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Real-Time Bayesian Learning and Bond Return Predictability

Fulop, Andras; Li, Junye; WAN, Runqing - 2019

The paper examines statistical and economic evidence of out-of-sample bond return predictability for a real-time Bayesian investor who learns about parameters, hidden states, and predictive models over time. We find some statistical evidence using information contained in forward rates. However,...

Persistent link: https://www.econbiz.de/10014120968

Mixed-Frequency Predictive Regressions with Parameter Learning

Leippold, Markus; Yang, Hanlin - 2023

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...

Persistent link: https://www.econbiz.de/10014348997

Long-term investing under uncertain parameter instability

Keijsers, Bart - 2023

mixture innovation model using Bayesian methods. This allows us to estimate the break risk and the uncertainty around it. The …

Persistent link: https://www.econbiz.de/10014416056

Many Risks, One (Optimal) Portfolio

Homescu, Cristian - 2014

Persistent link: https://www.econbiz.de/10013050012

Tail Risk Protection in Asset Management

Homescu, Cristian - 2014

Tail risk refers to the possibility that a rare event would adversely affect the value of a portfolio in a significant … manner. It became much more relevant due to recent periods of strong market turbulence.We describe how to quantify such risk …, which tail risk protection strategies were considered in the literature, their effectiveness and associated costs. We also …

Persistent link: https://www.econbiz.de/10013044093

Uncertain identification

Giacomini, Raffaella; Kitagawa, Toru; Volpicella, Alessio - In: Quantitative economics : QE ; journal of the … 13 (2022) 1, pp. 95-123

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose...

Persistent link: https://www.econbiz.de/10012807735

A Tractable Characterisation of Linear Regression Model Uncertainty

Calvert Jump, Robert - 2022

A central maxim in statistics is that correlation does not imply causation, and a lack of correlation does not imply a lack of causation. However, this does not mean that correlation contains no informational content whatsoever for causality. In this paper, I propose a tractable characterisation...

Persistent link: https://www.econbiz.de/10013324373

Generalized dynamic factor model + GARCH exploiting multivariate information for univariate prediction

Alessi, Lucia (contributor); Barigozzi, Matteo (contributor) - 2007

We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns....

Persistent link: https://www.econbiz.de/10003321460

Evaluating density forecasts with an application to stock market returns

Raaij, Gabriela de; Raunig, Burkhard - 2002

in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density …

Persistent link: https://www.econbiz.de/10011431370

Range-based volatility estimation and forecasting

Benčík, Daniel - 2014

In this paper, we analyze new possibilities in predicting daily ranges, i.e. differences between daily high and low prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges and explore the use of these more...

Persistent link: https://www.econbiz.de/10010461231