Can Internet search queries help to predict stock market volatility?

This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high searches follow high volatility, and high volatility follows high searches. Using the latter feedback effect to predict volatility we find that search queries contain additional information about market volatility. They help to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful to predict volatility in high-volatility phases. --realized volatility,forecasting,investor behavior,noise trader,search engine data

Can internet search queries help to predict stock market volatility?

This paper studies the dynamics of stock market volatility and retail investor attention measured by internet search queries. We find a strong co-movement of stock market indices' realized volatility and the search queries for their names. Furthermore, Granger causality is bi-directional: high searches follow high volatility, and high volatility follows high searches. Using the latter feedback effect to predict volatility we find that search queries contain additional information about market volatility. They help to improve volatility forecasts in-sample and out-of-sample as well as for different forecasting horizons. Search queries are particularly useful to predict volatility in high-volatility phases. --realized volatility,forecasting,investor behavior,noise trader,search engine data

Labor income risk and the reluctance of fouseholds to invest in risky financial assets: A panel data analysis

We investigate the determinants of a household's decision on whether to invest in risky financial assets. Financial theory suggests that with increasing labor income risk, the reluctance of households to hold stocks increases. We propose to measure income risk as the observed variation of household income over a five year period. We find that indeed higher income risk reduces the propensity to invest in stocks. However, when controlling for household heterogeneity as well as subjective measures of a household's financial situation (income satisfaction, worries about financial situation), the impact of observed labor income variation vanishes

Stock return autocorrelations revisited: A quantile regression approach

The aim of this study is to provide a comprehensive description of the dependence pattern of stock returns by studying a range of quantiles of the conditional return distribution using quantile autoregression. This enables us in particular to study the behavior of extreme quantiles associated with large positive and negative returns in contrast to the central quantile which is closely related to the conditional mean in the least-squares regression framework. Our empirical results are based on 30 years of daily, weekly and monthly returns of the stocks comprised in the Dow Jones Stoxx 600 index. We find that lower quantiles exhibit positive dependence on past returns while upper quantiles are marked by negative dependence. This pattern holds when accounting for stock specific characteristics such as market capitalization, industry, or exposure to market risk. --stock return distribution,quantile autoregression,overreaction and underreaction

Financial market spillovers around the globe

Financial market spillovers around the globeThis paper investigates the transmission of return and volatility spillovers around the globe. It draws on index futures of three representative indices, namely the Dow Jones Euro Stoxx 50, the S&P 500 and the Nikkei 225. Devolatised returns and realised volatilities are modeled separately using a structural vector autoregressive model, thereby accounting for the particular sequential time structure of the trading venues. Within this framework, we test hypotheses in the spirit of Granger causality tests, investigate the short-run dynamics in the three markets using impulse response functions, and identify leadership effects through variance decomposition. Our key results are as follows. We find weak and shortlived return spillovers, in particular from the USA to Japan. Volatility spillovers are more pronounced and persistent. The information from the home market is most important for both returns and volatilities; the contribution from foreign markets is less pronounced in the case of returns than in the case of volatility. Possible gains in terms of forecasting precision when applying our modelling strategy are illustrated by a forecast evaluation

Price discovery in the markets for credit risk: A Markov switching approach

We examine price discovery in the Credit Default Swap and corporate bond market. By using a Markov switching framework we are able to analyze the dynamic behavior of the information shares during tranquil and crisis periods. The results show that price discovery takes place mostly on the CDS market. The importance of the CDS market even increases during the more volatile crisis periods. According to a cross sectional analysis liquidity is the main determinant of a market's contribution to price discovery. During the crisis period, however, we also find a positive link between leverage and CDS market information shares. Overall the results indicate that price discovery measures and their determinants change during tranquil and crisis periods, which emphasizes the importance of more exible frameworks, such as Markov switching models

Price discovery in the markets for credit risk

We examine price discovery in the Credit Default Swap and cor- porate bond market. By using a Markov switching framework we are able to analyze the dynamic behavior of the information shares dur- ing tranquil and crisis periods. The results show that price discovery takes place mostly on the CDS market. The importance of the CDS market even increases during the more volatile crisis periods. Accord- ing to a cross sectional analysis liquidity is the main determinant of a market's contribution to price discovery. During the crisis period, however, we also find a positive link between leverage and CDS market information shares. Overall the results indicate that price discovery measures and their determinants change during tranquil and crisis pe- riods, which emphasizes the importance of more exible frameworks, such as Markov switching models

Density Forecasts with Quantile Autoregression with an Application to Option Pricing

[EN] This paper presents a method for estimating the conditional and joint probability densities of multiple random variables using quantile regression, established by Koenker and Bassett (1978), for which the statistical inference has been extended to the field of time series analysis by Koenker and Xiao (2006). We provide a simple and robust framework for estimating auto-regressive, conditional densities, allowing for inference not only on the conditional density itself but also on functions of the modeled random variables, such as option prices. In our application, we demonstrate theoretically, via a simulation study and in out-of-the-sample density forecasts the effectiveness of our approach in estimating option prices with confidence bounds implied by the estimation method. Our findings suggest that quantile autoregression is effective in forecasting conditional densities and can be used for option pricing. The flexibility of our method in incorporating conditioning information, such as past returns or volatility, has the potential to further improve forecasting accuracy.Bleher, J.; Dimpfl, T.; Koch, S. (2023). Density Forecasts with Quantile Autoregression with an Application to Option Pricing. Editorial Universitat Politècnica de València. 279-280. http://hdl.handle.net/10251/20170727928

Econometric Analysis of International Financial Markets

Die zentrale Fragestellung meines Dissertationsprojektes "Ökonometrische Untersuchung internationaler Finanzmärkte" ist der Zusammenhang globaler Finanzmärkte in Bezug auf Informations- und Volatilitätsübertragung. Mit Hilfe verschiedener ökonometrischer Methoden werden gezielt Dynamiken offengelegt und einige der in der Literatur als Standard angesehenen Phänomene hinterfragt. Der erste Teil behandelt die sogenannten Informations- und Volatilitätsspillovers. Von zentraler Bedeutung ist hier die Tatsache, dass aus globaler Sicht der Handel an Börsen als kontinuierlich angesehen werden kann. Aus diesem Grund sollte es möglich sein, Informations- und Volatilitätsspillovers um den Erdball in Übereinstimmung mit der Abfolge aus Öffnen und Schließen der Märkte in Asien, Europa und den USA nachzuvollziehen. Der zweite Teil der Arbeit setzt sich mit Kointegration von Aktienmärkten und den speziellen Herausforderungen von Finanzmarktdatensätzen auseinander. Kointegration ist eine ökonometrische Methode, welche herangezogen wird, um den Integrationsgrad internationaler Finanzmärkte zu messen. Die Ergebnisse sind jedoch sehr heterogen. Wir zeigen, dass internationale Finanzmärkte nicht kointegriert sein können, sofern das „random walk“-Modell für Aktienpreise zutrifft. Mit Hilfe einer Simulationsstudie werden Gründe herausgearbeitet, warum Kointegrationstests andere Schlussfolgerungen nahelegen können. Schließlich widmet sich der letzte Teil der Dissertation der Informationsübertragung von den USA nach Europa zur Zeit der Eröffnung der US-amerikanischen Märkte. Es wird gezeigt, dass Nachrichten aus den USA (welche durch Quantile der Renditeverteilung des S&P 500 identifiziert werden) einen signifikanten Einfluss auf die Renditen und die Volatilität des DAX ausüben und sowohl schnell als auch effizient von deutschen Händlern verarbeitet werden.The central problem of the dissertation project "Econometric Analysis of International Financial Markets" is the question how financial markets around the globe are linked in terms of information and volatility transmission. Using different econometric techniques some of the dynamics are unraveled and explanations for phenomena taken for granted in the literature so far are proposed. More precisely, the first aspect covered concerns information and volatility spillovers around the globe, the central aspect being that from a global point of view stock trading is continuous. We therefore state that information and volatility spillovers are traceable around the globe in accordance with the sequence of opening and closing of financial markets in Asia, Europe and the USA. The second subject deals with cointegration of financial markets and the peculiarity of financial data. Cointegration is an econometric technique which is quite frequently used to asses the degree of integration of financial markets. The results are, however, far from being clear-cut. We show that international financial markets are not cointegrated given the commonly used random walk model for stock prices is true. By means of simulation studies we elaborate reasons why the results of cointegration tests can be misleading. Finally we take a closer look at the information transmission from the USA to Europe at the time when the US markets open. We show that news originating in the USA (which are identified using quantiles of the S&P 500 index return distribution) have a significant impact on the returns and the volatility of the German DAX and are processed rapidly and efficiently by German traders