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on Econometric Time Series |
| By: | Michael W. Brandt (Department of Finance, University of Pennsylvania, and NBER); Francis X. Diebold (Departments of Economics, Finance and Statistics, University of Pennsylvania, and NBER) |
| Abstract: | We extend the important idea of range-based volatility estimation to the multivariate case. In particular, we propose a range-based covariance estimator that is motivated by financial economic considerations (the absence of arbitrage), in addition to statistical considerations. We show that, unlike other univariate and multivariate volatility estimators, the range-based estimator is highly efficient yet robust to market microstructure noise arising from bid-ask bounce and asynchronous trading. Finally, we provide an empirical example illustrating the value of the high-frequency sample path information contained in the range-based estimates in a multivariate GARCH framework. |
| Keywords: | Range-based estimation, volatility, covariance, correlation, absence of arbitrage, exchange rates, stock returns, bond returns, bid-ask bounce, asynchronous trading |
| Date: | 2004–01–07 |
| URL: | http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200407&r=ets |
| By: | Peter F. Christoffersen (McGill University and CIRANO); Francis X. Diebold (University of Pennsylvania and NBER) |
| Abstract: | We consider three sets of phenomena that feature prominently – and separately – in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis. |
| Date: | 2004–01–08 |
| URL: | http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200408&r=ets |
| By: | Torben G. Andersen (Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208, and NBER); Tim Bollerslev (Department of Economics, Duke University, Durham, NC 27708, and NBER); Peter F. Christoffersen (Faculty of Management, McGill University, Montreal, Quebec, H3A 1G5, and CIRANO); Francis X. Diebold (Department of Economics, University of Pennsylvania, Philadelphia, PA 19104, and NBER) |
| Abstract: | What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions – in particular, real-time risk tracking in very high-dimensional situations – impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds. |
| JEL: | G10 |
| Date: | 2005–01–02 |
| URL: | http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200502&r=ets |
| By: | Roy Cerqueti and Mauro Costantini |
| Abstract: | This paper provides a further generalization of co-integration tests in a nonparametric setting. We adopt Bierens' approach in order to give an extension for processes I(d), with a fixed integer d. A generalized eigenvalue problem is solved, and the test statistics involved are obtained starting from two matrices that are independent on the data generating process. The mathematical tools we adopt are related to the asymptotic theory of the stochastic processes. The key point of our work is linked to the distinguishing between the stationary and non-stationary part of an integrated process. |
| Keywords: | Multivariate analysis, Nonparametric methods, Co-integration, Asymptotic properties. |
| JEL: | C14 C32 |
| Date: | 2005–07–12 |
| URL: | http://d.repec.org/n?u=RePEc:mol:ecsdps:esdp05026&r=ets |
| By: | Roberto Basile, Mauro Costantini, Sergio Destefanis (ISAE, Roma – Università di Macerata, ISAE, Roma – Università “La Sapienza” di Roma, CELPE, CSEF – Università di Salerno) |
| Abstract: | This paper employs recently developed non stationary panel methodologies that assume some cross-section dependence to estimate the production function for Italian regions in the industrial sector over the period 1970-1998. The analysis consists in three steps. First, unit root tests for cross-sectionally dependent panels are used. Second, the existence of a co-integrating relationship among value added, physical capital and human capital-augmented labor is investigated. The Dynamic OLS (DOLS) and Fully modified (FMOLS) estimators developed by Pedroni (1996, 2000, 2001) and the Panel Dynamic OLS (PDOLS) estimator proposed by Mark and Sul (2003) are then used to estimate the long run relationship between the variables considered. |
| Keywords: | panel cointegration, cross-section dependence, production function. |
| JEL: | C33 C15 D24 |
| Date: | 2005–05 |
| URL: | http://d.repec.org/n?u=RePEc:sal:celpdp:94&r=ets |
| By: | Naoto Kunitomo (Faculty of Economics, University of Tokyo); Makoto Takaoka (Research Center for Advanced Science and Technology, University of Tokyo) |
| Abstract: | In the recent X-12-ARIMA program developed by the United States Census Bureau for seasonal adjustments, the RegARIMA modeling has been extensively utilized. We shall discuss some problems in the RegARIMA modeling when the time series are realizations of non-stationary integrated stochastic processes with fixed regressors. We propose to use the seasonal switching autoregressive moving average (SSARMA) model and the regression SSARMA (RegSSARMA) model to cope with seasonality commonly observed in many economic time series. We investigate the basic properties of the SSAR (seasonal switching autoregressive) models. We argue that the phenomenon called "spurious seasonal unit roots" could be an explanation for a good fit of the seasonal ARIMA models to actual data. Some results of economic data analyses are reported. |
| Date: | 2005–07 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2005cj135&r=ets |