Economic and statistical measures of forecast accuracy

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

“…Second, as Granger and Pesaran (2000) point out, the choice of a loss functional should reflect the objectives of the forecast user. Therefore, we follow Sarma et al (2003) and consider a potential loss function of a regulating institution to evaluate the cES forecast performance.…”

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

“…Second, as Granger and Pesaran (2000) point out, the choice of a loss functional should reflect the objectives of the forecast user. Therefore, we follow Sarma et al (2003) and consider a potential loss function of a regulating institution to evaluate the cES forecast performance.…”

Small levels of predictability and large economic gains

“…Also related to this paper is the work by Elliott and Timmermann (2002), Granger and Pesaran (2000) and Pesaran and Timmermann (1995), where they argued that in evaluation of stock market forecasts profits generated from using the forecasts are more appropriate than the conventional statistical measures of forecast accuracy. In particular, Granger and Pesaran (2000) have studied the issue of economic versus statistical measures of forecast accuracy using a two-state Markov process.…”

Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness

“…The optimal decision rule f * (x) = sign(η(x) − c(x)) is well-known, see, e.g., Boyes, Hoffman, and Low (1989), Schervish (1989), Granger and Pesaran (2000), or Elliott and Lieli (2013). 13 More importantly, we show that the optimal decision rule for the convexified problem that can be easily solved in practice coincides with f * .…”

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

“…Second, as Granger and Pesaran (2000) point out, the choice of a loss functional should reflect the objectives of the forecast user. Therefore, we follow Sarma et al (2003) and consider a potential loss function of a regulating institution to evaluate the cES forecast performance.…”

Small levels of predictability and large economic gains

“…Also related to this paper is the work by Elliott and Timmermann (2002), Granger and Pesaran (2000) and Pesaran and Timmermann (1995), where they argued that in evaluation of stock market forecasts profits generated from using the forecasts are more appropriate than the conventional statistical measures of forecast accuracy. In particular, Granger and Pesaran (2000) have studied the issue of economic versus statistical measures of forecast accuracy using a two-state Markov process.…”

Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness

“…The optimal decision rule f * (x) = sign(η(x) − c(x)) is well-known, see, e.g., Boyes, Hoffman, and Low (1989), Schervish (1989), Granger and Pesaran (2000), or Elliott and Lieli (2013). 13 More importantly, we show that the optimal decision rule for the convexified problem that can be easily solved in practice coincides with f * .…”

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

“…Second, as Granger and Pesaran (2000) point out, the choice of a loss functional should reflect the objectives of the forecast user. Therefore, we follow Sarma et al (2003) and consider a potential loss function of a regulating institution to evaluate the cES forecast performance.…”

Small levels of predictability and large economic gains

“…Also related to this paper is the work by Elliott and Timmermann (2002), Granger and Pesaran (2000) and Pesaran and Timmermann (1995), where they argued that in evaluation of stock market forecasts profits generated from using the forecasts are more appropriate than the conventional statistical measures of forecast accuracy. In particular, Granger and Pesaran (2000) have studied the issue of economic versus statistical measures of forecast accuracy using a two-state Markov process.…”

Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness

“…The optimal decision rule f * (x) = sign(η(x) − c(x)) is well-known, see, e.g., Boyes, Hoffman, and Low (1989), Schervish (1989), Granger and Pesaran (2000), or Elliott and Lieli (2013). 13 More importantly, we show that the optimal decision rule for the convexified problem that can be easily solved in practice coincides with f * .…”