Elementary abelian 2 subgroups of compact Lie groups

We classify elementary abelian 2 subgroups of compact simple Lie groups of adjoint type. This finishes the classification of elementary abelian $p$ subgroups of compact (or linear algebraic) simple groups of adjoint type.Comment: 40 pages, comments are welcom

Simulation-based Estimation Methods for Financial Time Series Models

This chapter overviews some recent advances on simulation-based methods of estimating financial time series models that are widely used in financial economics. The simulation-based methods have proven to be particularly useful when the likelihood function and moments do not have tractable forms, and hence, the maximum likelihood (ML) method and the generalized method of moments (GMM) are diffcult to use. They are also capable of improving the finite sample performance of the traditional methods. Both frequentist's and Bayesian simulation-based methods are reviewed. Frequentist's simulation-based methods cover various forms of simulated maximum likelihood (SML) methods, the simulated generalized method of moments (SGMM), the efficient method of moments (EMM), and the indirect inference (II) method. Bayesian simulation-based methods cover various MCMC algorithms. Each simulation-based method is discussed in the context of a specific financial time series model as a motivating example. Empirical applications, based on real exchange rates, interest rates and equity data, illustrate how the simulation-based methods are implemented. In particular, SML is applied to a discrete time stochastic volatility model, EMM to estimate a continuous time stochastic volatility model, MCMC to a credit risk model, the II method to a term structure model.Generalized method of moments, Maximum likelihood, MCMC, Indirect Inference, Credit risk, Stock price, Exchange rate, Interest rate..

Asymmetric Response of Volatility: Evidence from Stochastic Volatility Models and Realized Volatility

This paper examines the asymmetric response of equity volatility to return shocks. We generalize the news impact function (NIF), originally introduced by Engle and Ng (1993) to study asymmetric volatility under the ARCH-type models, to be applicable to both stochastic volatility (SV) and ARCH-type models. Based on the generalized concept, we provide a unified framework to examine asymmetric properties of volatility. A new asymmetric volatility model, which nests both ARCH and SV models and at the same time allows for a more flexible NIF, is proposed. Empirical results based on daily index return data support the classical asymmetric SV model with a monotonically decreasing NIF. This empirical result is further reinforced by the realized volatility obtained from high frequency intraday data. We document the option pricing implications of these findings.Bayes factors; Leverage effect; Markov chain Monte Carlo; EGARCH; Realized volatility; Asymmetric volatility

Temporal Aggregation and Risk-Return Relation

The function form of a linear intertemporal relation between risk and return is suggested by Merton’s (1973) 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 reliablility 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) dynamics, the linear relation between risk and return will not be distorted by the temporal aggregation at all. Neither will the sign of this relation be flipped by the temporal aggregation, even at the yearly horizon. This finding excludes the temporal aggregation issue as a potential source for the conflicting empirical evidence about the risk-return relation in the earlier studies.