Moritz Heiden

In this article, we explore the traditional concept of Cash & Carry in the emissions markets. Despite the negative interest rate environment, the EUA futures curve has been consistently in contango over the last years, thus presenting an attractive opportunity to harvest positive yields on euro cash through a Cash & Carry trade in which the trader is long the EUA physical certificates while eliminating the market risk through a short EUA futures position

Based on a dataset of positioning data from one of the largest forex trading platforms in the world, we study the trading behaviour of retail investors on a daily frequency for 14 currency pairs. We examine, whether retail investors could benefit from positioning data. Using a quantile regression framework we find that a representative investor shows a combination of short term contrarian behaviour and long term trend following. Employing positioning data in a naive trading strategy, we observe… Mehr anzeigen

Based on a dataset of positioning data from one of the largest forex trading platforms in the world, we study the trading behaviour of retail investors on a daily frequency for 14 currency pairs. We examine, whether retail investors could benefit from positioning data. Using a quantile regression framework we find that a representative investor shows a combination of short term contrarian behaviour and long term trend following. Employing positioning data in a naive trading strategy, we observe strong signs of the disposition effect. Overall, investors approach to market timing paired with an asymmetry in realizing gains and losses leads to systematic underperformance based on a strategy mimicking the trading behaviour of investors average positioning. Weniger anzeigen

Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible Empirical Similarity (ES) approach is used to emulate the dynamics of the volatility process by a time-varying parameter that is created via the similarity of realized volatility and investor sentiment. Out-of-sample results show that the ES model produces significantly better volatility forecasts than various benchmark… Mehr anzeigen

Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible Empirical Similarity (ES) approach is used to emulate the dynamics of the volatility process by a time-varying parameter that is created via the similarity of realized volatility and investor sentiment. Out-of-sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed. Weniger anzeigen

This cumulative dissertation studies various approaches to improve stock market volatility forecasts based on nonlinearity and asymmetric dependence modeling as well as new innovative data sources. Studying multivariate dependence patterns using a vine copula approach and incorporating Google search data as measure of investor attention in a framework of empirical similarity significantly improves volatility forecasts based on different statistical and economic measures. The importance of… Mehr anzeigen

This cumulative dissertation studies various approaches to improve stock market volatility forecasts based on nonlinearity and asymmetric dependence modeling as well as new innovative data sources. Studying multivariate dependence patterns using a vine copula approach and incorporating Google search data as measure of investor attention in a framework of empirical similarity significantly improves volatility forecasts based on different statistical and economic measures. The importance of accurate volatility forecasts in portfolio- and risk management is highlighted in several economic applications and empirical studies. Weniger anzeigen

This paper proposes a dynamic framework for modeling and forecasting of realized covariance matrices using vine copulas to allow for more flexible dependencies between assets. Our model automatically guarantees positive definiteness of the forecast through the use of a Cholesky decomposition of the realized covariance matrix. We explicitly account for long-memory behavior by using ARFIMA and HAR models for the individual elements of the decomposition. Furthermore, our model incorporates… Mehr anzeigen

This paper proposes a dynamic framework for modeling and forecasting of realized covariance matrices using vine copulas to allow for more flexible dependencies between assets. Our model automatically guarantees positive definiteness of the forecast through the use of a Cholesky decomposition of the realized covariance matrix. We explicitly account for long-memory behavior by using ARFIMA and HAR models for the individual elements of the decomposition. Furthermore, our model incorporates non-Gaussian innovations and GARCH effects, accounting for volatility clustering and unconditional kurtosis. The dependence structure between assets is studied using vine copula constructions, which allow for nonlinearity and asymmetry without suffering from an inflexible tail behavior or symmetry restrictions as in conventional multivariate models. Further, the copulas have a direct impact on the point forecasts of the realized covariances matrices, due to being computed as a nonlinear transformation of the forecasts for the Cholesky matrix. Beside studying in-sample properties, we assess the usefulness of our method in a one-day ahead forecasting framework, comparing recent types of models for the realized covariance matrix based on a model confidence set approach. Additionally, we find that in Value-at-Risk (VaR) forecasting, vine models require less capital requirements due to smoother and more accurate forecasts. Weniger anzeigen

This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms conventional time-series models in an out-of-sample forecasting framework. We find that especially in high-volatility market phases prediction accuracy increases together with investor attention. The… Mehr anzeigen

This paper proposes an empirical similarity approach to forecast weekly volatility by using search engine data as a measure of investors attention to the stock market index. Our model is assumption free with respect to the underlying process of investors attention and significantly outperforms conventional time-series models in an out-of-sample forecasting framework. We find that especially in high-volatility market phases prediction accuracy increases together with investor attention. The practical implications for risk management are highlighted in a Value-at-Risk forecasting exercise, where our model produces significantly more accurate forecasts while requiring less capital due to fewer overpredictions. Weniger anzeigen

This paper studies the pitfalls of applying the Cholesky decomposition for forecasting multivariate volatility. We analyze the impact of one of the main issues in empirical application of using the decomposition: The sensitivity of the forecasts to the order of the variables in the covariance matrix. We find that despite being frequently used to guarantee positive semi-definiteness and symmetry of the forecasts, the Cholesky decomposition has to be used with caution, as the ordering of the… Mehr anzeigen

This paper studies the pitfalls of applying the Cholesky decomposition for forecasting multivariate volatility. We analyze the impact of one of the main issues in empirical application of using the decomposition: The sensitivity of the forecasts to the order of the variables in the covariance matrix. We find that despite being frequently used to guarantee positive semi-definiteness and symmetry of the forecasts, the Cholesky decomposition has to be used with caution, as the ordering of the variables leads to significant differences in forecast performance. A possible solution is provided by studying an alternative, the matrix exponential transformation. We show that in combination with empirical bias correction, forecasting accuracy of both decompositions does not significantly differ. This makes the matrix exponential a valuable option, especially in larger dimensions. Weniger anzeigen

Bei der Datenanalyse werden oftmals Verteilungen an Stichprobendaten angepasst, um Schlussfolgerungen für eine Grundgesamtheit ziehen zu können. Hierbei fällt auf, dass beobachtete Verteilungen aufgrund von heterogenem Datenmaterial sehr häufig komplexe Formen annehmen, welche durch gängige Verteilungen nur unzureichend erfasst werden können. Mischverteilungen bieten die Möglichkeit, komplexe Verteilungen durch die Mischung einfacher Verteilungen zu approximieren.

While theoretical literature highlights that investor sentiment impacts the mean-variance tradeoff,
sentiment measures have barely been used in a portfolio optimization context. Up to now, sentiment
measures have been used in forecasting assessments and have shown some predictive power in different asset markets. Additionally, sentiment has shown to provide incremental information when forecasting future volatility, even on an out-of-sample basis. In this publication, we integrate… Mehr anzeigen

While theoretical literature highlights that investor sentiment impacts the mean-variance tradeoff,
sentiment measures have barely been used in a portfolio optimization context. Up to now, sentiment
measures have been used in forecasting assessments and have shown some predictive power in different asset markets. Additionally, sentiment has shown to provide incremental information when forecasting future volatility, even on an out-of-sample basis. In this publication, we integrate investor sentiment - measured by the weekly investor survey of sentix.de - into the portfolio optimization context. Using a opinion pooling framework, we investigate whether the information contained in investor sentiment can be used to improve upon traditional portfolio selection. Additionally, we examine if the sentiment by sentix.de - which stems predominantly from European investors - provides a local advantage in markets known "inside out" by those investors. Weniger anzeigen