence of the endogeneity of observation times. Asymptotic properties and feasible central limit theorems are established. In the presence of time endogeneity, our bias-corrected Laplace estimator takes advantage of the informational content of time endogeneity, which leads to narrower confidence bounds. The finite sample properties of the estimator are studied through Monte Carlo simulations. Through the simulation study, we also find that due to the presence of the kernel, Laplace estimator could be adopted in a model with microstructure noise. The performance of the Laplace estimator is compared with other commonly used estimators through forecasting exercises by employing high frequency data. We conclude that the bias-corrected Laplace estimator performs better than most estimators in terms of forecasting equity return volatility in the presence of both time endogeneity and market microstructure noise. [ABSTRACT FROM AUTHOR]

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Due to increasing speculation, crude oil futures are now becoming one of the highest traded commodities. This paper studies the relationship between trading volume and serial correlation in crude oil futures returns using high frequency data. We find that volume can positively predict the serial correlation in the short run (within an hour) but negatively predict the serial correlation in the midterm. The trading volume is not able to consistently predict serial correlation in the long run (more than a day). The results from our empirical studies are robust to a variety of controls and our study gives a new insight in the relation between volume and serial correlation of crude oil futures returns. [ABSTRACT FROM AUTHOR]

In the light of micro-scale inefficiencies due to the highly fragmented bitcoin trading landscape, we use a granular data set comprising orderbook and trades data from the most liquid bitcoin markets, to understand the price formation process at sub-1-second time scales. To this end, we construct a set of features that encapsulate relevant microstructural information over short lookback windows. These features are subsequently leveraged, first to generate a leader–lagger network that quantifies how markets impact one another, and then to train linear models capable of explaining between 10% and 37% of total variation in 500 ms future returns (depending on which market is the prediction target). The results are then compared with those of various PnL calculations that take trading realities, such as transaction costs, into account. The PnL calculations are based on natural taker strategies (meaning they employ market orders) associated with each model. Our findings emphasize the role of a market's fee regime in determining both its propensity to lead or lag, and the profitability of our taker strategy. We further derive a natural maker strategy (using only passive limit orders) which, due to the difficulties associated with backtesting maker strategies, we test in a real-world live trading experiment, in which we turned over 1.5 M USD in notional volume. Lending additional confidence to our models, and by extension to the features they are based on, the results indicate a significant improvement over a naive benchmark strategy, which we also deploy in a live trading environment with real capital, for the sake of comparison. [ABSTRACT FROM AUTHOR]

Forecasting exchange rates is a complex problem that has benefitted from recent advances and research in machine learning. The main goal of this study is to design and implement a method to improve the learning performance of artificial neural networks with large volumes of data using population-based metaheuristics. The micro-genetic training algorithm is thoroughly analyzed using profiling tools to find bottlenecks. We compare the use of a micro-genetic algorithm to predict changes in currency exchange rates on a data set containing more than 500,000 values. To find the best parameters of neural networks, we propose an improved micro-genetic training algorithm by dividing the training data into mini batches. In this case, the improved micro-genetic algorithm proved to be much faster compared to the standard genetic algorithm, while achieving the same prediction accuracy. This allows for the use of this algorithm for just-in-time predictions of high frequency data. Here, neural network models are first created and validated on an existing data set. Then, the new data values can be added to neural network models and retrained in a short time.

Information and communication technology have enabled the collection of high-frequency financial asset time series data. However, the high spatial and temporal resolution nature of these data makes it challenging to compare financial asset characteristics patterns and identify the risk. To address this challenge, a method for realized volatility calculation based on the functional data analysis (FDA) method is proposed. A time–price functional curve is constructed by the functional data analysis method to calculate the realized volatility as the curvature integral of the time–price functional curve. This method could effectively eliminate the interference of market microstructure noise, which could not only allow capital asset price to be decomposed into a continuous term and a noise term by asymptotic convergence, but also could decouple the noise from the discrete-time series. Additionally, it could obtain the value of volatility at any given time, which is no concern about correlations between repeated, mixed frequencies and unequal intervals sampling problems and relaxes the structural constraints and distribution setting of data acquisition. To demonstrate our methods, we analyze a per-second level financial asset dataset. Additionally, sensitivity analysis on the selection of the no equally spaced sample is conducted, and we further add noise to ensure the robustness of our methods and discuss their implications in practice, especially being conducive to more micro analysis of the volatility of the financial market and understanding the rapidly changing changes.

The promising empirical results presented using high-frequency data show that the log-volatility behaves essentially as a fractional Brownian motion (fBm) with a Hurst exponent smaller than 0.5. Motivated by these findings, we propose the autoregressive rough volatility (ARRV) model, which combines the fractional Gaussian noise (fGn) process and time series models to forecast volatility. We apply this model to the VIX index by adopting the fBm approximation technique, and our results indicate that the ARRV model can significantly improve VIX out-of-sample forecast accuracy, particularly during turbulent times. • Built the ARRV model combining the time series and fractional Gaussian noise to forecast volatility. • The ARRV model has a good micro-structural foundation to leverage effect at larger time scales. • The ARRV model can significantly improve the VIX forecast accuracy than the AR and ARFIMA models. • The ARRV model has better performance especially during turbulent times. [ABSTRACT FROM AUTHOR]