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of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much …
Persistent link: https://www.econbiz.de/10012127861
Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its … standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility … standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more …
Persistent link: https://www.econbiz.de/10012910114
The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...
Persistent link: https://www.econbiz.de/10013250734
, with a particular emphasis on bank profitability. Methodologically, it employs two multivariate time series models, namely … comprehensive profitability outlook for the Maltese core banking sector. Key findings are summarized as follows: (i) neither of the …
Persistent link: https://www.econbiz.de/10015053640
Recent contributions highlight the importance of intraday jumps in forecasting realized volatility at horizons up to … importance of considering the continuous/jump decomposition of volatility for the purpose of density forecasting. Specifically …
Persistent link: https://www.econbiz.de/10012902447
A reflection on the lackluster growth over the decade since the Global Financial Crisis has renewed interest in preventative measures for a long-standing problem. Advances in machine learning algorithms during this period present promising forecasting solutions. In this context, the paper...
Persistent link: https://www.econbiz.de/10013362692
The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...
Persistent link: https://www.econbiz.de/10012416151
The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...
Persistent link: https://www.econbiz.de/10012180543
We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and...
Persistent link: https://www.econbiz.de/10014351271
An accurate forecast of intraday volume is a key aspect of algorithmic trading. This manuscript proposes a state-space model to forecast intraday trading volume via the Kalman filter and derives closed-form expectation-maximization (EM) solutions for model calibration. The model is extended to...
Persistent link: https://www.econbiz.de/10012930388