CFS working paper series
https://gfk-cfs.de/working-papers/
Filtern
Dokumenttyp
- Arbeitspapier (7)
Sprache
- Englisch (7)
Volltext vorhanden
- ja (7)
Gehört zur Bibliographie
- nein (7)
Schlagworte
Institut
2007, 16
This paper proposes a possible way of assessing the effect of interest rate dynamics on changes in the decision-making approach, communication strategy and operational framework of a Central bank. Through a GARCH specification we show that the USA and Euro area displayed a limited but significant spillover of volatility from money market to longer-term rates. We then checked the stability of this phenomenon in the most recent period of improved policymaking and found empirical evidence that the transmission of overnight volatility along the yield curve vanished soon after specific policy changes of the FED and ECB.
2006, 23
A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L. Klassifizierung: C22, C53, C63, G12
2005, 11
While much of classical statistical analysis is based on Gaussian distributional assumptions, statistical modeling with the Laplace distribution has gained importance in many applied fields. This phenomenon is rooted in the fact that, like the Gaussian, the Laplace distribution has many attractive properties. This paper investigates two methods of combining them and their use in modeling and predicting financial risk. Based on 25 daily stock return series, the empirical results indicate that the new models offer a plausible description of the data. They are also shown to be competitive with, or superior to, use of the hyperbolic distribution, which has gained some popularity in asset-return modeling and, in fact, also nests the Gaussian and Laplace. Klassifikation: C16, C50 . March 2005.
2005, 09
Financial markets embed expectations of central bank policy into asset prices. This paper compares two approaches that extract a probability density of market beliefs. The first is a simulatedmoments estimator for option volatilities described in Mizrach (2002); the second is a new approach developed by Haas, Mittnik and Paolella (2004a) for fat-tailed conditionally heteroskedastic time series. In an application to the 1992-93 European Exchange Rate Mechanism crises, that both the options and the underlying exchange rates provide useful information for policy makers. JEL Klassifikation: G12, G14, F31.
2005, 33
Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing "observable" or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time-varying volatility of realized volatility leads to a substantial improvement of the model's fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting. Klassifikation: C22, C51, C52, C53
2002, 04
Forecasting stock market volatility and the informational efficiency of the DAX-index options market
(2002)
Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index return data it is found that past returns do not contain useful information beyond the volatility expectations already reflected in option prices. This supports the efficient market hypothesis for the DAX-index options market.
2002, 10
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect. Klassifikation: C22, C51, G10