Similar Search Results

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

Out-of-sample comparison of copula specifications in multivariate density forecasts

Diks, Cees G. H.; Panchenko, Valentyn; Dijk, Dick van - 2008

We introduce a statistical test for comparing the predictive accuracy of competing copula specifications in multivariate density forecasts, based on the Kullback-Leibler Information Criterion (KLIC). The test is valid under general conditions: in particular it allows for parameter estimation...

Persistent link: https://www.econbiz.de/10011377261

Forecasting volatility with Copula-based time series models

Sokolinskiy, Oleg; Dijk, Dick van - 2011

Persistent link: https://www.econbiz.de/10009720755

Getting the most out of macroeconomic information for predicting stock returns and volatility

Çakmaklı, Cem; Dijk, Dick van - 2010

predicting monthly US excess stock returns and volatility over the period 1980-2005. Factor-augmented predictive regression … superior market timing ability and volatility timing ability, while a mean-variance investor would be willing to pay an annual …

Persistent link: https://www.econbiz.de/10011382428

Getting the Most Out of Macroeconomic Information for Predicting Stock Returns and Volatility

Çakmaklı, Cem - 2010

Persistent link: https://www.econbiz.de/10013135780

Nonlinear time series models in empirical finance

Franses, Philip Hans; Dijk, Dick van - 2000 - 1. publ.

Persistent link: https://www.econbiz.de/10001484071

Slow expectation-maximization convergence in low-noise dynamic factor models

Opschoor, Daan; Dijk, Dick van - 2023

This paper addresses the poor performance of the Expectation-Maximization (EM) algorithm in the estimation of low-noise dynamic factor models, commonly used in macroeconomic forecasting and nowcasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a...

Persistent link: https://www.econbiz.de/10014249849

Forecasting S&P 500 volatility : long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements

Martens, Martin; Dijk, Dick van; Pooter, Michiel de - In: International journal of forecasting 25 (2009) 2, pp. 282-303

Persistent link: https://www.econbiz.de/10003870055

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

Forecasting value-at-risk under temporal and portfolio aggregation

Kole, Erik; Markwat, Thijs; Opschoor, Anne; Dijk, Dick van - 2015

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly...

Persistent link: https://www.econbiz.de/10011431503

1
2
3
4
5
6
7
8
9
10
Next
Last