The role of transaction costs and risk aversion when selecting between one and two regimes for portfolio models

Platanakis, Emmanouil; Σάκκας, Αθανάσιος; Sutcliffe, Charles

doi:10.1080/13504851.2018.1486984

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…To control for this, in our final robustness check we allow for the presence of two market regimes which have different asset means, variances and correlations. We applied the regimes methodology used by Platanakis et al (2019b) and used a Taylor series expansion for the CRRA utility function to incorporate higher moments in the portfolio construction process. We ruled out short sales and estimated a two-state multivariate regime switching model for each 60 month rolling window for λ = 2, 5 and 10.…”

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confidence: 99%

Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach

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Platanakis

Stafylas

et al. 2021

The British Accounting Review

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confidence: 99%

Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach

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Platanakis

Stafylas

et al. 2021

The British Accounting Review

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“…Ouattara et al (2019) focused on a representative farmer’s attitude toward risk; the results reveal that crop production is highly affected by the farmer’s attitude toward risk. Platanakis et al (2019) investigated US data and concluded that differences in risk preference cause investors to prefer different investment models. The above literature review shows that risk aversion has a profound impact on decision-making.…”

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confidence: 99%

Risk investment decisions within the deterministic equivalent income model

Song

et al. 2020

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Purpose The purpose of this paper is to establish a deterministic equivalent income model (DEIM) based on the risk cost (RC) and risk aversion of investors. The model fully considers both subjective and objective factors that affect risk investment and reasonably evaluates risk investment schemes to choose the correct investment scheme and gain greater investment returns. Design/methodology/approach The utility function is used to measure the extent to which an investor is satisfied by investment returns in various scenarios. Risk aversion expresses subjective attitude of investors to risk. RC represents risk loss in currency. This methodology is based on risk aversion function, utility function and RC theory to establish DEIM. Findings This study shows that investors with different risk preferences have different certainty equivalent returns (CER), so their choices of investment options change accordingly. Practical implications In this paper, the authors use DEIM to test an investment case and conclude that the CER and investment scheme both change with different risk preferences. At the same time, case analysis shows that DEIM is reasonable and stable when evaluating risk investment schemes. Originality/value In this study, the authors innovate by introducing both the RC and risk aversion degree into risk investment schemes evaluation and by deriving a utility function from the absolute risk aversion function to build a utility decision matrix and establish DEIM. The model combines the subjective and objective factors that influence risk investment decisions.

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“…Since the seminal work of Hamilton (1989), HM models have been fruitfully applied in diverse areas such as communications engineering and bioinformatics Cappé et al (2006). In finance, they have been extensively used in predicting and explaining exchange rates Panopoulou and Pantelidis (2015); Lee and Chen (2006); Frömmel et al (2005); Bollen et al (2000), stock market returns Dias et al (2015); Angelidis and Tessaromatis (2009); Guidolin and Timmermann (2006), business cycles Tian and Shen (2019); Chauvet and Hamilton (2006), realized volatility Koki et al (2020); Liu and Maheu (2018), the behavior of commodities Pereira et al (2017) and in portfolio allocation Platanakis et al (2019). The reason for their increased popularity is that they present various attractive features.…”

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Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models

Koki¹,

Leonardos²,

Piliouras³

2020

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In this paper, we consider a variety of multi-state Hidden Markov models for predicting and explaining the Bitcoin, Ether and Ripple returns in the presence of state (regime) dynamics. In addition, we examine the effects of several financial, economic and cryptocurrency specific predictors on the cryptocurrency return series. Our results indicate that the Non-Homogeneous Hidden Markov (NHHM) model with four states has the best one-step-ahead forecasting performance among all competing models for all three series. The dominance of the predictive densities over the single regime random walk model relies on the fact that the states capture alternating periods with distinct return characteristics. In particular, the four state NHHM model distinguishes bull, bear and calm regimes for the Bitcoin series, and periods with different profit and risk magnitudes for the Ether and Ripple series. Also, conditionally on the hidden states, it identifies predictors with different linear and non-linear effects on the cryptocurrency returns. These empirical findings provide important insight for portfolio management and policy implementation.

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The role of transaction costs and risk aversion when selecting between one and two regimes for portfolio models

Abstract: We wish to thank Chris Brooks and Ioannis Oikonomou (Reading), and Xiaoxia Ye (Bradford) for their comments on an earlier draft.

Cited by 7 publications

References 19 publications

Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach

Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach

Risk investment decisions within the deterministic equivalent income model

Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models

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