Overnight News and Daily Equity Trading Risk Limits

Ahoniemi, Katja; Fuertes, Ana-Marı́a; Olmo, José

doi:10.1093/jjfinec/nbu032

Cited by 21 publications

(22 citation statements)

References 41 publications

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“…This section begins by presenting a general framework for forecasting the day-ahead close-to-close return and variance. Sections 2.2 and 2.3 discuss, respectively, as particular and competing approaches, the forecasting equations of Ahoniemi et al [5] that condition on information up to the market close, and the novel univariate modeling approach here entertained that conditions on information up to the market open which thus, incorporates the overnight close-to-open return.…”

Section: Overnight Information For Modeling Daily Pricesmentioning

confidence: 99%

“…k . We follow Engle and Manganelli [12], Dumitrescu et al [48], Ahoniemi et al [5] and others in deploying these backtesting tests using the expanded regressor set…”

Section: Backtestingmentioning

confidence: 99%

“…In order to assess the information content of the ex post observed overnight price variation for VaR forecasting, we will compare two modeling approaches. One is the bivariate approach proposed by Ahoniemi et al [5] which applies to a setup where forecaster constructs the day-ahead prediction 1 See George and Hwang [1], Jones et al [2], and French and Roll [3], inter alios. 2 De Goojier et al [4] exploit full information in the intra-day stock price patterns in foreign markets during non-trading hours in a home market to predict the opening of an index in the home market.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of this bivariate framework, as proposed in Ahoniemi et al [5], is not only its flexibility to model separately the overnight and daytime return processes, r o,t and r d,t , but also to model their covariance. For the latter purpose, these authors extend the dynamic conditional correlation (DCC) model of Engle [29] to the current setting where a GARCH model characterizes the overnight conditional variance and an ARFIMA realized volatility model characterizes the daytime conditional variance 6 .…”

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confidence: 99%

“…This author also proposes a cDCC procedure based on reformulating the DCC correlation driving process as a linear multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) process. For consistency with the univariate specifications of the conditional volatility of the daytime and overnight return processes presented in this paper, we use, instead, the version of the DCC model proposed in Ahoniemi et al[5].…”

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confidence: 99%

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On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

Fuertes

Olmo

2016

JRFM

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This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et al. that constructs the forecast at the market close instead and, accordingly, it models separately the daytime and overnight return processes and their covariance. For a small cap portfolio, the bivariate VaR approach affords superior predictive ability than the ex post overnight VaR approach whereas for a large cap portfolio the results are reversed. The contrast indicates that price discovery at the market open is less efficient for small capitalization, thinly traded stocks.

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Section: Overnight Information For Modeling Daily Pricesmentioning

confidence: 99%

“…k . We follow Engle and Manganelli [12], Dumitrescu et al [48], Ahoniemi et al [5] and others in deploying these backtesting tests using the expanded regressor set…”

Section: Backtestingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

Fuertes

Olmo

2016

JRFM

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Trading around the clock: Revisit volatility spillover between crude oil and equity markets in different trading sessions

Hao

et al. 2023

Journal of Futures Markets

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We investigate the volatility spillover between the crude oil (West Texas Intermediate) and G7 countries' equity markets with high‐frequency data. Considering the trading period difference among different countries, the volatility spillover of oil market intraday and overnight sessions is studied with G7 countries, respectively. The empirical findings suggest that the US stock market dominates intraday volatility spillover to the oil market, while the European and Asian stock markets dominate overnight spillovers. The asymmetric spillover result shows that stock markets mainly spillover bad volatility to the oil market in the intraday trading session, and spillover good volatility to the oil market in the overnight trading session. Our results provide empirical evidence that crude oil market overnight trading information is also important for understanding information diffusion and volatility forecasting.

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Inference on Self‐Exciting Jumps in Prices and Volatility Using High‐Frequency Measures

Maneesoonthorn

Forbes

Martin

2016

J of Applied Econometrics

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Dynamic jumps in the price and volatility of an asset are modelled using a joint Hawkes process in conjunction with a bivariate jump diffusion. A state space representation is used to link observed returns, plus nonparametric measures of integrated volatility and price jumps, to the specified model components; with Bayesian inference conducted using a Markov chain Monte Carlo algorithm. An evaluation of marginal likelihoods for the proposed model relative to a large number of alternative models, including some that have featured in the literature, is provided. An extensive empirical investigation is undertaken using data on the S&P500 market index over the 1996 to 2014 period, with substantial support for dynamic jump intensities -including in terms of predictive accuracy -documented.alternatives to the state space form.As in Bandi and Reno (2016) spot price data only is used to analyse all models, with the results unaffected as a consequence by the nature of -and potential dynamics in -volatility and jump risk premia (see Bollerslev, Gibson andManeesoonthorn, Martin, Forbes andGrose, 2012, for analyses in which such specifications do feature). However, and in contrast with Bandi and Reno, data measured at the daily frequency (including that which aggregates to the daily level over intraday observations) underpins the analysis. In common with the large part of the relevant literature (Bollerslev, Kretschmer, Pigorsch andTauchen, 2009, andLiu, Patton and Sheppard, 2015, amongst many others) we also choose to construct all measures using within-day observations only, thereby avoiding the need to model closeto-open movements in the index (as in, for example, Ahoniemi, Fuertes and Olmo, 2015, and Andersen, Bollerslev and Huang, 2011) and any specific dynamic movements therein. (See Hansen and Lunde, 2005, and Takahashi, Omori and Watanabe, 2009, for earlier discussions on the role played by non-trading periods in the construction of high frequency measures).The remainder of the paper is organized as follows. Section 2 describes our proposed asset price model and its main properties. The continuous time representation is presented first, followed by the discrete time state space structure adopted for inference. Details are given of the high frequency measures of volatility and price jumps that are used to supplement daily returns in defining the state space model. The Bayesian inferential approach is then outlined in Section 3, including the way in which the alternative specifications are to be assessed, relative to the most general model, both in terms of marginal likelihoods and cumulative log scores. Results from the extensive empirical analysis of the S&P500 index are presented and discussed in Section 4. The benefits of allowing for a very flexible dynamic specification for price and variance jumps are confirmed by both the within-sample and predictive assessments, with the bivariate Hawkes specification given strong support by the data, relative to other more restrictive models. The empirical results also indic...

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Overnight News and Daily Equity Trading Risk Limits

Cited by 21 publications

References 41 publications

On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

Trading around the clock: Revisit volatility spillover between crude oil and equity markets in different trading sessions

Inference on Self‐Exciting Jumps in Prices and Volatility Using High‐Frequency Measures

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