Financial Market Volatility and Primary Placements

This paper studies empirically the link between financial markets volatility and primary placements of stocks and bonds for the US economy. We find that the impact of volatility on primary placements is not statistically significant.Financial risk, Primary placements

Understanding Financial Market Volatility

__Abstract__ Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. Loosely speaking, volatility is defined as the average magnitude of fluctuations observed in some phenomenon over time. Within the area of economics, this definition narrows to the variability of an unpredictable random component of a time series variable. Typical examples in finance are returns on assets, such as individual stocks or a stock index like the S&P 500 index. As indicated by the quote from Campbell et al. (1997), (financial market) volatility is central to financial economics. Since it is the most common measure of the risk involved in investments in traded securities, it plays a crucial role in portfolio management, risk management, and pricing of derivative securities including options and futures contracts. Volatility is therefore closely tracked by private investors, institutional investors like pension funds, central bankers and policy makers. For example, the so-called Basel accords contain regulations where banks are required to hold a certain amount of capital to cover the risks involved in their consumer loans, mortgages and other assets. An estimate of the volatility of these assets is a crucial input for determining these capital requirements. In addition, the financial crisis in 2007-2008 has proven that the impact of financial market volatility is not only limited to the financial industry. It shows that volatility may be costly for the economy as a whole. For example, extreme stock market volatility may negatively influence aggregate investments behavior, in particular as companies often require equity as a source of external financing. This thesis contributes to the volatility literature by investigating several relevant aspects of volatility. First, we focus on the parameter estimation of multivariate volatility models, which is problematic if the number of considered assets increases. Second, we consider the question what exactly causes financial market volatility? In this context, we relate volatility with various types of information. In addition, we pay attention to modeling volatility, by adapting volatility models such that they allow for including possible exogenous variables. Finally, we turn to forecasting techniques of volatility, with the focus on the combination of density forecasts

Financial market volatility: informative in predicting recessions

It is commonly agreed that the term spread and stock returns are useful in predicting recessions. We extend these empirical findings by examining interest rate and stock market volatility as additional recession indicators. Both risk-return analysis and the theory of investment under uncertainty provide a rationale for this extension. The results for the United States, Germany and Japan show that interest rate and stock return volatility contribute significantly to the forecasting of future recessions. This holds in particular for short term predictions.business cycles; stock market volatility; interest rate volatility; probit model

The recent behaviour of financial market volatility

A striking feature of financial market behaviour in recent years has been the low level of price volatility over a wide range of financial assets and markets. The issue has attracted the attention of central bankers and financial regulators due to the potential implications for financial stability. This paper makes an effort to shed light on this phenomenon, drawing on literature surveys, reviews of previous analyses by non-academic commentators and institutions, and some new empirical evidence. The paper consists of seven sections. Section 2 documents the current low level of volatility, putting it into a historical perspective. Section 3 briefly reviews the theoretical determinants of volatility, with the aim of helping the reader through the subsequent sections of this Report, which are devoted to the explanations of the phenomenon under study. These explanations have been grouped into four categories: real factors; financial factors; shocks; and monetary policy. Thus, Section 4 looks into the relation between volatility and real factors, from both a macro- and a microeconomic perspective. Section 5 considers how the recent developments in financial innovation and improvements in risk management techniques might have contributed to the decline in volatility. Section 6 considers the relation between real and financial shocks and volatility. Finally, Section 7 explores whether more systematic and transparent monetary policies might have led to lower asset price volatility.financial volatility, risk taking, international financial markets

Detecting Multiple Breaks in Financial Market Volatility Dynamics

The paper evaluates the performance of several recently proposed tests for structural breaks in conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new test for detecting breaks in the second conditional variance under various realistic univariate heteroskedastic models, change-point hypotheses and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. These events resulted in changes in the dynamics of volatility of asset returns in the samples prior and post the breaks.change-point, break dates, ARCH, high-frequency data.

Has financial market volatility increased?

Money market ; Stock market ; Interest rates ; Foreign exchange rates

High quality topic extraction from business news explains abnormal financial market volatility

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affect trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affect stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized fact in financial economies, namely that at certain times trading volumes appear to be "abnormally large," can be partially explained by the flow of news. In this sense, our results prove that there is no "excess trading," when restricting to times when news are genuinely novel and provide relevant financial information.Comment: The previous version of this article included an error. This is a revised versio

Cracking the Conundrum

From 2004 to 2006, the FOMC raised the target federal funds rate by 4.25%, yet long-maturity yields and forward rates fell. We consider several possible explanations for this "conundrum." The most likely, in our view, is a fall in the term premium, probably associated with some combination of diminished macroeconomic and financial market volatility, more predictable monetary policy, and the state of the business cycle.

The Brexit question will increase financial market volatility

That is the nearly unanimous response in a survey of leading experts, writes a Centre for Macroeconomics tea

Detecting Mutiple Breaks in Financial Market Volatility Dynamics

Nous appliquons plusieurs nouveaux tests conçus pour déceler les ruptures structurelles dans la dynamique de variance et de covariance conditionnelles. Les tests s'appliquent à la fois aux processus de la classe ARCH et de type SV et tiennent compte des caractéristiques de mémoire longue. Nous les appliquons également aux estimateurs de volatilité engendrés par les données, en utilisant des données à haute fréquence et nous suggérons des applications multivariées. En plus de déterminer la présence des ruptures, les statistiques permettent d identifier le nombre de ruptures ainsi que l'emplacement de ruptures multiples. Nous étudions la taille et la puissance des nouveaux tests pour divers modèles réalistes univariés et multivariés de variance conditionnelle et d échantillonnage. L article conclut avec une analyse empirique à partir de données provenant des marchés d actions et de taux de change pour lesquels nous trouvons de multiples ruptures associées aux crises financières asiatiques et russes. Dans les échantillons sélectionnés avant et après les ruptures, nous trouvons des changements dans la dynamique et dans la mémoire longue de la volatilité.We apply several recently proposed tests for structural breaks in conditional variance and covariance dynamics. The tests apply to both the class of ARCH and SV type processes and allow for long memory features. We also apply them to data-driven volatility estimators using high-frequency data and suggest multivariate applications. In addition to testing for the presence of breaks, the statistics allow to identify the number of breaks and the location of multiple breaks. We study the size and power of the new tests under various realistic univariate and multivariate conditional variance models and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. We find changes in the dynamics and long memory of volatility in the samples prior and post the breaks