Intraday Returns Research Articles

Night Trading and Intraday Return Predictability: Evidence from Chinese Metal Futures Market

Night Trading and Intraday Return Predictability: Evidence from Chinese Metal Futures Market

Volatility Estimation When the Zero-Process is Nonstationary

Financial returns are frequently nonstationary due to the nonstationary distribution of zeros. In daily stock returns, for example, the nonstationarity can be due to an upwards trend in liquidity over time, which may lead to a downwards trend in the zero-probability. In intraday returns, the zero-probability may be periodic: It is lower in periods where the opening hours of the main financial centers overlap, and higher otherwise. A nonstationary zero-process invalidates standard estimators of volatility models, since they rely on the assumption that returns are strictly stationary. We propose a GARCH model that accommodates a nonstationary zero-process, derive a zero-adjusted QMLE for the parameters of the model, and prove its consistency and asymptotic normality under mild assumptions. The volatility specification in our model can contain higher order ARCH and GARCH terms, and past zero-indicators as covariates. Simulations verify the asymptotic properties in finite samples, and show that the standard estimator is biased. An empirical study of daily and intradaily returns illustrate our results. They show how a nonstationary zero-process induces time-varying parameters in the conditional variance representation, and that the distribution of zero returns can have a strong impact on volatility predictions.

Increasing the information content of realized volatility forecasts

Abstract Assuming N available calendar days, each with M intraday returns, the realized volatility literature suggests creating N end-of-day estimators by summing the M squared returns from each particular date. Instead of this “Calendar” [realized variance (RV)] approach, we propose a “Rolling” [rolling RV (RRV)] approach that simply sums trailing M returns at each timestamp, regardless if all M returns belong to the same calendar date. When estimating an out-of-sample 1-day realized volatility model, the former results in an ordinary least squares (OLS) regression with N−1 datapoints while the latter incorporates M(N−2)+1 datapoints, effectively lowering the standard errors, and potentially resulting in more accurate forecasts. We compare both models for the S&P 500 and 26 Dow Jones Industrial Average stocks; our results generally suggest that the Rolling approach yields both statistically and economically significant superior out-of-sample performance over the traditional Calendar approach.

Trading behavior of retail investors in derivatives markets: Evidence from Mini options

Mini options are specially catered to retail investors with limited capital for trading options on extremely high-priced securities. The coexistence of both Mini and standard options for the same underlying security provides us a novel setting to investigate whether and how small retail investors use derivatives contracts differently compared to their counterparts. First, we find that the Mini option investors are more subject to constraints of limited attention. Specifically, Mini option investors trade more intensively near market opens, and their trading activities are more heavily influenced by attention-grabbing events and attention-distracting events. Second, we document that Mini option investors’ trading is more likely to be driven by market sentiment than standard option investors. Third, the trading performance of Mini option investors is also worse than that of standard option investors, with less positive intraday returns and more negative overnight returns.

Hedging on the Hill: Does Political Hedging Reduce Firm Risk?

We examine whether firms’ political hedging activities are effective at mitigating political risk. Focusing on the risk induced by partisan politics, we measure political hedging as the degree to which firms’ political connections are balanced across Republican and Democratic candidates. We find that greater political hedging is associated with reduced stock return volatility, particularly during periods of higher policy uncertainty. Similarly, greater political hedging is associated with reduced crash risk, investment volatility, and earnings volatility. Moreover, the reduction in earnings volatility appears to relate to both a firm’s taxes and its operating activities, as we find that greater political hedging is associated with reduced cash effective tax rate volatility and pretax income volatility. We further find investors are better able to anticipate future earnings for firms that engage in political hedging, suggesting that political hedging helps improve firms’ information environments. Lastly, we perform an event study using President Obama’s Clean Power Plan. We find that on the days this policy proposal was debated in Congress, energy and utility firms experienced heightened intraday return volatility (relative to other firms and nonevent days). However, this heightened volatility is mitigated for energy and utility firms that are more politically hedged. Overall, we conclude that political hedging is an effective risk management tool that helps mitigate firm risk. This paper was accepted by Suraj Srinivasan, accounting.

Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators

The statistical properties of realized volatility estimators critically depend on the sampling frequency of the underlying intra-day returns and on the chosen estimation formula. This gives rise to a substantial model uncertainty when realized volatility is used as a regressor in tail risk forecasting models. In this paper, aiming to mitigate the impact of model uncertainty on the generation of tail risk forecasts, we propose parsimonious extensions of the Realized Exponential GARCH model that combine information from several volatility estimators. Both fixed and time-varying parameter models are considered. An application to the prediction of daily Value-at-Risk and Expected Shortfall for the S&P 500 provides evidence that modelling approaches based on the combination of different frequencies and estimation formulas can lead to significant accuracy gains.

Extremal dependence measure for functional data

Principal component analysis is one of the most fundamental tools of functional data analysis. It leads to an efficient representation of infinitely dimensional objects, like curves, by means of multivariate vectors of scores. We study the dependence between extremal values of the scores using the extremal dependence measure (EDM). The EDM has been proposed and studied for positive bivariate observations. After extending it to multivariate observations, we focus on its application to the vectors of scores of functional data. Estimated scores form a triangular array of dependent random variables. We derive condition guaranteeing that a suitable estimator of the EDM based on these scores converges to the population EDM and is asymptotically normal. These conditions are completely different from those encountered in the second-order theory of functional data. They are formulated within the framework of functional regular variation. Large sample theory is complemented by an application to intraday return curves for certain stocks and by a simulation study.

LSTM forecasting foreign exchange rates using limit order book

We use long and short term memory (LSTM) to predict intraday returns in foreign exchange markets. As predictors, we use events in the limit order book. Compared to other models, our model predicts the movement of a 1-min midquote return. When we consider the bid-ask spread, this prediction does not bring economic gains. This result indicates that these events can contribute to price discovery and the studied markets efficiently set the spread.

How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin

This paper investigates the differential sensitivity of Sharia-compliant stocks to Bitcoin's realized volatility of positive and negative intraday returns in bear, normal, and bull market states. We use a quantile regression approach, after orthogonalizing raw equity returns with respect to a variety of relevant global factors and accounting for structural breaks in the data. For developed markets, the results indicate that upside volatility tends to exert contemporaneous and lagged negative influences on Islamic stocks more in bear than in bull market conditions, whereas the downside counterpart positively affects returns when Sharia-compliant equities are in bear and bull phases. For emerging markets, we find that Bitcoin's upside (downside) volatility has lagged negative (positive) effects on returns across all market regimes. The dependence structures tend to be asymmetric and have noticeably become stronger in the last two years than in earlier periods of the sample. Our evidence offers important implications for investors.

Large dimensional portfolio allocation based on a mixed frequency dynamic factor model

In this paper, we propose a mixed-frequency dynamic factor model (MFDFM) taking into account the high-frequency variation and low-frequency variation at the same time. The factor loadings in our model are affected by the past quadratic variation of factor returns, while the process of the factor quadratic variation is under a mixed-frequency framework (DCC-RV). By combing the variations from the high-frequency and low-frequency domain, our approach exhibits a better estimation and forecast of the assets covariance matrix. Our empirical study compares our MFDFM model with the sample realized covariance matrix and the traditional factor model with intraday returns or daily returns. The results of the empirical study indicate that our proposed model indeed outperforms other models in the sense that the Markowitz’s portfolios based on the MFDFM have a better performance.

The long-term effects of section transfers on return volatility: Intraday and overnight periods

PurposeStudies on transfers to a more regulated section show an increase in information disclosure and stocks’ liquidity levels. Classical theories suggest that volatility should also be reduced. This study aims to analyse the long-term effects of a section transfer to a more regulated section (TSE 1/TSE 2) on stock return volatility.Design/methodology/approachThis study uses an empirical framework relying on two-sample t-tests and panel regressions. These use robust standard errors and control for fixed effects, day effects and macroeconomic factors. The return variance of comparable stocks’ benchmark sample, instead of market variance, is used as a control variable. Comparable stocks operate within the same industry and do not transfer during the sample period. The authors test our results’ robustness using generalized autoregressive conditional heteroskedasticity estimates.FindingsThe study’s main findings show that pre-transferred stocks are more volatile than the stocks’ benchmark sample. The transfer to a more regulated section leads to a gradual decrease in the total daily stock return volatility, intraday return volatility and overnight return volatility.Originality/valueTo the best of my knowledge, this study is the first to empirically address the volatility change caused by the stocks’ transfer to a more regulated section. This study highlights the benefits of choosing section transfers to reduce volatility.

They Chose to Not Tell You

The world's stock markets display a strikingly suspicious, decades long pattern of overnight and intraday returns that nobody (other than us) has plausibly explained and that nobody (other than us) has clearly and persistently alerted you to. We use correspondence on this topic over the past five years to show that the silence of others on this issue does not arise from their having a good reason to believe this pattern is fine. Separately, and regardless of whether this pattern turns out to be fine, we have documented that people in a position to alert you to the presence of strikingly suspicious return patterns in the world's stock markets that nobody can innocuously explain are aware of this issue, have no good reason to believe it is not a problem, and chose to not tell you.

Forecasting directional movements of stock prices for intraday trading using LSTM and random forests

We employ both random forests and LSTM networks (more precisely CuDNNLSTM) as training methodologies to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500 from January 1993 till December 2018 for intraday trading. We introduce a multi-feature setting consisting not only of the returns with respect to the closing prices, but also with respect to the opening prices and intraday returns. As trading strategy, we use Krauss et al. (2017) and Fischer and Krauss (2018) as benchmark. On each trading day, we buy the 10 stocks with the highest probability and sell short the 10 stocks with the lowest probability to outperform the market in terms of intraday returns — all with equal monetary weight. Our empirical results 22All the codes are available on https://github.com/pushpendughosh/Stock-market-forecasting. show that the multi-feature setting provides a daily return, prior to transaction costs, of 0.64% using LSTM networks, and 0.54% using random forests. Hence we outperform the single-feature setting in Fischer and Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0.41% and of 0.39% with respect to LSTM and random forests, respectively.33Fischer and Krauss (2018) and Krauss et al. (2017) obtain 0.46% and 0.43%, as the period from November 2015 until December 2018 was not included in their backtesting.

Forecasting Volatility With Spot Index and Index Futures: Evidence From Taiwan

In this article, we use the volatility of the Taiwan Stock Index (TAIEX) and its futures in the encompassing regression model to respectively make asynchronous forecasts of realized volatility (RV) and implied volatility (IV). Initially, we discover that, to obtain a stationary RV with a stable, long memory parameter, the optimal sampling intervals for the intraday return were 9 and 30 minutes. We uncover that the spot volatility is more predictive of RV than the futures volatility. To forecast IV, the volatility of futures has more information content, helping to improve overall forecast performance. The result implies that the underlying asset of the TAIEX options (TXO) is approximately the index futures rather than the spot index, owing mainly to the demands for hedging and arbitrage from the TXO holders. Finally, we confirm the forecasting capability of our various models via extensive forecasting error and simulation exercises.

Asymmetric dependence of intraday frequency components in the Brazilian stock market.

The multivariate dependence plays an important role in financial instrument management. Due to the inherent characteristics in the financial market, such as heavy tails in the returns unconditional distribution and asymmetry between gain and loss, we obtained the asymmetric dependence structure in different short-term variation scales based on the wavelet technique MODWT. The study sought to capture the relations between financial returns represented by its frequency components. Intraday returns series was used in the 15-min sampling interval from stocks and applied the D-Vine pair-copula to decompose in trade frequencies of 15 min, 1 h, 1 day, and 1 week with margin adjustments of ARIMA-APARCH class and BB7 copula function, responsible for measuring the dependence on tails. The results indicated the prevalence of a high dependence during market upturns, rising over the analyzed frequencies. Being an important tool in financial management and allowing short-term strategies of diversification.

Intra-day co-movements of crude oil futures: China and the international benchmarks.

Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its co-movements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases.

Wasserstein autoregressive models for density time series

Data consisting of time‐indexed distributions of cross‐sectional or intraday returns have been extensively studied in finance, and provide one example in which the data atoms consist of serially dependent probability distributions. Motivated by such data, we propose an autoregressive model for density time series by exploiting the tangent space structure on the space of distributions that is induced by the Wasserstein metric. The densities themselves are not assumed to have any specific parametric form, leading to flexible forecasting of future unobserved densities. The main estimation targets in the order‐p Wasserstein autoregressive model are Wasserstein autocorrelations and the vector‐valued autoregressive parameter. We propose suitable estimators and establish their asymptotic normality, which is verified in a simulation study. The new order‐p Wasserstein autoregressive model leads to a prediction algorithm, which includes a data driven order selection procedure. Its performance is compared to existing prediction procedures via application to four financial return data sets, where a variety of metrics are used to quantify forecasting accuracy. For most metrics, the proposed model outperforms existing methods in two of the data sets, while the best empirical performance in the other two data sets is attained by existing methods based on functional transformations of the densities.

Hedging demand and market intraday momentum

Hedging short gamma exposure requires trading in the direction of price movements, thereby creating price momentum. Using intraday returns on over 60 futures on equities, bonds, commodities, and currencies between 1974 and 2020, we find strong market intraday momentum everywhere. The return during the last 30 minutes before the market close is positively predicted by the return during the rest of the day (from previous market close to the last 30 minutes). The predictive power is economically and statistically highly significant, and reverts over the next days. We provide novel evidence that links market intraday momentum to the gamma hedging demand from market participants such as market makers of options and leveraged ETFs.

Asset pricing and FOMC press conferences

A press conference (pc) organized by the Federal Open Market Committee (fomc) followed half of the scheduled announcements from 2011 to 2018. We document that excess stock returns are strongly and positively related to their betas on announcement days with a pc. In addition, the cross-sectional dispersion in betas declines substantially on pc days when measured using both daily and intraday return data. These effects are absent on announcement days without a pc. Last, we find that stock-bond correlations are positive (negative) on pc (all other) days and that their variations are related to uncertainty and yield curve information. We discuss implications and possible explanations for our findings.

Volatility of intraday financial data: Multiscale Ibovespa behavior under to the COVID-19 pandemic

In financial markets, volatility modeling has been a strategy widely used because it reflects uncertainties about changes in asset prices. Incorporating peculiarities of financial series, this study estimated the volatility for the intraday index of the Brazilian stock market (Ibovespa) using ARIMA-APARCH models in different time frequencies with the aid of the wavelet MODWT decomposition technique. This work proposes an analysis of the impacts of the frequency components on the behavior of the volatility of intraday returns using the series of details wavelet in different time horizons, in an atypical period in the global financial markets, generated by the COVID-19 pandemic. The empirical results suggest low unconditional volatility and strong signs of persistence in all analyzed frequencies. The asymmetry in volatility is evidenced in the higher frequencies, the leverage effect being present only in the series of details with variations of 15-120 min., which is corroborated with the results obtained with the reconstructed series. The evidenced behaviors have an impact on the elaboration of short-term investment strategies and risk management, since the positive and negative shocks, such as those given by the world pandemic of COVID-19, have different impacts on the volatility of returns in shorter periods. The information obtained can contribute to the analysis of future atypical events in the Brazilian stock market, supporting the decision-making of economic agents.