Comovements of Different Asset Classes During Market Stress

This paper assesses the linkages between the most important U.S.financial asset classes (stocks, bonds, T-bills and gold) during periods of financial turmoil. Our results have potentially important implications for strategic asset allocation and pension fund management. We use multivariate extreme value theory to estimate the exposure of one asset class to extreme movements in the other asset classes. By applying structural break tests to those measures we study to what extent linkages in extreme asset returns and volatilities are changing over time. Univariate results andch bivariate comovement results exhib significant breaks in the 1970s and 1980s corresponding to the turbulent times of e.g. the oil shocks, Volcker's presidency of the Fed or the stock market crash of 1987.Flight to quality, financial market distress, extreme value theory

Estimating and Forecasting Asset Volatility and Its Volatility: A Markov-Switching Range Model

This paper proposes a new model for modeling and forecasting the volatility of asset markets. We suggest to use the log range defined as the natural logarithm of the difference of the maximum and the minimum price observed for an asset within a certain period of time, i.e. one trading week. There is clear evidence for a regime-switching behavior of the volatility of the S&P500 stock market index in the period from 1962 until 2007. A Markov-switching model is found to fit the data significantly better than a linear model, clearly distinguishing periods of high and low volatility. A forecasting exercise leads to promising results by showing that some specifications of the model are able to clearly decrease forecasting errors with respect to the linear model in an absolute and mean square sense.Volatility, range, Markov-switching, GARCH, forecasting.

Comovements of Returns and Volatility in International Stock Markets: A High-Frequency Approach

This paper analyzes common factors in the continuous volatility component, co-extreme and co-jump behavior of a sample of stock market indices. In order to identify those components in stock price processes during a trading day we use high-frequency data and techniques. We show that in most of the cases one common factor is enough to describe the largest part of the international variation in the continuous part of volatility and that this factor's importance has increased over time. Furthermore, we find strong evidence for asymmetries between extremely negative and positive co-extreme close-open returns and of negative and positive co-jumps across countries..Volatility, realized volatility, high-frequency, comovements, cojumps

Духовно-консервативний феномен Григорія Сковороди та реальність українського необароко у контексті відродження християнської індивідуальності

This paper analyzes common factors in the continuous volatility component, co-extreme and co-jump behavior of a sample of stock market indices. In order to identify those components in stock price processes during a trading day we use high-frequency data and techniques. We show that in most of the cases one common factor is enough to describe the largest part of the international variation in the continuous part of volatility and that this factor’s importance has increased over time. Furthermore, we find strong evidence for asymmetries between extremely negative and positive co-extreme close-open returns and of negative and positive co-jumps across countries

Estimating and Forecasting Asset Volatility and Its Volatility: A Markov-Switching Range Model

This paper proposes a new model for modeling and forecasting the volatility of asset markets. We suggest to use the log range defined as the natural logarithm of the difference of the maximum and the minimum price observed for an asset within a certain period of time, i.e. one trading week. There is clear evidence for a regime-switching behavior of the volatility of the S&P500 stock market index in the period from 1962 until 2007. A Markov-switching model is found to fit the data significantly better than a linear model, clearly distinguishing periods of high and low volatility. A forecasting exercise leads to promising results by showing that some specifications of the model are able to clearly decrease forecasting errors with respect to the linear model in an absolute and mean square sense