Forecasting financial market volatility: Sample frequency vis-à-vis forecast horizon

This paper explores the return volatility predictability inherent in high-frequency speculative returns. Our analysis focuses on a refinement of the more traditional volatility measures, the integrated volatility, which links the notion of volatility more directly to the return variance over the relevant horizon. In our empirical analysis of the foreign exchange market the integrated volatility is conveniently approximated by a cumulative sum of the squared intraday returns. Forecast horizons ranging from short intraday to 1-month intervals are investigated. We document that standard volatility models generally provide good forecasts of this economically relevant volatility measure. Moreover, the use of high-frequency returns significantly improves the longer run interdaily volatility forecasts, both in theory and practice. The results are thus directly relevant for general research methodology as well as industry applications.     

Estimating daily volatility in financial markets utilizing intraday data

This study proposes a new approach to the estimation of daily realised volatility in financial markets from intraday data. Initially, an examination of intraday returns on S&P 500 Index Futures reveals that returns can be characterised by heteroscedasticity and time-varying autocorrelation. After reviewing a number of daily realised volatility estimators cited in the literature, it is concluded that these estimators are based upon a number of restrictive assumptions in regard to the data generating process for intraday returns. We use a weak set of assumptions about the data generating process for intraday returns, including transaction returns, given in den Haan and Levin [den Haan, W.J., Levin, A., 1996. Inferences from parametric and non-parametric covariance matrix estimation procedures, Working paper, NBER, 195.], which allows for heteroscedasticity and time-varying autocorrelation in intraday returns. These assumptions allow the VARHAC estimator to be employed in the estimation of daily realised volatility. An empirical analysis of the VARHAC daily volatility estimator employing intraday transaction returns concludes that this estimator performs well in comparison to other estimators cited in the literature.     

High frequency correlation dynamics and day-of-the-week effect: A score-driven approach in an emerging market stock exchange

Intraday correlation dynamics poses challenges to financial econometricians, especially in recently popular high frequency domain, due to non-synchronous trading and market microstructure noise. Traditional models fail to address the issues inherent to the nature of the data, which is riddled with noisy signals and missing values. We employ a recently developed method based on Generalized Autoregressive Score (GAS) framework and State-Space Modeling to remedy these characteristics of high frequency data and estimate intraday correlations in Turkish equity market Borsa Istanbul. Our findings reveal that average intraday conditional correlation rises as trading commences and lingers around certain altitude for some time, with the eigenvalues associated with market factor becoming progressively more dominant. An upward trend closes out the trading day on Mondays, which we attribute to the US market opening, whereas the rest of the week does not show a generalizable closing-time effect. Assessment of the findings across different market conditions and days of the week reveals elevated correlation levels in volatile markets as well as a distinguishable path for the beginning of the week. Beyond the scholarly contribution, the methodology can be used as a nowcasting tool and the findings are of interest to various parties like high-frequency traders, risk and portfolio managers and regulatory agencies in formulating their high frequency trading practices, hedging, portfolio construction schemes and margin requirements, respectively.     

Stock Crashes and Jumps Reactions to Information Demand and Supply: An Intraday Analysis

The objective of this paper is to examine the possible linkage between the intraday stock price crashes and jumps and public information by using data from the Chinese stock market and Baidu Index. We divided public information into two kinds of information: supply through online media and information demand across inquiries by individual investors. Using a large sample from Chinese listed firms from 2013 to 2019, our evidence clearly indicates that online information supply and demand both have a positive impact on the intraday crashes and jumps; this is, the firm with higher information supply and demand more likely to experience intraday crashes and jumps. The results are robust to an alternative measure of crash risk. Moreover, we further examine whether the market conditions have an impact on the relationship between information flow and intraday crashes and jumps, and find that the marginal effect of information supply on intraday price crashes and jumps is smaller in the bull market phase. Moreover, the bull market phase enhances the effect of information demand on intraday price crashes and jumps.

     

Liquidity Dynamics Around Jumps: The Evidence from the Warsaw Stock Exchange

The aim of our study is to examine the dynamics of trading volume and the number of trades around jumps detected in intraday stock returns. We detect jumps in equally spaced 10-minute returns for most liquid stocks quoted on the Warsaw Stock Exchange within one-year sample period. We match jumps with macroeconomic and firm specific news. We find that only the minority of jumps is associated with public information releases, whereas the majority of them is motivated by liquidity shocks observed in the spreads, volume, and the number of trades. Our findings show that jumps are related to the inability of the market to absorb new and big orders. Liquidity shocks in volatility, volume, and quoted spread are the key drivers accompanying the occurrence of the jumps. Finally, the introduction of a faster and more efficient trading system improves the liquidity by increasing the depth of the market.     

The influence of intraday seasonality on volatility transmission pattern

Using data on a five-minute interval basis, this article analyses the effects of intraday seasonality on volatility transmission between the spot and futures markets of the CAC40, DAX30 and FTSE100. Remarkable differences in the impulse response analysis and in the dynamic and directional measurement of volatility spillovers are encountered depending on whether the intraday periodic component is considered. Thus, the convenience of removing intraday seasonality seems to be critical to reduce the risk of spurious causality when employing high-frequency data in volatility transmission. Moreover, the impact of market microstructure noise seems negligible when using an optimal frequency of observations.     

Market closure effects on return, volatility, and turnover patterns in the Hong Kong index futures market

This paper examines the market closure effect of the Stock Exchange of Hong Kong (SEHK) on the intraday behaviour of the index futures contract which continues to trade for 5–15 min after the close of the SEHK. The behaviour of the index futures market in Hong Kong is consistent with the contagion model of King and Wadhwani (1990) in that the close of the SEHK leads to an immediate downturn in the return, volatility, and turnover in the index futures market. The long period of nontrading before the morning also leads to a higher morning volatility and turnover.     

High-frequency trading and stock liquidity: An intraday analysis

This paper studies the impact of high-frequency trading (HFT) on intraday liquidity of CAC40 stocks listed on Euronext. Spreads display an intraday L-shaped pattern, while quoted depth follows an inverse pattern: low at the open and increasing towards the end of the trading day. When liquidity demand is particularly high, there is a high rate of order cancellations attributable to high-frequency traders who use frequent order cancellations to strategically manage their limit orders and close positions near the market close. Using the generalized method of moments estimator, we generate strong evidence that greater intensity of HFT is associated with lower spreads and higher depth. The positive effect of HFT on liquidity is due mainly to decreased adverse selection costs arising from asymmetric information among market participants.     

Idiosyncratic risk and the cross-section of stock returns: Merton (1987) meets Miller (1977)

Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] predicts that idiosyncratic risk should be priced when investors hold sub-optimally diversified portfolios, and cross-sectional stock returns should be positively related to their idiosyncratic risk. However, the literature generally finds a negative relationship between returns and idiosyncratic risk, which is more consistent with Miller's [1977. Risk, uncertainty, and divergence of opinion. Journal of Finance 32, 1151–1168] analysis of asset pricing under short-sale constraints. We examine the cross-sectional effects of idiosyncratic risk while explicitly recognizing the confounding effects that dispersion of beliefs and short-sale constraints produce in the Merton framework. We find strong support for Merton's [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] model among stocks that have low levels of investor recognition and for which short selling is limited. For these stocks, the relation between idiosyncratic risk and expected returns is positive, as predicted by Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510].     

When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions

We examine high-frequency market reactions to an intraday stock-specific news flow. Using unique pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the indicated relevance, novelty and direction of company-specific news. Employing a high-frequency VAR model based on 20 s data of a cross-section of stocks traded at the London Stock Exchange we find distinct responses in returns, volatility, trading volumes and bid-ask spreads due to news arrivals. We show that a classification of news according to indicated relevance is crucial to filter out noise and to identify significant effects. Moreover, sentiment indicators have predictability for future price trends though the profitability of news-implied trading is deteriorated by increased bid-ask spreads.     

Do DOW returns really influence the intraday Spanish stock market behavior?

Although the behavior of the Spanish stock market has been studied from many different points of view, none of the previous research has ever analyzed the influence of previous daytime, overnight and daily returns from the DOW and IBEX upon 5-min intraday returns of the IBEX throughout the complete trading session. Clear evidence is provided relative to the influence of the DOW. The main finding that it underreacts to the DOW returns in the first hours of trading but overreacts during the last 2 h (after the opening of the US markets) would help to develop a profitable trading strategy.     

Implicit intraday interest rate in the UK unsecured overnight money market

This paper estimates the intraday value of money implicit in the UK unsecured overnight money market. Using transactions data on overnight loans advanced through the UK large value payments system CHAPS in 2003–2009, we find a positive and economically significant intraday interest rate. While the implicit intraday interest rate is quite small pre-crisis, it increases more than tenfold during the financial crisis of 2007–2009. The key interpretation is that an increase in implicit intraday interest rate reflects the increased opportunity cost of pledging collateral intraday and can be used as an indicator to gauge the stress of the payment system. We obtain qualitatively similar estimates of the intraday interest rate by using quoted intraday bid and offer rates and confirm that our results are not driven by the intraday variation in the bid-ask spread.     

Intraday dynamics of volatility and duration: Evidence from Chinese stocks

We propose a new joint model of intraday returns and durations to study the dynamics of several Chinese stocks. We include three U.S. stocks for comparison. Flexible innovation distributions are used for durations and returns, and the total variance of returns is decomposed into different volatility components associated with different transaction horizons. The new model provides strong improvements in density forecasts for duration and returns and only modest gains for points forecasts of the variance of returns. The conditional hazard functions are non-monotonic and there is strong evidence for different volatility components. Although diurnal patterns, volatility components, and market microstructure implications are similar across the markets, there are interesting differences. Durations for lightly traded Chinese stocks tend to carry more information than heavily traded stocks. Chinese investors usually have longer investment horizons, which may be explained by the specific trading rules in China.     

Intraday patterns in London listed Exchange Traded Funds

In this paper we examine the intraday trading patterns of Exchange Traded Funds (ETFs) listed on the London Stock Exchange. ETFs have been shown to be characterised by much lower bid–ask spread costs and by lower levels of information asymmetry than individual securities. One possible explanation for intraday trading patterns is that concentration of trading arises at the start of the trading day because informed traders have private information that quickly diminishes in value as trading progresses. Since ETFs have lower trading costs and lower levels of information asymmetry we would expect these securities to display less pronounced intraday patterns than individual securities. We fail to find that ETFs are characterised by concentrated trading bouts during the day and therefore find support for the argument that information asymmetry is the cause of intraday volume patterns in stock markets. We find that ETF bid–ask spreads and volatility are elevated at the open but not at the close. This lends support to the “accumulation of information” explanation that sees high spreads and volatility at the open as a consequence of information accumulating during a market closure and impacting on the market when it next opens.     

Volatility estimation on the basis of price intensities

This paper investigates the use of price intensities, i.e. the time between price changes of a given size, to estimate volatilities based on high-frequency data. We interpret the conditional probability for the occurrence of a price event within a certain time horizon as a risk measure which allows us to obtain an estimator of the conditional volatility per time. To consider censoring effects caused by nontrading periods, we use a proportional hazard model. Seasonalities are taken into account by including regressors based on a flexible Fourier form capturing intraday and time-to-maturity seasonalities. Testing for serial correlation and controlling for unobservable heterogeneity permits us to check for misspecification on different aggregation levels. Empirical results are based on intraday transaction data of Bund future trading at the LIFFE, London.     

Option trading after the opening bell and intraday stock return predictability

Prior literature finds that information is reflected in option markets before stock markets, but no study has explored whether option volume soon after market open has predictive power for intraday stock returns. Using novel intraday signed option-to-stock volume data, we find that a composite option trading score (OTS) in the first 30 min of market open predicts stock returns during the rest of the trading day. Such return predictability is greater for smaller stocks, stocks with higher idiosyncratic volatility, and stocks with higher bid–ask spreads relative to their options’ bid–ask spreads. Moreover, OTS is a significantly stronger predictor of intraday stock returns after overnight earnings announcements. The evidence suggests that option trading in the 30 min after the opening bell has predictive power for intraday stock returns.     

Multifrequency news and stock returns

Equity prices are driven by shocks with persistence levels ranging from intraday horizons to several decades. To accommodate this diversity, we introduce a parsimonious equilibrium model with regime shifts of heterogeneous durations in fundamentals, and estimate specifications with up to 256 states on daily aggregate returns. The multifrequency equilibrium has higher likelihood than the Campbell and Hentschel [1992. No news is good news: an asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31, 281–318] specification, while producing volatility feedback 10 to 40 times larger. Furthermore, Bayesian learning about volatility generates a novel trade-off between skewness and kurtosis as information quality varies, complementing the uncertainty channel [e.g., Veronesi, 1999. Stock market overreaction to bad news in good times: a rational expectations equilibrium model. Review of Financial Studies 12, 975–1007]. Economies with intermediate information best match daily returns.     

Normality of stock returns with event time clocks

We introduce a different way to measure time using event clocks, with which we can observe a normal distribution of intraday stock returns. Most finance studies employ a ‘default’ time measurement that uses a calendar clock. Cumulative evidence from prior literature shows that returns with a calendar clock follow a distribution with an excess kurtosis and a heavier tail, relative to a normal distribution. We examine the distribution of intraday stock returns using different clocks. We find that returns do not follow a normal distribution with a traditional calendar clock, but do follow a normal distribution when event clocks are applied.     

Intraday Returns and the Frequency of Trading at the Ask on the Sydney Futures Exchange: A Research Note

This article documents and provides explanations for intraday patterns in returns for the Share Price Index (SPI) futures contract traded on the Sydney Futures Exchange (SFE). Consistent with overseas futures markets research, a positive and significant overnight return is documented. Unlike overseas futures markets, we find little evidence of an end of day price rise. Our evidence suggests that overnight returns for the SPI contract are largely driven by the way returns are typically measured, which ignores the fact that there is a significantly greater frequency of sellers at the market close and buyers at the start of the day. These patterns are consistent with hedging behaviour by futures traders with long positions in the underlying stock.     

Adverse selection in cryptocurrency markets

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.