Measuring High-Frequency Causality between Returns, Realized Volatility and Implied Volatility

Dufour, Jean–Marie; García, René; Taamouti, Abderrahim

doi:10.2139/ssrn.1777282

Cited by 12 publications

(11 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bollerslev et al (2006),Giot (2005) andDufour et al (2012) report evidence of a return-driven relationship whileBekaert and Wu (2000) andDennis et al (2006) find evidence of a volatility-driven relationship.…”

mentioning

confidence: 97%

The intraday dynamics of bitcoin

Eross¹,

McGroarty

Urquhart

et al. 2019

Research in International Business and Finance

View full text Add to dashboard Cite

Bitcoin has received much investor attention in recent years and following this, there has been an explosion of academic studies examining this new financial asset. We contribute to the growing literature of Bitcoin by examining the intraday variables of the leading Bitcoin exchange with the highest information share over 4 years' worth of data to reveal the intraday stylized facts of Bitcoin and how they have developed over time. Employing GMT-timestamped tick data aggregated to the 5-mintuely frequency, we find that Bitcoin returns have increased over time, while trading volume, volatility and liquidity varied substantially over time. We also find that volume increases throughout the day and falls from around 4pm until midnight, which is consistent with the intraday patterns found in currency markets. Realised volatility is fairly consistent throughout the day although it is highest during the opening times of the three major global stock markets. Also liquidity is highest during the opening times of the major global exchanges and the markets tend to be illiquid during the early morning. Finally, we show evidence of the mixture of distribution hypothesis of Clark (1973).

show abstract

mentioning

confidence: 97%

The intraday dynamics of bitcoin

Eross¹,

McGroarty

Urquhart

et al. 2019

Research in International Business and Finance

View full text Add to dashboard Cite

show abstract

“…Similarly, the fact that aggregation and subsampling can distort dynamic relations has been widely discussed; see Tiao and Wei (1976), Wallis (1974), Sims (1974), Wei (1982), Hylleberg (1986), Marcellino (1999), Kaiser and Maravall (2001), Breitung and Swanson (2002), Gong et al (2015), and the references in the survey of Silvestrini and Veredas (2008). For a specific example showing that causal relations are modified by changing observation frequency, see Dufour et al (2012). However, Theorem 8 and Corollary 1 give general necessary and sufficient conditions under which 'spurious' causal relations between (vector) time series will not be induced by linear transformations of the variables involved.…”

Section: Resultsmentioning

confidence: 99%

“…Errors-in-variables can be interpreted as missing variables : if the noise were observable, it could be included as an additional variable, and different conclusions can emerge. As previously observed by several authors (see Hsiao, 1982;Lütktepohl, 1982;Dufour and Renault, 1998;Triacca, 1998Triacca, , 2000, causality properties in the sense of Wiener-Granger depend crucially on the information set considered, which can affect both the sheer presence of causality (or non-causality) and causality measures (Geweke, 1982;Dufour and Taamouti, 2010;Dufour et al, 2012). Of course, the central difficulty remains that noise is typically unobserved.…”

Section: Introductionmentioning

confidence: 88%

On the Sensitivity of Granger Causality to Errors‐In‐Variables, Linear Transformations and Subsampling

Anderson

Deistler

Dufour

2018

Journal Time Series Analysis

View full text Add to dashboard Cite

This article studies the sensitivity of Granger causality to the addition of noise, the introduction of subsampling, and the application of causal invertible filters to weakly stationary processes. Using canonical spectral factors and Wold decompositions, we give general conditions under which additive noise or filtering distorts Granger‐causal properties by inducing (spurious) Granger causality, as well as conditions under which it does not. For the errors‐in‐variables case, we give a continuity result, which implies that: a ‘small’ noise‐to‐signal ratio entails ‘small’ distortions in Granger causality. On filtering, we give general necessary and sufficient conditions under which ‘spurious’ causal relations between (vector) time series are not induced by linear transformations of the variables involved. This also yields transformations (or filters) which can eliminate Granger causality from one vector to another one. In a number of cases, we clarify results in the existing literature, with a number of calculations streamlining some existing approaches.

show abstract

“…The study of Bali and Peng (2006) on the Center for Research in Security Prices value-weighted index, S&P 500 cash index, S&P 500 index futures, seems to indicate a significantly positive relation between the conditional volatility and conditional mean of market returns at the daily level, and this both for the cases where volatility is estimated using daily and intraday data. Dufour et al (2012) combine high-frequency data with option price data and propose to use the difference between the implied and realized volatility (called the variance risk premium) to predict future returns. They also state that the use of implied volatilities rather than realized volatilities is essential to assess the volatility feedback effect.…”

Section: The Use Of High-frequency Data In Risk-return Analysis and Pmentioning

confidence: 99%