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Lasso Regressions and Forecasting Models in Applied Stress Testing

Chan-Lau, Jorge A. - 2016

Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are...

Persistent link: https://www.econbiz.de/10012991517

Evaluating Performance of Regression Machine Learning Models Using Multiple Error Metrics in Azure Machine Learning Studio

Botchkarev, Alexei - 2018

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud … limitations, e.g. small number of performance metrics and lack of functionality to evaluate custom built regression models with R … evaluation of the regression models. EEM offers 4 times more metrics than built-in Azure Evaluate Model module. EEM metrics …

Persistent link: https://www.econbiz.de/10012919484

Dynamic quantile panel data analysis of stock returns predictability

Güloğlu, Bülent; Uyar, Sinem Güler Kangalli; Uyar, Umut - In: International journal of economics and finance 8 (2016) 2, pp. 115-126

Persistent link: https://www.econbiz.de/10011442125

Threshold bipower variation and the impact of jumps on volatility forecasting

Corsi, Fulvio; Pirino, Davide; Renò, Roberto - 2010

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous component using estimators which are not...

Persistent link: https://www.econbiz.de/10008729093

Forecasting time-varying covariance with a range-based dynamic conditional correlation model

Chou, Ray Yeutien; Wu, Chun-chou; Liu, Nathan - In: Review of quantitative finance and accounting 33 (2009) 4, pp. 327-345

This paper proposes a range-based dynamic conditional correlation (DCC) model combined by the return-based DCC model and the conditional autoregressive range (CARR) model. The substantial gain in efficiency of volatility estimation can boost the accuracy for estimating time-varying covariances....

Persistent link: https://www.econbiz.de/10003927245

Some like it smooth, and some like it rough : untangling continuous and jump components in measuring, modeling, and forecasting asset return volatility

Andersen, Torben; Bollerslev, Tim; Diebold, Francis X. - 2003 - This version: September 2003

A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high-frequency returns coupled with relatively simple reduced-form time series modeling procedures. Building on...

Persistent link: https://www.econbiz.de/10009764770

Confounding and missing data in cost-effectiveness analysis : comparing different methods

Härkänen, Tommi; Maljanen, Timo; Lindfors, Olavi; … - In: Health economics review 3 (2013) 8, pp. 1-11

this can result in biased results. Parametric models such as regression models are commonly applied to adjust for … regression model for costs, a hierarchical linear model for effectiveness, and Bayesian inference. Results : The adjustment for …

Persistent link: https://www.econbiz.de/10009783264

Liquidity-induced dynamics in futures markets

Fagan, Stephen; Gençay, Ramazan - 2007

Futures contracts on the New York Mercantile Exchange are the most liquid instruments for trading crude oil, which is the world’s most actively traded physical commodity. Under normal market conditions, traders can easily find counterparties for their trades, resulting in an efficient market...

Persistent link: https://www.econbiz.de/10011523414

Time-adaptive probabilistic forecasts of electricity spot prices with application to risk management.

López Cabrera, Brenda; Schulz, Franziska - 2016

The increasing exposure to renewable energy has amplified the need for risk management in electricity markets. Electricity price risk poses a major challenge to market participants. We propose an approach to model and fore- cast electricity prices taking into account information on renewable...

Persistent link: https://www.econbiz.de/10011538153

The impact of the use of forecasts in information sets

Gallo, Giampiero M.; Granger, C. W. J.; Jeon, Yongil - 1999

We analyze the properties of multiperiod forecasts which are formulated by a number of companies for a fixed horizon ahead which moves each month one period closer and are collected and diffused each month by some polling agency. Some descriptive evidence and a formal model suggest that knowing...

Persistent link: https://www.econbiz.de/10010504318

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