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ratio. For large sample sizes, the correlation between pairs of performance measures is asymptotically equal to unity …. Therefore, a new specification for tests of correlation between pairs of performance measures, as well as a new multivariate …
Persistent link: https://www.econbiz.de/10012973178
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equivocal empirical evidence reported by several authors about the correlation of performance measures with the Sharpe ratio … will fail. The paper shows that for large sample sizes the correlation between pairs of performance measures that are … functions of the Sharpe ratio is unity. The correct null hypothesis for tests of correlation is therefore ρ=1. Two multivariate …
Persistent link: https://www.econbiz.de/10012970408
diversification benefits of correlations may vary substantially based on the time-varying measure of correlation used for different … asset types. Our study evaluates and compares alternative time-series correlation modeling techniques according to both … statistical and economic metrics, focusing specifically on individual asset pairs. We identify the moving correlation structure …
Persistent link: https://www.econbiz.de/10012830911
risk assessment, uncertainty-penalized optimization to counter estimation error and improve realized utility, and …
Persistent link: https://www.econbiz.de/10013251623
Forecasting based pricing of Weather Derivatives (WDs) is a new approach in valuation of contingent claims on nontradable underlyings. Standard techniques are based on historical weather data. Forward-looking information such as meteorological forecasts or the implied market price of risk (MPR)...
Persistent link: https://www.econbiz.de/10009511156
Most existing text-based sentiment measures in finance are lexicon-based which are effectively based on word counts of positive and negative sentiment dictionaries, and naturally lose most information. We measure news sentiment using BERT, a state-of-the-art large language model, which reads and...
Persistent link: https://www.econbiz.de/10014350274
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This paper studies portfolio optimization through improvements of ex-ante conditional covariance estimates. We use the cross-section of stock returns over a 52-year sample to analyze trading performance by implementing the machine learning algorithm of hierarchical clustering. We find that...
Persistent link: https://www.econbiz.de/10014514019