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This paper considers the use of multiple noisy daily realized variance measures to extract a denoised latent variance process. The class of stochastic volatility models used for signal extraction has the important feature that they can be written as a linear state space model. As a result, prediction of the denoised latent variance and likelihood evaluation can be carried out efficiently using the Kalman filter. This is in contrast to stochastic models that jointly model the return and variance, which require computationally expensive nonlinear filtering for prediction and inference. The gain from using multiple noisy daily variance measures is examined empirically for the S&P 500 index using daily OHLC (open-high-low-close) data. [ABSTRACT FROM AUTHOR]

This paper proposes a model for realized variance that exploits information in realized quarticity. The realized variance and quarticity measures are both highly persistent and highly correlated with each other. The proposed model incorporates information from the observed realized quarticity process via autoregressive conditional variance dynamics. It exploits conditional dependence in higher order (fourth) moments in analogy to the class of GARCH models exploit conditional dependence in second moments. [ABSTRACT FROM AUTHOR]

This paper specifies a semiparametric variance impulse response function using realized variances. The news impact and impulse responses are estimated using local projection methods using least squares and external instruments. Compared to impulse responses estimated from parametric GARCH type models, semiparametric local projection responses show less persistence though the estimates are quite noisy. [ABSTRACT FROM AUTHOR]

This paper evaluates the information content in daily volatility measures that utilize OHLC (Open‐High‐Low‐Close) price data. An encompassing regression framework is used to evaluate the absolute and relative information contain in such measures. 2‐step GMM (generalized method of moments) estimates using two sets of instruments are used to address potential bias from measurement errors. The evidence using S&P 500 index data suggest that volatility measures that use OHLC data encompass those based only on close‐to‐close returns or high‐minus‐low ranges. However, the proposed instruments do not all pass statistical tests of instrument validity and the identification robust confidence set can be quite large. [ABSTRACT FROM AUTHOR]

This paper considers a class of multivariate ARCH models with scalar weights. A new specification with hyperbolic weighted moving average (HWMA) is proposed as an analogue of the EWMA model. Despite the restrictive dynamics of a scalar weight model, the proposed model has a number of advantages that can deal with the curse of dimensionality. The empirical application illustrates that the (pseudo) out-of-sample multistep forecasts can be surprisingly more accurate than those from the DCC model. [ABSTRACT FROM AUTHOR]

Purpose This study aims to examine the relation between population composition and financial market variables in post-war Japan.Design/methodology/approach Cointegration and Granger causality tests are applied to annual data for the period 1948-2015.Findings Accounting for nonstationarity, this study finds long-run equilibrium relations between real financial price (stock and house) indices and the proportion of population in the prime earning (45-64) or retirement (65+) age. Granger causality tests that account for possibly nonstationary variables find some evidence of dynamic causation running from the 45-64 cohort to the real financial price indices. No such evidence is found for the 65+ cohort.Originality/value This study complements the existing literature primarily based on US data with analysis of Japanese data that has some unique population composition features. [ABSTRACT FROM AUTHOR]

This paper examines the performance of iterated and direct forecasts for the number of shares traded in high-frequency intraday data. Constructing direct forecasts in the context of formulating volume weighted average price trading strategies requires the generation of a sequence of multistep-ahead forecasts. I discuss nonlinear transformations to ensure nonnegative forecasts and lag length selection for generating a sequence of direct forecasts. In contrast to the literature based on low-frequency macroeconomic data, I find that direct multiperiod forecasts can outperform iterated forecasts when the conditioning information set is dynamically updated in real time. [ABSTRACT FROM AUTHOR]

We propose an EM algorithm to estimate ordered probit models with endogenous regressors. The proposed algorithm has a number of computational advantages in comparison to direct numerical maximization of the (limited information) log-likelihood function. First, the sequence of conditional M(aximization)-steps can all be computed analytically. Second, the algorithm updates the model parameters so that positive definiteness of the covariance matrix and monotonicity of cutpoints are naturally satisfied. Third, the variance parameters normalized for identification can be activated to accelerate convergence of the algorithm. The algorithm can be applied to models with dummy endogenous, continuous endogenous or latent endogenous regressors. A small Monte Carlo simulation experiment examines the finite sample performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

I consider Gaussian filters based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. I show that for this class of models, the prediction step of the Gaussian filter can be evaluated analytically without linearizing the state–space model. Monte Carlo simulations show that the mixture Gaussian filter performs remarkably well in terms of both accuracy and computation time compared to the quasi-maximum likelihood and importance sampler filters. The result that the prediction step of the Gaussian filter can be evaluated analytically is shown to apply more generally to a number of commonly used specifications of the stochastic volatility model. [ABSTRACT FROM AUTHOR]

The paper considers the problem of computing p-values of non-standard distributions for which the characteristic function is available in closed form. When the characteristic function is a multivalued complex function, the standard numerical inversion method needs to be used with care as the integrand may become discontinous due to branch cuts. An alternative inversion method based on the Gaver-Wynn-Rho algorithm is shown to be a general and effective solution to the discontinuity problem as it works with real-valued functions. The method is illustrated with two well-known time series tests with non-standard distributions. [ABSTRACT FROM AUTHOR]

Presents a study which examined the effect of economic and financial liberalization on the predictability of real stock returns in emerging markets. Investigation on the time series properties before and after financial liberalization; Difference between economic and financial liberalization; Methodology of the study; Importance of asset allocation models development.

The efficient markets hypothesis in finance suggests that as equity markets are liberalized and made more open to the public, equity prices should reflect the increased availability of information and be more efficiently priced. In this paper, we examine whether emerging market equity prices have become more efficient after financial liberalization. Using two sets of financial liberalization dates, a battery of econometric tests, and data from sixteen countries and three composite portfolios, we find that in spite of theory suggesting the opposite, liberalization does not seem to have improved the efficiency of emerging markets. In fact, most of our statistical tests indicate that the markets were already efficient before the actual liberalization. [ABSTRACT FROM AUTHOR]

This paper considers observation driven models with conditional mean and variance dynamics for non-negative valued time series. The motivation is to relax the restriction imposed on the higher order moment dynamics in standard multiplicative error models driven only by the conditional mean dynamics. The empirical fit of a zero inflated mixture distribution is assessed with trade duration data with a large fraction of zero observations. All authors have read and agreed to the published version of the manuscript. [ABSTRACT FROM AUTHOR]

A correction to the article "Numerical Inversion Methods for Computing Approximate p-Values" that was published in the December 4, 2005 issue is presented.