Least squares asymptotics in spurious and cointegrated panel regressions with common and idiosyncratic stochastic trends

This article makes an analytical study of the effects of the presence of both common and idiosyncratic stochastic trends on the pooled least squares estimator. The results suggest that the usual result of asymptotic normality depends critically on the absence of the common stochastic trend.

The volume and volatility relation in the Australian stock market

The thesis studies the volume-volatility relation in the Australian Securities Market. It is concluded that the number of trades is the most important variable driving realized volatility. The average trade size is significant but its explanatory power is only trivial. Order imbalance does not drive volatility in the Australian market.

Financial development, shocks, and growth volatility

This paper uses spectral theory to develop the following two testable hypotheses in a unified framework for the predictions of business-cycle and endogenous growth models: (i) financial development affects only business-cycle volatility; and (ii) shocks affect both business-cycle volatility and long-run volatility of GDP growth. In other words, volatility caused by shocks is more persistent than that caused by financial underdevelopment. We decompose the business-cycle and long-run volatility by the spectral method and then test the hypotheses at the cross-country level. Empirical evidence provides support for both hypotheses. Higher private credit, a bank-based measure of financial development, dampens business-cycle volatility but not long-run volatility. Volatility of shocks, as measured by the volatility of changes in the terms of trade, magnifies both business-cycle and long-run volatility. The results are robust to accounting for endogeneity, a market-based measure of financial development, and an alternative method of volatility decomposition.

Non-homogeneous volatility correlations in the bivariate multifractal model

In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. "Volatility Comovement: A Multifrequency Approach." Journal of Econometrics {131}: 179-215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models." Journal of Business & Economic Statistics 20 (3): 339-350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model. © 2014 Taylor & Francis.