How to build a factor portfolio: Does the allocation strategy matter?

Dichtl, Hubert; Drobetz, Wolfgang; Wendt, Viktoria-Sophie

doi:10.1111/eufm.12264

Cited by 7 publications

(4 citation statements)

References 120 publications

(149 reference statements)

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“…Despite our good results, it would be interesting to carry out an economic valuation of the volatility in this field, suggested by [55,56]. This future research would take into account more general MGARCH models and the risk aversion of the investor.…”

Section: Discussionmentioning

confidence: 96%

“…Asset pricing theory suggests that the unconditional expected returns for stocks should be positive, and that stocks should have the highest expected return. So, following [55][56][57], we do not consider return vectors with all of their components negative, because this does not respond to reality. The selected vectors of returns have to be a proxy of expected returns, and the prior belief of investors is that those returns should be non-negative, and even as high as possible.…”

Section: Optimal Portfolio Weightsmentioning

confidence: 99%

“…Finally, in the third case, we have considered the no overlapping 13 yearly averages without discarding any. In order to compare and select the best option of the three strategies we follow the approach of [56].…”

Section: Optimal Portfolio Weightsmentioning

confidence: 99%

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Co-Movements between Eu Ets and the Energy Markets: A Var-Dcc-Garch Approach

et al. 2021

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This paper analyzes the co-movements of prices of fossil fuels, energy stock markets and EU allowances. This analysis is conducted in order to identify the spillover effect of volatility and correlation among these financial markets, and to provide a scientific basis that shows the interest of incorporating sustainable assets in the design of minimum risk strategies of investment. To achieve this goal, we have used a Vector Autoregressive-Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (VAR-DCC-GARCH) model that also incorporates a stock index of industrial companies as a leading indicator of the level of economic activity. In addition, the paper conducts an impulse response analysis to determine how unexpected shocks to prices are propagated along time, and, in particular, how they affect prices of the others, both in mean, variance and correlation. Therefore, the results of this one- and two-dimensional analysis allow for the study of short and long run dynamics of the relationship among those prices, thus, providing greater meaning and information for investors, which has implications for building their portfolios. The analyzed period was from January 2010 to February 2021, so that the data include half of phase II, full phase III and the onset of phase IV of the EU ETS, as well as the COVID-19 outbreak in the European context. We also analyzed whether the EUA price impulses the demand of clean energy stocks, which has important implications for the objective of triggering the investment in clean energy. Our results show the transmission mechanism of all of those prices, which are relevant not only for investors but also for policymakers to construct an early-warning system, revealing the most important transmission channels. Moreover, from an investment viewpoint, we observe a decline in dirty energies and a rise in the clean energy market, which might be an indication of the progress towards the energy transition to renewables sources within a circular economy perspective. Therefore, this shows that the EU ETS is achieving its goals, and that clean energy companies, aligned with their role towards socially responsible initiatives, are also gaining acceptance in terms of investments, which would be beneficial for the environment.

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Section: Discussionmentioning

confidence: 96%

Section: Optimal Portfolio Weightsmentioning

confidence: 99%

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Co-Movements between Eu Ets and the Energy Markets: A Var-Dcc-Garch Approach

et al. 2021

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“…Several empirical studies by Sharma and Vipul (2018), Silva et al (2020), Yunus (2020), and Dichtl et al (2021) on the allocation of financial assets (e.g., gold, stocks, bonds, and real estate) show that funds can be fixed income, stocks or net asset values, multiple markets or currencies, and commodities. Researchers are increasingly trying to figure out what impact stocks have on other asset classes such as currencies, fixed income, and commodities.…”

Section: Capm Modelmentioning

confidence: 99%

Using the Capital Asset Pricing Model and the Fama–French Three-Factor and Five-Factor Models to Manage Stock and Bond Portfolios: Evidence from Timor-Leste

Anuno,

Madaleno,

Vieira

2023

JRFM

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Timor-Leste is a new country still in the process of economic development and does not yet have a capital market for stock and bond investments. These two asset classes have been invested in international capital markets such as the US, the UK, Japan, and Europe. We examine the performance of the capital asset pricing model (CAPM) and the Fama–French three-factor and five-factor models on the excess returns of Timor-Leste’s equity and bond investments in the international market over the period 2006 to 2019. Our empirical results show that the market factor (MKT) is positively and significantly associated with the excess returns of the CAPM and the Fama–French three-factor and five-factor models. Moreover, the two variables Small Minus Big (SMB) as a size factor and High Minus Low (HML) as a value factor have a negative and significant effect on the excess returns in the Fama–French three-factor model and five-factor model. Further analysis revealed that the explanatory power of the Fama–French five-factor model is that the Robust Minus Weak (RMW) factor as a profitability factor is positively and significantly associated with excess returns, while the Conservative Minus Aggressive (CMA) factor as an investment factor is insignificant.

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The diversification benefits of cryptocurrency factor portfolios: Are they there?

Han,

Newton,

Platanakis

et al. 2024

Rev Quant Finan Acc

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We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.

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How to build a factor portfolio: Does the allocation strategy matter?

Cited by 7 publications

References 120 publications

Co-Movements between Eu Ets and the Energy Markets: A Var-Dcc-Garch Approach

Co-Movements between Eu Ets and the Energy Markets: A Var-Dcc-Garch Approach

Using the Capital Asset Pricing Model and the Fama–French Three-Factor and Five-Factor Models to Manage Stock and Bond Portfolios: Evidence from Timor-Leste

The diversification benefits of cryptocurrency factor portfolios: Are they there?

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