Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Social and Behavioral Sciences
Estimating The Garch Diffusion: Simulated Maximum Likelihood In Continuous Time, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Estimating The Garch Diffusion: Simulated Maximum Likelihood In Continuous Time, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Research Collection School Of Economics
A new algorithm is developed to provide a simulated maximum likelihood estimation of the GARCH diffusion model of Nelson (1990) based on return data only. The method combines two accurate approximation procedures, namely, the polynomial expansion of Ait-Sahalia (2008) to approximate the transition probability density of return and volatility, and the Efficient Importance Sampler (EIS) of Richard and Zhang (2007) to integrate out the volatility. The first and second order terms in the polynomial expansion are used to generate a base-line importance density for an EIS algorithm. The higher order terms are included when evaluating the importance weights. Monte Carlo …
Realized Daily Variance Of S&P 500 Cash Index: A Revaluation Of Stylized Facts, Shirley Huang, Qianqiu Liu, Jun Yu
Realized Daily Variance Of S&P 500 Cash Index: A Revaluation Of Stylized Facts, Shirley Huang, Qianqiu Liu, Jun Yu
Research Collection School Of Economics
In this paper the realized daily variance is obtained from intraday transaction prices of the S&P 500 cash index over the period from January 1993 to December 2004. When constructing realized daily variance, market microstructure noise is taken into account using a technique proposed by Zhang, Mykland and Ait-Sahalia (2005). The time series properties of realized daily variance are compared with those of variance estimates obtained from parametric GARCH and stochastic volatility models. Unconditional and dynamic properties concerning the realized daily variance are examined, the relationship between realized variance and returns is investigated, and the stylized facts concerning realized daily …
Temporal Aggregation And Risk-Return Relation, Jin Xing, Leping Wang, Jun Yu
Temporal Aggregation And Risk-Return Relation, Jin Xing, Leping Wang, Jun Yu
Research Collection School Of Economics
The function form of a linear intertemporal relation between risk and return is suggested by Merton's [1973. Econometrica 41, 867–887] analytical work for instantaneous returns, whereas empirical studies have examined the nature of this relation using temporally aggregated data, i.e., daily, monthly, quarterly, or even yearly returns. Our paper carefully examines the temporal aggregation effect on the validity of the linear specification of the risk–return relation at discrete horizons, and on its implications on the reliability of the resulting inference about the risk–return relation based on different observation intervals. Surprisingly, we show that, based on the standard Heston's [1993. Review …
Bugs For A Bayesian Analysis Of Stochastic Volatility Models, Renate Meyer, Jun Yu
Bugs For A Bayesian Analysis Of Stochastic Volatility Models, Renate Meyer, Jun Yu
Research Collection School Of Economics
This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ease with which the Bayesian stochastic volatility model can now be studied routinely via BUGS (Bayesian inference using Gibbs sampling), a recently developed, user-friendly, and freely available software package. It is an ideal software tool for the exploratory phase of model building as any modifications of a model including changes of priors and sampling error distributions are readily realized with only minor changes of the code. However, due to the single move …