Meihui Guo

Goodness-of-fit test for stochastic volatility models

In this paper, we propose a goodness of fit test for continuous time stochastic volatility models based on discretely sampled observations. The proposed test is constructed by measuring deviations between the empirical and true characteristic functions obtained from the hypothesized stochastic volatility model. In this study, both the test statistics based on the fixed and decreasing sampling schemes are taken into consideration. It is shown that under the null, the proposed tests asymptotically follow a weighted sum of products of centered normal random variables. In order to evaluate the proposed tests, a simulation study is performed, in which a bootstrap method is also considered. Finally, a real data analysis is conducted for illustration.

A paradox in least-squares estimation of linear regression models

This note considers a paradox arising in the least-squares estimation of linear regression models in which the error terms are assumed to be i.i.d. and possess finite rth moment, for r[set membership, variant][1,2). We give a concrete example to show that the least-squares estimator of the slope parameter is inconsistent when the intercept parameter of the model is given. However, surprisingly this estimator is consistent when the intercept parameter is intendedly assumed to be unknown and re-estimated simultaneously with the slope parameter.

Model risk of the implied GARCH-normal model

Model risk causes significant losses in financial derivative pricing and hedging. Investors may undertake relatively risky investments due to insufficient hedging or overpaying implied by flawed models. The GARCH model with normal innovations (GARCH-normal) has been adopted to depict the dynamics of the returns in many applications. The implied GARCH-normal model is the one minimizing the mean square error between the market option values and the GARCH-normal option prices. In this study, we investigate the model risk of the implied GARCH-normal model fitted to conditional leptokurtic returns, an important feature of financial data. The risk-neutral GARCH model with conditional leptokurtic innovations is derived by the extended Girsanov principle. The option prices and hedging positions of the conditional leptokurtic GARCH models are obtained by extending the dynamic semiparametric approach of Huang and Guo [Statist. Sin., 2009, 19, 1037–1054]. In the simulation study we find significant model risk of the implied GARCH-normal model in pricing and hedging barrier and lookback options when the underlying dynamics follow a GARCH-t model.

Fuzzy thermal placement for multichip module applications

This paper presents an algorithm based on the fuzzy logic approach for the placement of the power dissipating chips on the multichip module substrate. In the conventional force-directed placement method, the forces are related to the number of connection among the modules. However, unlike the conventional force-directed placement method, the fuzzy approach is used to model the force interaction among the chips in our placement method. The force interaction and the distance between two chips are chosen as linguistic variables. The fuzzy placement analysis uses the Z and S membership functions to describe the repulsive and attractive forces among the modules. The proposed thermal force-directed placement method is then to relate the force equations to the power dissipation values of the individual bare chip. Details of implementation of our placement algorithm are provided. To verify the thermal placement prediction, the finite element analysis is carried out to map the thermal distribution of all the chips in the multichip module. Our fuzzy logic-based thermal placement method is applied to a multichip module for a case study. The finite element simulation results show our approach can obtain better thermal distribution than other traditional placement methods.

A lower bound for expectation of a convex functional

Let [phi] be a symmetric convex function from n to . Under certain conditional symmetric conditions on the random variables X1,...,Xn, the inequality:E[[phi](X1,...,Xn)] [greater-or-equal, slanted] E[maxi [less-than-or-equals, slant] i [less-than-or-equals, slant] n[phi](0,...,0, Xi, 0,...,0)] is derived. Conditions under which the strict inequality holds are also obtained. Application to nonlinear autoregressive models and symmetrization of random variables are given.

Assessing influential trade effects via high-frequency market reactions

In the literature, traders are often classified into informed and uninformed and the trades from informed traders have market impacts. We investigate these trades by first establishing a scheme to identify the influential trades from the ordinary trades under certain criteria. The differential properties between these two types of trades are examined via the four transaction states classified by the trade price, trade volume, quotes, and quoted depth. Marginal distribution of the four states and the transition probability between different states are shown to be distinct for informed trades and ordinary liquidity trades. Furthermore, four market reaction factors are introduced and logistic regression models of the influential trades are established based on these four factors. Empirical study on the high-frequency transaction data from the NYSE TAQ database show supportive evidence for high correct classification rates of the logistic regression models.

Estimation of MA(1) model based on rounded data

ABSTRACT Most recorded data of continuous distributions are rounded to the nearest decimal place due to the precision of the recording mechanism. This rounding entails errors in estimation and measurement. In this study, we consider parameter estimation of time series models based on rounded data. The adjusted maximum likelihood estimates in [Stam, A.-Cogger, K. O.: Rounding errors in autoregressive processes, Internat. J. Forecast. 9 (1993), 487-508] are derived theoretically for the first order moving average MA(1) model. Simulations are performed to compare the efficiencies of the adjusted maximum likelihood estimators with other estimators.