Results 161 to 170 of about 4,544 (205)
Modelling the age distribution of longevity leaders. [PDF]
Sci RepKiss C, Németh L, Vető B.
europepmc +1 more source
Multi-scale fusion transformer for EV charging station load prediction. [PDF]
Sci RepLiu W, Qiao J, Wang W, Zhao X.
europepmc +1 more source
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2019
We discuss the backtesting of investment and trading strategies. We start with the challenges and pitfalls: overfitting, data preparation, and the effects of randomness. Then, we introduce and describe R software for backtesting. We demonstrate how to use the software for univariate and multivariate strategies (i.e. portfolio strategies) for two equity
Manfred Gilli +2 more
exaly +3 more sources
We discuss the backtesting of investment and trading strategies. We start with the challenges and pitfalls: overfitting, data preparation, and the effects of randomness. Then, we introduce and describe R software for backtesting. We demonstrate how to use the software for univariate and multivariate strategies (i.e. portfolio strategies) for two equity
Manfred Gilli +2 more
exaly +3 more sources
Backtesting Expected Shortfall and Beyond
Quantitative Finance, 2021We conduct a comprehensive study of the performance of leading backtesting procedures for expected shortfall. The tests differ in their analytical complexity, stability over different models, sensitivity to the sample sizes (both estimation and backtesting), and computational burden.
Kaihua Deng, Jie Qiu
exaly +2 more sources
Monitoring and Backtesting Churn Models
Expert Systems With Applications, 2011The evaluation of the relationship with the customer and related benefits has become a key point for a company's competitive advantage. The development of customer churn models has increased over the years. However, these models tend to be complex and used on demand, being developed every time the customer analysis is requested.
Christophe Mues
exaly +3 more sources
Backtesting an Equity Risk Model Under Solvency II [PDF]
Journal of Business Research, 2018 Backtesting is a technique for validating internal models under Solvency II, which allows for evaluating the discrepancies between the results provided by a model and real observations.
Luis Otero Gonzalez +1 more
exaly +2 more sources
2012
This chapter surveys methods for backtesting risk models using the ex ante riskmeasure forecasts from the model and the ex post realized portfolio profit or loss. The risk measure forecast can take the form of a VaR, an Expected Shortfall, or a distribution forecast.
Chris Kenyon, Roland Stamm
openaire +2 more sources
This chapter surveys methods for backtesting risk models using the ex ante riskmeasure forecasts from the model and the ex post realized portfolio profit or loss. The risk measure forecast can take the form of a VaR, an Expected Shortfall, or a distribution forecast.
Chris Kenyon, Roland Stamm
openaire +2 more sources
The Journal of Portfolio Management, 2013
When evaluating a trading strategy, it is routine to discount the Sharpe ratio from a historical backtest. The reason is simple: there is inevitable data mining by both the researcher and by other researchers in the past. Our paper provides a statistical framework that systematically accounts for these multiple tests.
Campbell R. Harvey, Yan Liu
openaire +1 more source
When evaluating a trading strategy, it is routine to discount the Sharpe ratio from a historical backtest. The reason is simple: there is inevitable data mining by both the researcher and by other researchers in the past. Our paper provides a statistical framework that systematically accounts for these multiple tests.
Campbell R. Harvey, Yan Liu
openaire +1 more source