Realized Volatility and Stylized Facts of Chinese Treasury Bond Market LA VOLATILITÉ RÉALISÉE ET LES FAITS STYLISÉS DU MARCHÉ DE BON DU TRÉSOR CHINOIS

Abstract: Based on high frequency data, this paper studies the volatility stylized facts of Chinese Treasury bond market (CTBM) in detail, including the best sampling frequency selected to compute the realized volatility, the conditional and unconditional distribution of the returns, the long memory property, the intraday, inter-day pattern of the returns and volatility, the asymmetry of volatility, and so on. The main conclusions about CTBM volatility are provided. 15 minute is best sampling frequency. The RV-based conditional distribution of return is nearly normal. Both return and volatility have significant inter-day but insignificant intraday periodicity. Moreover, the volatility asymmetry existing widely in stock or exchange market is not significant in Chinese Treasury bond market. Key words: Realized volatility, Chinese Treasury bond market, High frequency data Resume: Base sur des donnees de haute frequence, le present article etudie en detail la volatilite des faits stylises du Marche de bon du Tresor chinois (MBTC), comprenant la meilleure frequence de prelevement selectionnee pour calculer la volatilite realisee, la distribution conditionnelle et inconditionnelle des retours, la propriete de longue memoire, le modele intrajour et interjour des retours et la volatilite, l'asymetrie de volatilite, etc. Les conclusions principales sur la volatilite du MBTC sont les suivantes : 15 minutes est la meilleure frequence de prelevement, la distribution conditionnelle RV-base du retour est presque normale. Le retour et l'asymetrie de volatilite ont tous les deux une periodicite inter-jour signifiante, mais une periodicite intrajour insignifiante. D'ailleurs, l'asymetrie de volatilite existant amplement dans la bourse et le marche des changes n'est pas importante sur le Marche de bon du Tresor chinois. Mots-Cles: volatilite realisee, Marche de bon du Tresor chinois, donnees de haute frequence (ProQuest: . denotes formulae omitted.) INTRODUCTIONS In financial time series analysis, it is almost no use to predicate the first moment of the return (or price) of an asset but to study its variance (or volatility). Our ability to estimate time variation in expected returns is hardly improved but we achieve potentially huge gains in our ability to monitor variation in return volatility, or second moments of returns (Andersen et al, 2000). Volatility is an essential ingredient for many applied issues in finance and financial engineering, such as in asset pricing, asset allocation, and risk management (Corsi et al, 2001). After Markowitz quantitative described the volatility firstly in 1952, volatility modeling and forecasting have become one of the most popular topics in finance. Because the volatility of Markowitz is calculated from historical data and is not time-varying, it is also named historical volatility. To describe the time varying of the variance, Engle (1982) proposed the ARCH model. In the following years, the advance of ARCH-based models such as GARCH (Bollerslev, 1986), TARCH (Zakoian, 1994), EGARCH (Nelson, 1991), GARCH-M (Engle, 1987) etc. give us more capability to solve different special problems in financial markets, such as long-memory of volatility, the effect of conditional variance on conditional mean, or persistence of volatility and so on. In the last few years, a new method of volatility modeling based on high frequency data was constructed by Andersen, Bollerslev, Diebold, Labys (ABDL hereafter, 1997, 1999) named realized volatility (RV). Comparing with the former volatility, realized volatility is observable and free of model. ABDL (2000) has shown that by sampling intra-day returns sufficiently frequently, the realized volatility can be arbitrarily closed to the underlying integrated volatility, which is a natural volatility measure. What's more important, based on high frequency data, realized volatility can provide a benchmark to evaluate other volatility models (Andersen et al, 2003). …