High-dimensional regression problems which reveal dynamic behavior are typicallyanalyzed by time propagation of a few number of factors. The inference on thewhole system is then based on the low-dimensional time series analysis. Such highdimensional problems occur frequently in many different...

Investors recently are really concerned about the risk aspects associated with the investment in securities. Volatility calculation, therefore, has become an important aspect in the financial markets. For these reasons time series models are greatly used to forecast volatility. One such model is...

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005)...

This paper explores a common machine learning tool, the kernel ridge regression, as applied to financial volatility forecasting. It is shown that kernel ridge provides reliable forecast improvements to both a linear specification, and a fitted nonlinear specification which represents well known...

This paper introduces a new modelling for detecting the presence of commonalities in a set of realized volatility measures. In particular, we propose a multivariate generalization of the heterogeneous autoregressive model (HAR) that is endowed with a common index structure. The Vector...

This study examined the impact of oil price on African stock markets. Using quarterly data from five selected oil producing countries with stock market presence, from Q1:2010 to Q4:2018, the study deployed dynamic panel analysis technique for a model comprising stock returns, real gross domestic...

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...

We investigate long-run stock-bond correlation using a model that combines the dynamic conditional correlation model with the mixed-data sampling approach and allows long-run correlation to be affected by macro-finance factors (historical and forecasts). We use macro-finance factors related to...

Michael Schröder (Hrsg.) Basistechniken, Fortgeschrittene Verfahren, Prognosemodelle 2., überarbeitete Auflage 2012 Schäffer-Poeschel Verlag Stuttgart IX Inhaltsübersicht Vorwort zur zweiten Auflage ...