Comparison of range-based volatility estimators against integrated volatility in European emerging markets

Arnerić, Josip; Matković, Mario; Sorić, Petar

doi:10.1016/j.frl.2018.04.013

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…In particular, we highlight Molnar, who mentioned in [26] the mean squared error (MSE) and proportional bias (PB). Arnerić et al [27] employed the MSE as well in their analysis, in order to rank the volatility estimators. For a n-period time interval, these functions have the following representations, where 𝑉 𝑖 is the true, unobserved volatility, employed as a benchmark, and 𝑉 ̂𝑖 is the estimated volatility provided by one of the estimators for each period i in the interval:…”

Section: Discussionmentioning

confidence: 99%

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

Vințe

Ausloos

Furtuną

2021

Entropy

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Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

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Section: Discussionmentioning

confidence: 99%

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

Vințe

Ausloos

Furtuną

2021

Entropy

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“…Even though all the mentioned range-based volatility estimators have different characteristics, one can figure out that there are no certain results in the literature about which estimator performs more accurately. Arnerić et al (2019) find no exact result for the comparison. They use two different metrics to calculate the forecast error.…”

Section: Introductionmentioning

confidence: 88%

Untitled

2022

JLECON

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is an international peer-reviewed and periodical journal. It has been published since 2014. It aims to create a forum where the economic fundamentals of life are discussed. In this perspective, it brings together the views and studies of academics, researchers and professionals who shape their work on the basis of economy. High quality theoretical and applied articles are published. Journal of Life Economics includes studies in fields such as Economics, Business and Marketing, Finance, Accounting, Banking, Econometrics, Labor Economics and so on.The articles in the Journal is published in 4 times a year; WINTER (January), SPRING (April), SUMMER (July) and AUTUMN (October).

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“…In order to define the optimal slow time scale sampling frequency k, the root mean square error (RMSE) of the benchmark RTSRV ∆,k,θ t is used. The RMSE of the benchmark for each stock index was calculated as the sum of its squared bias and its variance, and afterwards the RMSE was minimized with respect to slow time scale frequency k, which corresponds to the optimal number of non-overlapping subsamples or subgrids, over which the slow time scales sum of squared returns is averaged [29].…”

Section: Sampling Frequency Selectionmentioning

confidence: 99%

“…This research utilizes the upper tail dependence coefficient, a result of the Gumbel copula function, which is found useful for measuring extreme dependence, i.e., a dependence above a high quartile. While the Mincer-Zarnowitz regression and Kolmogorov-Smirnov test have not demonstrated the preference of a specific estimator, the copula-based approach can be a powerful and suitable for comparison [29]. The paper focuses on a Gumbel copula as an extreme value copula that is not elliptical, due to the similar behavior of volatility estimator over time, and sometimes takes on extremely large values.…”

Section: Upper Tail Dependencementioning

confidence: 99%

Is Jump Robust Two Times Scaled Estimator Superior among Realized Volatility Competitors?

Čuljak¹,

Arnerić

Žigman³

2022

Mathematics

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This paper compares the empirical performance of the realized volatility estimators on an extensive high-frequency dataset of stock indices from four developed European markets with thick trading and intensive intraday activity. Even though the proposed estimators have distinctive properties, it is not clear which one has a better performance in terms of unbiasedness and consistency. Some of them are robust to microstructure noise only, and others are robust solely to price jumps, whereas a few of them are robust to both. Therefore, the main purpose is finding a benchmark estimator among alternative competitors, as the best proxy of integrated variance, and empirical demonstration of its superiority. The vast majority of the existing studies largely rely on developed US data or simulation data, but inferences obtained on such data might deviate from European developed markets. This study aims to fill in that niche. In particular, the optimal sampling frequency of proposed benchmark estimator is determined with respect to the trade-off between its bias and the variance of each stock index individually. Afterwards, probability integral transformation, Mincer–Zarnowitz regression and upper tail correlation from appropriate copula function are considered as an adequate pairwise comparison methods. Notable contributions of this paper include unambiguously proven superiority of robust two times scaled estimator for selected European developed markets within the range of optimal slow time frequency from 10 to 30 s. Finally, recommendations for research and practitioners regarding the usage of jump robust two times scaled estimator are given. In fact, asset managers, institutional investors as well as market regulators could benefit from proposed realized volatility benchmark in making long-term investment decisions, leading to sustainable finance.

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Comparison of range-based volatility estimators against integrated volatility in European emerging markets

Cited by 7 publications

References 17 publications

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

Untitled

Is Jump Robust Two Times Scaled Estimator Superior among Realized Volatility Competitors?

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