Estimating and forecasting volatility of stock market returns using GARCH models – with an application to value-at-risk and expected shortfall

This could be very useful for future missions since change detection can be done more effectively in an interesting area by using time series of geocoded images with different

Abstract: Different stock–environment recruitment models for Norwegian spring spawning herring (Clupea harengus) are fitted to a time series of spawning stock, recruitment,

NEA Greenland halibut: Maintain time series to monitor distribution and abundance for adult stock and juveniles (trawl indices). Survey data are also used as tuning series for

Using the root mean squared error (RMSE) as measure for forecasting ability over various time horizons, Meese and Rogoff find that the wide range of exchange rate models

The results show large statistical significant improvements in point forecasting of Bitcoin and Ethereum when using combinations of univariate models and in density forecasting

According to the feedback hypothesis, increased volatility in the stock market causes higher expected return, meaning that the volatility is the primary effect and the

GARCH models provide approximate descriptions of conditional volatility for a wide range of volatility processes, and the LM test shows that the GARCH (1,1) specification

First, we define the error metric that we use to compare different forecasting models. Input to our method are time series of the var- ious predictions of meteorological