Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models

Racicot, François-Éric; Théoret, Raymond; Coën, Alain

doi:10.1007/s11294-008-9134-2

Cited by 18 publications

(7 citation statements)

References 21 publications

(48 reference statements)

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“…This could be an interesting future path of research, because these type of strategies have been successfully applied to other field of science, although they cannot be easily implemented in any of the standard software packages (see Harvey & Shephard [ 34 ]; Ruiz & Veiga [ 35 ], García & Mínguez [ 36 ]; Montero, Fernández-Avilés & García [ 37 ]; Montero, García & Fernández-Avilés [ 38 ]). Another promising future research line is the use of ultra-high frequency measures of volatility (Racicot, Théoret & Coen [ 39 ]) in the field of cryptocurrencies.…”

Section: Discussionmentioning

confidence: 99%

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

2021

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This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies’ volatility and revealed persistence and “intensifying” levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.

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Section: Discussionmentioning

confidence: 99%

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

2021

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“…A more generalized LM tests of the ACD models are proposed by Meitz and Terasvirta (2006), including tests against higher-order of ACD models and tests against no remaining ACD effects in the standardized duration. Moreover, Racicot, Théoret and Coen (2008) employ GARCH model, ACD-GARCH model, extended ACD-GARCH model and ultra-high-frequency GARCH (UHF-GARCH) models to forecast volatility. Their empirical results indicate that the ACD-GARCH model has not performed better than the realized volatility.…”

Section: Literature Reviewmentioning

confidence: 99%

Block Trading Based Volatility Forecasting: An Application of VACD-FIGARCH Model

Tu¹,

LIAO²

2020

JAFEB

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The purpose of this study is to construct the ACD model for the block trading volume duration. The ACD model based on the block trading volume duration is referred to as Volume ACD (VACD) in this study. By integrating with GARCH-type models, the VACD based GARCH type models, which include VACD-GARCH, VACD-IGARCH and VACD-FIGARCH models, are set up. This study selects Chunghwa Telecom (CHT) Inc., offering the America Depository Receipt (ADR) in NYSE, to investigate the block trading volume duration in Taiwanese equity market. The empirical results indicate that the long memory in volume duration series increases dependence at level of volatility clustering by VACD (2,1)-FIGARCH (3,d,1) model. Moreover, the VACD (2,1)-IGARCH (1,1) exhibits relatively better performance of prediction on capturing block trading volume duration. This volatility model is more appropriate in this study to portray the change of the CHT Inc. prices and provides more information about the volatility process for investment strategy, which can be a reference indicator of financial asset pricing, hedging strategy and risk management.

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“…For these two financial shocks indicators, we used the realized volatility. Indeed, as stressed, by Racicot et al (2008), Chan et al (2009), andAndersen et al (2010), among others, the realized volatility is an ex post nonparametric and unbiased volatility estimator allowing us to obtain an indicator of volatility very close to the true volatility.…”

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confidence: 97%

Macroeconomic policy responses to financial crises in emerging European economies

Josifidis¹,

Allégret²,

Gimet³

et al. 2014

Economic Modelling

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Abstract:This paper contributes to the literature on monetary policy responses in emerging economies to international financial crises. Such issue is especially relevant for these countries insofar as they tend to be more unstable than developed countries. In addition, they suffer from larger cumulative output losses that have long-lasting negative effects on growth. If the earlier literature has suggested that emerging countries conduct pro-cyclical policies that exacerbate the impact of shocks, recent findings drawn from the experience of the global financial crisis show that they tend to more frequently adopt counter-cyclical monetary policies. However, even in the last crisis, all countries did not conduct expansionary monetary policies. Among the factors explaining such a behavior, the literature identifies the currency mismatch. This paper is related to this literature. It analyzes monetary policy responses to common financial shocks over the period 1995-2010 for a sample of ten emerging European countries. Emerging Europe has especially suffered from the global financial crisis. Three monetary instruments are analyzed: the nominal short-term interest rate, the real exchange rate, the foreign exchange reserves. Our empirical methodology used Structural Bayesian vector autoregressive (SBVAR) models over two crises periods (1995Q1-2001Q4 and 2002Q1-2010Q4). Our main findings are the following. First, common international financial shocks lead to different monetary policy responses. Second, countries with high currency mismatch ratios suffer from both fear of floating and fear of losing international reserves.Key words: Financial crises, External shocks, Emerging European Countries, Structural Bayesian VAR model. JEL: G1, E5, E6. 2 Macroeconomic Policy Responses to Financial Crises in Emerging European Economies Abstract:This paper contributes to the literature on monetary policy responses in emerging economies to international financial crises. Such issue is especially relevant for these countries insofar as they tend to be more unstable than developed countries. In addition, they suffer from larger cumulative output losses that have long-lasting negative effects on growth. If the earlier literature has suggested that emerging countries conduct pro-cyclical policies that exacerbate the impact of shocks, recent findings drawn from the experience of the global financial crisis show that they tend to more frequently adopt counter-cyclical monetary policies. However, even in the last crisis, all countries did not conduct expansionary monetary policies. Among the factors explaining such a behavior, the literature identifies the currency mismatch. This paper is related to this literature. It analyzes monetary policy responses to common financial shocks over the period 1995-2010 for a sample of ten emerging European countries. Emerging Europe has especially suffered from the global financial crisis. Three monetary instruments are analyzed: the nominal short-term interest rate, the real exchange rate, the foreign ex...

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Forecasting Irregularly Spaced UHF Financial Data: Realized Volatility vs UHF-GARCH Models

Abstract: Realized volatility, UHF-GARCH, Time deformation, Financial markets, Daily VaR, Historical simulation, C10, G20,

Cited by 18 publications

References 21 publications

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

Block Trading Based Volatility Forecasting: An Application of VACD-FIGARCH Model

Macroeconomic policy responses to financial crises in emerging European economies

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