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Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks

Baruník, Jozef - 2015

crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework …

Persistent link: https://www.econbiz.de/10013024184

Forecasting Stock Market Crashes via Machine Learning

Dichtl, Hubert; Drobetz, Wolfgang; Otto, Tizian - 2021

and among the best-performing univariate benchmarks, while still being truly out-of-sample. The ability to forecast …

Persistent link: https://www.econbiz.de/10013225686

Forecasting Bond Risk Premia using Stationary Yield Factors

Hoogteijling, Tobias; Martens, Martin; van der Wel, Michel - 2021

The standard way to summarize the yield curve is to use the first three principal components of the yield curve, resulting in level, slope and curvature factors. Yields, however, are non-stationary. We analyze the first three principal components of yield changes, which correspond to changes in...

Persistent link: https://www.econbiz.de/10013233328

Can Anchoring Explain Biased Forecasts? Experimental Evidence

Meub, Lukas - 2015

Biased forecasts, particularly the inadequate adjustment from current values and excessive clustering, are increasingly explained as resulting from anchoring. However, experiments presented in support of this interpretation lack economic conditions, particularly monetary incentives, feedback for...

Persistent link: https://www.econbiz.de/10013035246

Predictability Puzzles

Eraker, Bjorn - 2020

Dynamic equilibrium models based on present value computation imply that returns are predictable but also generate particular patterns of predictability in asset returns. I take advantage of this to construct a set of tests of Equilibrium Generated Predictability (EGP). I apply the tests to...

Persistent link: https://www.econbiz.de/10012831389

Stock Returns Forecasting via Machine Learning with Average Windows Forecasts

Ho, Tsung-Wu; LIN, Ya-chi - 2022

Stock returns predictability has been a long-standing topic in the literature on financial economics. Developments in prediction technology have facilitated the wide use of machine learning techniques, which motivates our study of whether stock returns predictability can be improved using...

Persistent link: https://www.econbiz.de/10013313206

Time-frequency forecast of the equity premium

Faria, Gonçalo; Verona, Fabio - 2020

paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between …

Persistent link: https://www.econbiz.de/10012208225

Forecasting term structure of interest rates in Japan

Ishii, Hokuto - In: International Journal of Financial Studies : open … 7 (2019) 3/39, pp. 1-35

In this paper, we examined and compared the forecast performances of the dynamic Nelson–Siegel (DNS), dynamic Nelson …–Siegel–Svensson (DNSS), and arbitrage-free Nelson–Siegel (AFNS) models after the financial crisis period. The best model for the forecast … that the AFNS is superior to the DNS model in the long forecast horizon. …

Persistent link: https://www.econbiz.de/10012039649

On the economic determinants of optimal stock-bond portfolios : international evidence

Conrad, Christian; Stürmer, Karin - 2017 - This version: May 31, 2017

Using a modified DCC-MIDAS specification that allows the long-term correlation component to be a function of multiple explanatory variables, we show that the stock-bond correlation in the US, the UK, Germany, France, and Italy is mainly driven by inflation and interest rate expectations as well...

Persistent link: https://www.econbiz.de/10011745369

Forecasting Negative Yield-Curve Distributions

Jun, Jae-Yun - 2017

, this policy involves difficulties for market practitioners as there is no model that enables to forecast negative interest …

Persistent link: https://www.econbiz.de/10012948165