This paper analyses the ability of beta and other factors, like firm size and book-to-market, to explain cross-sectional variation in average stock returns on the Swedish stock market for the period 1980-1990. We correct for errors in variables problem of the estimated market beta. Since this...

We employ spatial econometrics techniques to investigate to what extent countries’ economic and geographical relations affect their stock market co-movements. Among the relations that we analyze, bilateral trade proves to be best suited to capture co-variations in returns. We find a strong...

This paper applies the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in...

We employ spatial econometrics techniques to investigate to what extentcountries’ economic and geographical relations affect their stock market comovements.We propose an econometric model that is particularly suitable for financial data, where common time trends prevail. In general, among the...

Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that...

We employ the optimal orthogonal portfolio approach to investigate if the size and book-to-market effects in US data are related to risk factors beside the market risk. This method enables us to estimate the upper limit of the risk premium, due to observed as well as all possible unobserved...

This paper employs a conditional asset-pricing model based on the optimal orthogonal portfolio approach to construct a factor portfolio that embodies all the latent factors important for pricing a given set of test assets. The advantage of this portfolio to the anomaly related mimicking...

We develop a stochastic volatility model with jumps in returns and volatility to analyze the risk spillover from the U.S. market and the regional market to a number of European countries' equity markets. The key advantage of this approach compared to the earlier approaches is that it enables us...

Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that...

We explore the relation between institutional quality, trust and stock-market participation. In our theoretical model, agents update their beliefs in a Bayesian manner based on observations on frauds and choose whether to invest in the stock market. The corresponding empirical model shows that...