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Daníelsson, Jón; Vries, Casper G. de - 1997

Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the...

Persistent link: https://www.econbiz.de/10010533206

Spot variance path estimation and its application to high frequency jump testing

Bos, Charles S.; Janus, Paweł; Koopman, Siem Jan - 2009

This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used...

Persistent link: https://www.econbiz.de/10011379469

A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations

Creal, Drew; Koopman, Siem Jan; Lucas, André - 2010

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...

Persistent link: https://www.econbiz.de/10011380135

Abnormal returns, risk, and options in large data sets

Caserta, Silvia; Daníelsson, Jón; Vries, Casper G. de - 1998

Large data sets in finance with millions of observations have becomewidely available. Such data sets enable the construction of reliablesemi-parametric estimates of the risk associated with extreme pricemovements. Our approach is based on semi-parametric statisticalextreme value analysis, and...

Persistent link: https://www.econbiz.de/10011299966

A hybrid joint moment ratio test for financial time series

Groenendijk, Patrick A.; Lucas, André; Vries, Casper G. de - 1998

. The methodology is hybrid because it combines a formaltesting procedure with volatility curve pattern recognition based …

Persistent link: https://www.econbiz.de/10011299968

Asymmetries in conditional mean and variance : modelling stock returns by asMA-asQGARCH

Brännäs, Kurt; Gooijer, Jan G. de - 2000

The asymmetric moving average model (asMA) is extended to allow forasymmetric quadratic conditional heteroskedasticity (asQGARCH). Theasymmetric parametrization of the conditional variance encompassesthe quadratic GARCH model of Sentana (1995). We introduce a framework fortesting asymmetries in...

Persistent link: https://www.econbiz.de/10011303289

Modeling and forecasting S&P 500 volatility : long memory, structural breaks and nonlinearity

Martens, Martin; Dijk, Dick van; Pooter, Michiel de - 2004

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …

Persistent link: https://www.econbiz.de/10011335205

Predicting the daily covariance matrix for S&P 100 stocks using intraday data : but which frequency to use?

Pooter, Michiel de; Martens, Martin; Dijk, Dick van - 2006

This paper investigates the merits of high-frequency intraday data when forming minimum variance portfolios and minimum tracking error portfolios with daily rebalancing from the individual constituents of the S&P 100 index. We focus on the issue of determining the optimal sampling frequency,...

Persistent link: https://www.econbiz.de/10011346450

Stock index volatility forecasting with high frequency data

Hol Uspensky, Eugenie; Koopman, Siem Jan - 2002

improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source … for longer horizon volatility forecasts. In this paper we explore the forecasting value of these high fre-quency series in … conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …

Persistent link: https://www.econbiz.de/10011326944

Forecasting value-at-risk using block structure multivariate stochastic volatility models

Asai, Manabu; Caporin, Massimiliano; McAleer, Michael - 2013

Persistent link: https://www.econbiz.de/10009767006

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