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on Econometric Time Series |
| By: | Manuele Bigeco; Enrico Grosso; Edoardo Otranto |
| Abstract: | The problem of forecasting financial time series has received great attention in the past, from both Econometrics and Pattern Recognition researchers. In this context, most of the efforts were spent to represent and model the volatility of the financial indicators in long time series. In this paper a different problem is faced, the prediction of increases and decreases in short (local) financial trends. This problem, poorly considered by the researchers, needs specific models, able to capture the movement in the short time and the asymmetries between increase and decrease periods. The methodology presented in this paper explicitly considers both aspects, encoding the financial returns in binary values (representing the signs of the returns), which are subsequently modelled using two separate Hidden Markov models, one for increases and one for decreases, respectively. The approach has been tested with different experiments with the Dow Jones index and other shares of the same market of different risk, with encouraging results. |
| Keywords: | Markov Models; Asymmetries; Binary data; Short-time forecasts |
| JEL: | C02 C63 G11 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:200803&r=ets |
| By: | Schlicht, Ekkehart |
| Abstract: | Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. Practical problems arise, however, if the time series contains structural breaks (as produced by German unification for German time series, for instance), or if some data are missing. This note proposes a method for coping with these problems. |
| Keywords: | dummies; gaps; Hodrick-Prescott filter; interpolation; Leser filter; missing observations; smoothing; spline; structural breaks; time-series; trend; break point; break point location |
| JEL: | C22 C32 C63 C14 |
| Date: | 2008–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:lmu:muenec:2127&r=ets |
| By: | Andrew J. Patton |
| Abstract: | This paper presents an overview of the literature on applications of copulas in the modelling of financial time series. Copulas have been used both in multivariate time series analysis, where they are used to charaterise the (conditional) cross-sectional dependence between individual time series, and in univariate time series analysis, where they are used to characterise the dependence between a sequence of observations of a scalar time series process. The paper includes a broad, brief, review of the many applications of copulas in finance and economics. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:sbs:wpsefe:2008fe21&r=ets |
| By: | Andrew J. Patton; Kevin Sheppard |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:sbs:wpsefe:2008fe22&r=ets |
| By: | Mardi Dungey (Univeristy of Cambridge); George Milunovich (Macquarie University); Susan Thorp (University of Technology, Sydney) |
| Abstract: | Markets in fnancial crisis may experience heightened sensitivity to news from abroad and they may also spread turbulence into foreign markets, creating contagion. We use a structural GARCH model to separate and measure these two parts of crisis transmission. Unobservable structural shocks are named and linked to source markets using variance decompositions, allowing clearer interpretation of impulse response functions. Applying this method to data from the Asian crisis, we find signifcant contagion from Hong Kong to nearby markets but little heightened sensitivity. Impulse response functions for an equally-weighted equity portfolio show the increasing dominance of Korean and Hong Kong shocks during the crisis, whereas Indonesia\'s infuence shrinks. |
| Keywords: | Contagion, Structural GARCH |
| JEL: | F37 C51 |
| Date: | 2008–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:qut:auncer:2008-97&r=ets |