Showing 1 - 10 of 2,603
We examine the pricing of tail risk in international stock markets. We find that the tail risk of different countries is highly integrated. Introducing a new World Fear index, we find that local and global aggregate market returns are mainly driven by global tail risk rather than local tail...
Persistent link: https://www.econbiz.de/10011751251
Models based on factors such as size, value, or momentum are ubiquitous in asset pricing. Therefore, portfolio allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a standard tool for liquid individual assets, this measure is...
Persistent link: https://www.econbiz.de/10011860248
We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...
Persistent link: https://www.econbiz.de/10012426271
We compare the predictive ability and economic value of implied, realized and GARCH volatility models for 13 equity indices from 10 countries. Model ranking is similar across countries, but varies with the forecast horizon. At the daily horizon, the Heterogeneous Autoregressive model offers the...
Persistent link: https://www.econbiz.de/10012996175
This paper provides global evidence supporting the hypothesis that expected return models are enhanced by the inclusion of variables that describe the evolution of book-to-market-changes in book value, changes in price, and net share issues. This conclusion is supported using data representing...
Persistent link: https://www.econbiz.de/10012022063
The study examined high volatile assets, specifically the currency exchange rate of the open financial market. Takes into consideration the five most traded paired currencies of the global financial market. And observed, generally, the data set of the unit currency exchange rate exhibit...
Persistent link: https://www.econbiz.de/10012835628
Recent research suggests that machine learning models dominate traditional linear models in predicting cross-sectional stock returns. We confirm this finding when predicting one-month forward-looking returns based on a set of common stock characteristics, including predictors such as short-term...
Persistent link: https://www.econbiz.de/10012840386
This paper introduces a new out-of-sample forecasting methodology for monthly market returns using the variance risk premium (VRP) that is both statistically and economically significant. This methodology is motivated by the `beta representation,' which implies that the market risk premium is...
Persistent link: https://www.econbiz.de/10012902980
This paper considers how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of different vector autoregressive models, the investor is able, each period, to revise past predictive mistakes and learn about...
Persistent link: https://www.econbiz.de/10012897719
The risk premium of stocks due to priced variance risk is summarized to two variables -- the stock-specific price of variance risk (the difference between realized and option-implied variance) and the quantity (i.e., how stock prices respond to their variance shocks) of variance risk....
Persistent link: https://www.econbiz.de/10012855216