Realized Measures to Explain Volatility Changes over Time

Floros, Christos; Γκίλλας, Κωνσταντίνος; Konstantatos, Christoforos; Τσαγκανός, Αθανάσιος

doi:10.3390/jrfm13060125

Cited by 15 publications

(7 citation statements)

References 63 publications

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“…As research in finance has increasingly focused on the behaviour of high-frequency data in recent decades, the availability of high-frequency data also opens up further research possibilities with respect to the analysis of the information efficiency of global LPE markets. For example, there is an opportunity to extend the results obtained in this paper to provide additional insights based on the heterogeneous market hypothesis (Dacorogna et al 2001) using high-frequency data in combination with realized volatility estimators (Floros et al 2020).…”

Section: Discussionmentioning

confidence: 89%

Testing the Efficiency of Globally Listed Private Equity Markets

Tegtmeier

2021

JRFM

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This study is the first to investigate the efficient market hypothesis in its weak form and the random walk behaviour of globally listed private equity (LPE) markets represented by nine global, regional, and style indices based on weekly data covering the period from January 2004 to December 2020. Autocorrelation tests, variance ratio tests, and a non-parametric runs test are employed. The results of the autocorrelation tests and the variance ratio tests tend to correspond for all indices, and they reject the random walk hypothesis for the returns of all LPE indices under investigation. In contrast, the runs test for direct weak-form market efficiency cannot reject the null hypothesis of a random walk process for almost all LPE indices under investigation. Furthermore, there is no evidence that the market efficiency of globally listed private equity markets has improved after the global financial crisis. Due to the fact that the rapidly growing asset class of LPE as a form of private equity is still relatively unknown, the implications of the results of our paper are relevant for investors, policy makers, and academics alike. In addition, the results provide valuable insights to better understand the emerging asset class of LPE.

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Section: Discussionmentioning

confidence: 89%

Testing the Efficiency of Globally Listed Private Equity Markets

Tegtmeier

2021

JRFM

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“…In fact, empirical studies take realized measures of volatility as an exogenous variable as it is much more informative on the volatility level than the squared returns rate (Hansen et al, 2012). Realized measures of volatility are theoretically high-frequency and are non-parametric estimators of variations in asset value changes when traded frequently (Floros et al, 2020).…”

Section: Exogenous Variablementioning

confidence: 99%

GARCH-X(1; 1) model allowing a non-linear function of the variance to follow an AR(1) process

Nugroho

Wicaksono

Larwuy

2023

Communications for Statistical Applications and Methods

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GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCH-X(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

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“…It is a common approach in the realized volatility literature (see e.g. Floros et al, 2020;Reschenhofer et al, 2020, Zhang et al, 2020Gkillas et al, 2021;Kambouroudis et al, 2021) 1 . We use percentage returns calculated as 7 = 100 ln(5 /6 ).…”

Section: Data Appliedmentioning

confidence: 99%

Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies

Fiszeder

Małecka

2022

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Research background: The Russian invasion on Ukraine of February 24, 2022 sharply raised the volatility in commodity and financial markets. This had the adverse effect on the accuracy of volatility forecasts. The scale of negative effects of war was, however, market-specific and some markets exhibited a strong tendency to return to usual levels in a short time. Purpose of the article: We study the volatility shocks caused by the war. Our focus is on the markets highly exposed to the effects of this conflict: the stock, currency, cryptocurrency, gold, wheat and crude oil markets. We evaluate the forecasting accuracy of volatility models during the first stage of the war and compare the scale of forecast deterioration among the examined markets. Our long-term purpose is to analyze the methods that have the potential to mitigate the effect of forecast deterioration under such circumstances. We concentrate on the methods designed to deal with outliers and periods of extreme volatility, but, so far, have not been investigated empirically under the conditions of war. Methods: We use the robust methods of estimation and a modified Range-GARCH model which is based on opening, low, high and closing prices. We compare them with the standard maximum likelihood method of the classic GARCH model. Moreover, we employ the MCS (Model Confidence Set) procedure to create the set of superior models. Findings & value added: Analyzing the market specificity, we identify both some common patterns and substantial differences among the markets, which is the first comparison of this type relating to the ongoing conflict. In particular, we discover the individual nature of the cryptocurrency markets, where the reaction to the outbreak of the war was very limited and the accuracy of forecasts remained at the similar level before and after the beginning of the war. Our long-term contribution are the findings about suitability of methods that have the potential to handle the extreme volatility but have not been examined empirically under the conditions of war. We reveal that the Range-GARCH model compares favorably with the standard volatility models, even when the latter are evaluated in a robust way. It gives valuable implication for the future research connected with military conflicts, showing that in such period gains from using more market information outweigh the benefits of using robust estimators.

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Realized Measures to Explain Volatility Changes over Time

Cited by 15 publications

References 63 publications

Testing the Efficiency of Globally Listed Private Equity Markets

Testing the Efficiency of Globally Listed Private Equity Markets

GARCH-X(1; 1) model allowing a non-linear function of the variance to follow an AR(1) process

Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies

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