Similar Search Results

A bibliometric analysis of machine learning econometrics in asset pricing

Zapata, Hector O.; Mukhopadhyay, Supratik - In: Journal of risk and financial management : JRFM 15 (2022) 11, pp. 1-17

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

Persistent link: https://www.econbiz.de/10013475217

Expectations and risk premia at 8:30AM : macroeconomic announcements and the yield curve

Hördahl, Peter; Remolona, Eli M.; Valente, Giorgio - 2015

Persistent link: https://www.econbiz.de/10011437580

Treasury Bond Price and Yield Curve Prediction via No Arbitrage Arguments and Machine Learning

Zhang, Weiping; Yang, Qing; Tian, Ruyan; Ye, Tingting; … - 2022

This paper proposes a novel bond return (price or yield curve) prediction methodology, unifying the classical no arbitrage pricing framework, which is ubiquitous and serves as the fundamental theoretical building block in mathematical finance, and empirical asset (bond) pricing methodologies,...

Persistent link: https://www.econbiz.de/10013306944

Investors’ valuation for asset liquidity and the corporate-treasury yield spread

Niestroj, Benjamin - In: International journal of economics and finance 6 (2014) 10, pp. 1-16

Persistent link: https://www.econbiz.de/10010421663

Ambiguity, nominal bond yields and real bond yields

Zhao, Guihai - 2018

Equilibrium bond-pricing models rely on inflation being bad news for future growth to generate upward-sloping nominal yield curves. We develop a model that can generate upward-sloping nominal and real yield curves by instead using ambiguity about inflation and growth. Ambiguity can help resolve...

Persistent link: https://www.econbiz.de/10011864574

Return seasonalities in government bonds and macroeconomic risk

Mikutowski, Mateusz; Karathanasopoulos, Andreas; … - In: Economics letters 176 (2019), pp. 114-116

Persistent link: https://www.econbiz.de/10012121248

Predicting returns in US treasuries : do tents matter?

Rebonato, Riccardo - In: International journal of theoretical and applied finance 21 (2018) 7, pp. 1-13

Persistent link: https://www.econbiz.de/10011957124

Diverging roads: theory-based vs. machine learning-implied stock risk premia

Grammig, Joachim; Hanenberg, Constantin; Schlag, Christian - 2020

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the … preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical … models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine …

Persistent link: https://www.econbiz.de/10012163064

Deep learning, predictability, and optimal portfolio returns

Babiak, Mykola; Baruník, Jozef - 2020

Persistent link: https://www.econbiz.de/10012542701

Extracting statistical factors when betas are time-varying

Gagliardini, Patrick; Ma, Hao - 2019 - This version: July 2019

This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is nonparametric regarding the way the loadings vary in time as functions of common shocks and individual...

Persistent link: https://www.econbiz.de/10012176811

1
2
3
4
5
6
7
8
9
10
Next
Last