Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach

Tse, Yiu Kuen; Yang, Thomas Tao

doi:10.1080/07350015.2012.707582

Cited by 29 publications

(38 citation statements)

References 12 publications

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“…Then, each BTS return has an approximately constant integrated volatility of V D = V m /K. Instead of using δ 2 as an approximation of the integrated volatility of each price event as in Tse and Yang (2012), we use V D to replace δ 2 in Equation (6) to obtain a new ACD-ICV estimate.…”

Section: Integrated Volatility Estimation Using the Modified Acd-icv mentioning

confidence: 99%

“…We first select transactions using the price-event sampling method (where transactions are selected based on absolute price change) and estimate the daily integrated volatility using the ACD-ICV method as in Tse and Yang (2012). We denote this method by ME1.…”

Section: Integrated Volatility Estimation Using the Modified Acd-icv mentioning

confidence: 99%

“…MD3 is the two-factor afine stochastic volatility model with an intraday U-shape pattern Hasbrouck (1999) and Andersen et al (2012). MD4 is a deterministic volatility set-up Tse and Yang (2012). Finally, MD5 is MD1 with price jumps.…”

Section: Simulation Modelsmentioning

confidence: 99%

“…Our Monte Carlo simulation shows that the TRV estimates (with and without subsampling) using the BTS returns provide the smallest root mean-squared error (RMSE). Second, we modify the ACD-ICV method of Tse and Yang (2012), making use of the BTS scheme. Our modified ACD-ICV estimator performs better than the Realized Kernel (RK) estimates Barndorff-Nielsen et al (2008) and the method of Tse and Yang (2012), which uses price-event sampling.…”

Section: Introductionmentioning

confidence: 99%

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Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility

Dong

Tse

2017

Econometrics

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Abstract:We propose a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data. We compute a time-transformation (TT) function using the intraday integrated volatility estimated by a jump-robust method. The BTS transactions are obtained using the inverse of the TT function. Using our sampled BTS transactions, we test the semi-martingale hypothesis of the stock log-price process and estimate the daily realized volatility. Our method improves the normality approximation of the standardized business-time return distribution. Our Monte Carlo results show that the integrated volatility estimates using our proposed sampling strategy provide smaller root mean-squared error.

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Section: Integrated Volatility Estimation Using the Modified Acd-icv mentioning

confidence: 99%

Section: Integrated Volatility Estimation Using the Modified Acd-icv mentioning

confidence: 99%

Section: Simulation Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility

Dong

Tse

2017

Econometrics

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“…It is furthermore important also to look at shorter, and more revealing, intraday returns instead of only focusing on the volatility of daily returns. Since the financial market microstructure reveals so much about the patterns in volatility, it is not surprising that a large body of research has been devoted to understanding it (Tse & Yang, 2012).…”

Section: Introductionmentioning

confidence: 99%

The influence of volatility spill-overs and market beta on portfolio construction

Heymans¹,

Brewer²

2015

SAJEMS

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This study adds to Modern Portfolio Theory (MPT) by providing an additional measure to market beta in constructing a more efficient investment portfolio. The additional measure analyses the volatility spill-over effects among stocks within the same portfolio. Using intraday stock returns from five top-40 listed stocks on the JSE between July 2008 and April 2010, volatility spill-over effects were estimated with a residual-based test (aggregate shock [AS] model) framework. It is shown that when a particular stock attracted fewer volatility spill-over effects from the other stocks in the portfolio, the overall portfolio volatility decreased as well. In most cases market beta showcased similar results. Therefore, in order to construct a more efficient risk-adjusted portfolio, one requires both a portfolio that has a unit correlation with the market (beta-based), and stocks that showcase the least amount of volatility spill-over effects amongst one another. These results might assist portfolio managers to construct lower mean variance portfolios.

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Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

Fei

Liu

Wen

2023

Journal of Forecasting

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In this paper, we study the predictive performance of two kinds of volatility estimators: the realized volatility (RV) type and duration‐based ones. This is motivated by the theoretical and empirical support for these distinct estimators. We use intraday data for 218 component stocks of the CSI 300 index in the Chinese equity market from 2010–2019 and perform in‐ and out‐of‐sample 1‐, 5‐, and 22‐day ahead volatility forecasts from combinations of volatility models and these estimators. We show that, although empirically more efficient with the US data, the duration‐based estimators fail to compete statistically, or in terms of economic value, with RV‐type ones in the Chinese market. We perform a comprehensive set of simulations to rationalize these results and show that duration‐based estimators underperform as they cannot handle the occasional heightened level of volatility in the Chinese market.

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Estimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach

Cited by 29 publications

References 12 publications

Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility

Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility

The influence of volatility spill-overs and market beta on portfolio construction

Forecasting stock return volatility: Realized volatility‐type or duration‐based estimators

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