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This paper presents an overview of the literature on applications of copulas in the modelling of financial time series. Copulas have been used both in multivariate time series analysis, where they are used to charaterise the (conditional) cross-sectional dependence between individual time...
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Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast...
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Key sources of disagreement among economic forecasters are identified by using data on cross-sectional dispersion in forecasters' long- and short-run predictions of macroeconomic variables. Dispersion among forecasters is highest at long horizons where private information is of limited value and...
Persistent link: https://www.econbiz.de/10008864338
The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We motivate our study with analytical results on the distortions caused by some widely used loss functions, when used with...
Persistent link: https://www.econbiz.de/10008866478
This paper presents new methods for comparing the accuracy of estimators of the quadratic variation of a price process. I provide conditions under which the relative accuracy of competing estimators can be consistently estimated (as T--[infinity]), and show that forecast evaluation tests may be...
Persistent link: https://www.econbiz.de/10008866493
Many theories in finance imply monotonic patterns in expected returns and other financial variables. The liquidity preference hypothesis predicts higher expected returns for bonds with longer times to maturity; the Capital Asset Pricing Model (CAPM) implies higher expected returns for stocks...
Persistent link: https://www.econbiz.de/10008872325
Forecast rationality under squared error loss implies various bounds on second moments of the data across forecast horizons. For example, the mean squared forecast error should be increasing in the horizon, and the mean squared forecast should be decreasing in the horizon. We propose rationality...
Persistent link: https://www.econbiz.de/10010690859