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Long memory persistence in the factor of Implied volatility dynamics

Härdle, Wolfgang Karl; Mungo, Julius - 2007

Volatility Surface (IVS). Practical applications require reducing the dimension and characterize its dynamics through a small … long time, affecting significantly stock prices. For appropriate representation of the series dynamics and the possibility …

Persistent link: https://www.econbiz.de/10010274129

Value-at-risk and expected shortfall when there is long range dependence

Härdle, Wolfgang Karl; Mungo, Julius - 2008

Empirical studies have shown that a large number of financial asset returns exhibit fat tails and are often characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in market volatility, with significant impact on pricing and...

Persistent link: https://www.econbiz.de/10010274140

A generalized ARFIMA process with Markov-switching fractional differencing parameter

Tsay, Wen-Jen; Härdle, Wolfgang Karl - 2007

We propose a general class of Markov-switching-ARFIMA processes in order to combine strands of long memory and Markov-switching literature. Although the coverage of this class of models is broad, we show that these models can be easily estimated with the DLV algorithm proposed. This algorithm...

Persistent link: https://www.econbiz.de/10010274125

Measuring and modeling risk using high-frequency data

Härdle, Wolfgang Karl; Hautsch, Nikolaus; Pigorsch, Uta - 2008

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management …

Persistent link: https://www.econbiz.de/10010274148

Combination of multivariate volatility forecasts

Amendola, Alessandra; Storti, Giuseppe - 2009

This paper proposes a novel approach to the combination of conditional covariance matrix forecasts based on the use of the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional systems by means of a two-step strategy. The finite...

Persistent link: https://www.econbiz.de/10010263760

Prognose mit nichtparametrischen Verfahren

Härdle, Wolfgang Karl; Schulz, Rainer; Wang, Weining - 2010

Statistische Prognosen basieren auf der Annahme, dass ein funktionaler Zusammenhang zwischen der zu prognostizierenden Variable y und anderen j-dimensional beobachtbaren Variablen x = (x1,...xl) besteht. Kann der funktionale Zusammenhang geschätzt werden, so kann im Prinzip für jedes x der...

Persistent link: https://www.econbiz.de/10010281577

Varying coefficient GARCH versus local constant volatility modeling. Comparison of the predictive power

Polzehl, Jörg; Spokoiny, Vladimir - Sonderforschungsbereich Ökonomisches Risiko <Berlin>

GARCH models are widely used in financial econometrics. However, we show by mean of a simple simulation example that the GARCH approach may lead to a serious model misspecification if the assumption of stationarity is violated. In particular, the well known integrated GARCH effect can be...

Persistent link: https://www.econbiz.de/10005854708

Capturing common components in high-frequency financial time series: A multivariate stochastic multiplicative error model

Hautsch, Nikolaus - 2007

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics …) common factor dynamics as well as idiosyncratic effects in the processes of high-frequency return volatilities, trade sizes … and intensities are driven by idiosyncratic dynamics as well as a highly persistent common factor capturing most causal …

Persistent link: https://www.econbiz.de/10010263700

Modelling high-frequency volatility and liquidity using multiplicative error models

Hautsch, Nikolaus; Jeleskovic, Vahidin - 2008

In this paper, we study the dynamic interdependencies between high-frequency volatility, liquidity demand as well as trading costs in an electronic limit order book market. Using data from the Australian Stock Exchange we model 1-min squared mid-quote returns, average trade sizes, number of...

Persistent link: https://www.econbiz.de/10010263738

Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory

Schaumburg, Julia - 2010

This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%)...

Persistent link: https://www.econbiz.de/10010270817