Intraday Price Research Articles

Detection and localization of changes in a panel of densities

We propose a new methodology for identifying and localizing changes in the Fréchet mean of a multivariate time series of probability densities. The functional data objects we study are random densities fs,t indexed by discrete time t and a vector component s, which can be treated as a broadly understood spatial location. Our main objective is to identify the set of components s, where a change occurs with statistical certainty. A challenge of this analysis is that the densities fs,t are not directly observable and must be estimated from sparse and potentially imbalanced data. Such setups are motivated by the analysis of two data sets that we investigate in this work. First, a hitherto unpublished large data set of Brazilian Covid infections and a second, a financial data set derived from intraday prices of U.S. Exchange Traded Funds. Chief statistical advances are the development of change point tests and estimators of components of change for multivariate time series of densities. We prove the theoretical validity of our methodology and investigate its finite sample performance in a simulation study.

A common shock model for multidimensional electricity intraday price modelling with application to battery valuation

In this paper, we propose a multidimensional statistical model of intraday electricity prices at the scale of the trading session, which allows all products to be simulated simultaneously. This model, based on Poisson measures and inspired by the Common Shock Poisson Model, reproduces the Samuelson effect (intensity and volatility increases as time to maturity decreases). It also reproduces the price correlation structure, highlighted here in the data, which decreases as two maturities move apart. This model has only three parameters that can be estimated using a moment method that we propose here. We demonstrate the usefulness of the model on a case of storage valuation by dynamic programming over a trading session.

Hierarchical modeling of irregularly spaced financial returns

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

Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe.

Retail investors, COVID and the quality of the New Zealand Stock Exchange

In this article, we investigate the impact of the influx of retail investors during the COVID-19 lockdowns on the New Zealand Stock Exchange on the efficiency of the market. On one hand, these investors bring added liquidity to the market, which generally improves market efficiency, and on the other hand, many of these investors were inexperienced retail traders who are often viewed as noise traders harming market efficiency. This natural experiment allows us to investigate the impact of retail investors. Using a sample of 99 New Zealand Exchange listed companies, we collect intraday price and quote data from 1 January 2019 to 24 March 2021. We compute a range of market quality measures across three categories: trading costs, price efficiency and speed of information incorporation. Our results clearly indicate that the influx of retail investors has reduced market quality; in particular, we observe more price predictability and information being impounded into prices more slowly. JEL Classification: C10, G14, G20

A Noisy Fractional Brownian Motion Model for Multiscale Correlation Analysis of High-Frequency Prices

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.

Do social media sentiments drive cryptocurrency intraday price volatility? New evidence from asymmetric TVP-VAR frequency connectedness measures

ABSTRACT In this paper, we investigate interdependencies between cryptocurrencies and investor sentiment by introducing the asymmetric TVP-VAR frequency connectedness approach. Our empirical results provide evidence of pronounced and time-varying interconnectedness between sentiment and cryptocurrency. In addition, we find that negative short-term interconnectedness dominates positive short-term interconnectedness until mid-2020 when this effect was reversed, persisting until the end of the sample period. While Ripple and Bitcoin are both found to be the main net transmitters of shocks, Stellar, Ethereum, and NEM are considered net receivers of shocks. Overall, our findings suggest that market sentiment is mainly driven by cryptocurrency volatility in both the short and the long run.

Multivariate simulation‐based forecasting for intraday power markets: Modeling cross‐product price effects

AbstractIntraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross‐product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high‐dimensional intraday price return vector. We model the marginal distribution as a zero‐inflated Johnson's distribution with location, scale, and shape parameters that depend on market and fundamental data. The dependence structure is modeled using copulas, accounting for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days and allowing the dependence parameter to be time‐varying. We validate our approach in a simulation study for the German intraday electricity market and find that modeling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.

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

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

Consequence of COVID-19 on Cryptocurrency Returns

This study scrutinizes the COVID-19 measures and their effect on leading cryptocurrency returns. Our direct measures of COVID-19 show that cryptocurrency returns are significantly influenced by COVID-19 and are most visible throughout pre-vaccination phase. The intraday price movement becomes wider during vaccination period compared to cryptocurrency returns. The findings demonstrate that even negative news of COVID-19 did not deter investors from being optimistic in the pre-vaccination period. Further, COVID-19 impacts on the cryptocurrency market diverge depending on the size of currency once vaccination begins. It reflects a different underlying dynamic process in cryptocurrency trading.

Deep Learning Approaches for Predicting Intraday Price Movements: An Evaluation of RNN Variants on High-Frequency Stock Data

This study discusses the comparison of four recurrent neural networks (RNN) models: Simple RNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional RNN (BiRNN), in forecasting minute-level stock price time series data. The performance of these four models is evaluated using the Mean Absolute Percentage Error (MAPE) on a stock dataset from Bank Central Asia (BBCA.JK). The experimental results reveal that the GRU model exhibits the best performance with an average MAPE of 0.0255%, followed by the LSTM model with an average MAPE of 0.0377%. The BiRNN model also demonstrates good performance with an average MAPE of 0.0668%, while the Simple RNN has the highest average MAPE at 0.5118%. This suggests that more complex recurrent architectures like GRU and LSTM have better capabilities in capturing patterns in high-frequency time series data. This study can be expanded by exploring other models such as CNN, conducting tests on diverse datasets, and experimenting with a wider range of hyperparameter variations. Additional variables such as economic indicators, global market data, and social data can also offer a more comprehensive understanding of factors influencing stock prices.

Improving prediction efficiency of Chinese stock index futures intraday price by VIX-Lasso-GRU Model

With T+0 and short selling mechanism, the stock index futures are attractive to short-term traders in China, where stocks cannot be liquidated within the day and are difficult to short. So in terms of futures, how to improve the accuracy and speed of intraday price forecasting always fascinates short-term traders and researchers. Here we propose a novel forecasting model, VIX-Lasso-GRU Model, which based on the gated recurrent unit (GRU) by adding VIX information and a method called Least absolute shrinkage and selection operator (Lasso). The volatility index (VIX) can reduce the prediction errors and the Lasso algorithm significantly improve the training speed of the model. We predict the 5-minute closing prices of three datasets of index futures by VIX-Lasso-GRU Model. Comparing to the pure GRU and LSTM, we find that this new prediction model can improve the prediction efficiency with faster speed and higher accuracy.

Do rights matter? An intraday analysis of rights issues in Australia

AbstractWe examine intraday abnormal returns associated with rights issue announcements in the Australian equity market over the period January 2000 to December 2022. Consistent with prior studies, we find significant abnormal returns ranging from −2.9% to −2.7% on the event day. We provide the first evidence on intraday price reactions pertaining to rights issues in Australia. Within 15 min of an announcement, we find significant abnormal trade returns of −1.2%. However, market participants are unable to profit by trading on these announcements due to transaction costs. Our results imply that the information content is fully impounded within 90 min.

Performance of the autoregressive integrated moving average model with exogenous variables statistical model on the intraday market for the Denmark-West bidding area

This article aims to investigate whether a statistical model known as Autoregressive Integrated Moving Average with Explanatory Variables can aid better predictability of volume-weighted average electricity prices compared to a commonly used forecasting method. This analysis was conducted for a specific bidding area, the Denmark-West bidding area (DK1). Autoregressive integrated moving average model with exogenous variable's performance was tested on the DK1 intraday market over a two-year period starting from 1 January 2019 until 31 December 2020. An explanatory variable used to support better the accuracy of the forecast is the day-ahead price for a corresponding intraday delivery hour. To ensure the validity of the paper, a well-known forecasting methodology was applied, and the results of the analysis show superior performance over the benchmark forecasting method. The autoregressive integrated moving average model with exogenous variables model developed was found to significantly outperform other commonly used forecasting methods, with an average mean absolute percentage error of 1.5%. The model was able to accurately predict intraday volume-weighted average prices up to 24 h in advance, using only publicly available data on day-ahead prices and historical intraday prices. Energy traders and other market players may find the developed autoregressive integrated moving average model with exogenous variables model to be a useful resource when looking to make more informed decisions in the intraday market.

Demand response scheduling of copper production under short-term electricity price uncertainty

Marketing demand response of industrial processes on electricity spot markets can reduce operational cost. We apply our simultaneous day-ahead and intraday electricity market participation approach (Germscheid et al., AIChE J. 2022;68:e17828) to analyze the demand response potential of power-intensive copper production with our previously derived resource–task network model (Röben et al., J. CLEAN. PROD. 2022;362:132221). Specifically, we tackle intraday price uncertainty by stochastic scheduling optimization considering both risk-neutral and risk-averse market participation. Risk-averse participation allows for 1.9% weekly savings compared to only participating on the day-ahead market. Risk-neutral participation allows for 6.4% savings, but is connected to higher financial risks on weekends. Moreover, we show that load-shifting capabilities significantly depend on modeling on/off decisions either as first or second-stage integer decisions and that sequential day-ahead and intraday scheduling allows to participate in both markets while balancing financial risk, expected cost, and computational complexity compared to stochastic scheduling.

Intraday price discovery and volatility transmission between the dual-listed stock index futures and spot markets – new evidence from India

PurposeThis study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.Design/methodology/approachThis study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.FindingsThis study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.Practical implicationsThese findings can potentially help market participants, policymakers, stock exchanges and regulators.Originality/valueUnlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

Dynamics of Intraday Price Discovery and Volatility Transmission: A Comprehensive Analysis of Gold Market Interactions

Dynamics of Intraday Price Discovery and Volatility Transmission: A Comprehensive Analysis of Gold Market Interactions

Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting

Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator’s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.

Disagreement in Market Index Options

Abstract 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.

Multiscale Volatility Analysis for Noisy High-Frequency Prices

We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of volatility behaviors suitable for intraday price processes. Algorithms for numerical estimation from time series observations are then introduced, with a new Hurst exponent estimator proposed for the noisy fractional Brownian motion model. Using real-world high-frequency price data for a collection of U.S. stocks and ETFs, we estimate the parameters in the noisy fractional Brownian motion and illustrate how the volatility varies over different timescales. The Hurst exponent and noise level also exhibit an intraday pattern whereby the the noise ratio tends to be higher near market close.