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on Econometric Time Series |
| By: | Gregory H. Bauer; Keith Vorkink |
| Abstract: | We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of both lagged volatility and returns. The model has several advantages: it is parsimonious; it does not require imposing parameter restrictions; and, it results in a positive-definite covariance matrix. We apply the model to the covariance matrix of size-sorted stock returns and find that two factors are sufficient to capture most of the dynamics. We also introduce a new method to track an index using our model of the realized volatility covariance matrix. |
| Keywords: | Econometric and statistical methods; Financial markets |
| JEL: | G14 C53 C32 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:07-20&r=ets |
| By: | Manuel Gomez (School of Economics, Universidad de Guanajuato); Daniel Ventosa-Santaularia (School of Economics, Universidad de Guanajuato) |
| Abstract: | We investigate the efficiency of the Dickey-Fuller (DF) test as a tool to examine the convergence hypothesis. In doing so, we first describe two possible outcomes, overlooked in previous studies, namely Loose Catching-up and Loose Lagging-behind. Results suggest that this test is useful when the intention is to discriminate between a unit root process and a trend stationary process, though unreliable when used to differentiate between a unit root process and a process with both deterministic and stochastic trends. This issue may explain the lack of support for the convergence hypothesis in the aforementioned literature. |
| Keywords: | Divergence, Loose Catching-up/Lagging-behind, Convergence, Deterministic and Stochastic trends |
| JEL: | C32 O40 |
| URL: | http://d.repec.org/n?u=RePEc:gua:wpaper:em200703&r=ets |
| By: | Fulvio Corsi; Francesco Audrino |
| Abstract: | We propose the Heterogeneous Autoregressive (HAR) model for the estimation and prediction of realized correlations. We construct a realized correlation measure where both the volatilities and the covariances are computed from tick-by-tick data. As for the realized volatility, the presence of market microstructure can induce significant bias in standard realized covariance measure computed with artificially regularly spaced returns. Contrary to these standard approaches we analyse a simple and unbiased realized covariance estimator that does not resort to the construction of a regular grid, but directly and efficiently employs the raw tick-by-tick returns of the two series. Montecarlo simulations calibrated on realistic market microstructure conditions show that this simple tick-by-tick covariance possesses no bias and the smallest dispersion among the covariance estimators considered in the study. In an empirical analysis on S&P 500 and US bond data we find that realized correlations show significant regime changes in reaction to financial crises. Such regimes must be taken into account to get reliable estimates and forecasts. |
| Keywords: | High frequency data, Realized Correlation, Market Microstructure, Bias correction, HAR, Regimes |
| JEL: | C13 C22 C51 C53 |
| Date: | 2007–01 |
| URL: | http://d.repec.org/n?u=RePEc:usg:dp2007:2007-02&r=ets |
| By: | David N. DeJong; Hariharan Dharmarajan; Roman Liesenfeld; Jean-Francois Richard |
| Abstract: | . . . |
| Date: | 2007–03 |
| URL: | http://d.repec.org/n?u=RePEc:pit:wpaper:300&r=ets |