Time-Varying Risk Attitude and Conditional Skewness

Liu, Zhifeng; Zhang, Tingting; Wen, Fenghua

doi:10.1155/2014/174848

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…( 15 Abordagem semelhante foi utilizada por Chou et al (1992) e Liu et al (2014). 16 Mais detalhes sobre o Filtro de Kalman podem ser vistos em Harvey (1990).…”

Section: Aversão Ao Risco No Modelo Ccapmunclassified

Flutuações na aversão ao risco podem prever crises no mercado de ações brasileiro?

Ribeiro¹,

Silva²

2023

Inter. J. Sci. Manag. Tour.

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Neste artigo elaboramos uma medida empírica para a aversão ao risco variante no tempo utilzando o Modelo de Heteroscedasticidade Condicional Autorregressivo Generalizado em Média (GARCH-M), e, analisamos se essa medida é capaz de antecipar crises no mercado ações brasileiro, entre os anos de 2000 e 2020. Para isso utilizamos dois métodos, o tradicional Logit e o método de Machine Learning Florestas Aleatórias (FAs). Os resultados demonstram que nossa medida de aversão ao risco variante no tempo é uma variável relevante para se prever crises, uma vez que melhora a capacidade preditiva de ambos os métodos. Também mostramos que as FAs apresentam maior capacidade preditiva quando comparado ao Logit.

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“…( 15 Abordagem semelhante foi utilizada por Chou et al (1992) e Liu et al (2014). 16 Mais detalhes sobre o Filtro de Kalman podem ser vistos em Harvey (1990).…”

Section: Aversão Ao Risco No Modelo Ccapmunclassified

Flutuações na aversão ao risco podem prever crises no mercado de ações brasileiro?

Ribeiro¹,

Silva²

2023

Inter. J. Sci. Manag. Tour.

View full text Add to dashboard Cite

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“…Other papers have implemented Harvey and Siddique (1999) method to model asset returns (Lanne & Pentti, 2007; Wen et al., 2013) and risk premiums (Liu et al., 2014).…”

Section: Autoregressive Conditional Density (Arcd) and Autoregressive...mentioning

confidence: 99%

“…However, the simulations and empirical evidence in Dark (2010) are not able to verify the findings of Harvey and Siddique (1999) that including time-varying skewness reduces the persistence and asymmetry properties of the conditional variance. Other papers have implemented Harvey and Siddique (1999) method to model asset returns (Lanne & Pentti, 2007;Wen et al, 2013) and risk premiums (Liu et al, 2014). Jondeau and Rockinger (2003) proposed the same specification for the standardized residuals as Hansen (1994).…”

Section: Autoregressive Conditional Density (Arcd) and Autoregressive...mentioning

confidence: 99%

Modeling time‐varying higher‐order conditional moments: A survey

Soltyk

Chan²

2021

Journal of Economic Surveys

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Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time‐varying second‐order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher‐order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time‐varying higher‐order conditional moments in the last two decades. This paper outlines recent developments in modeling time‐varying higher‐order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time‐varying higher‐order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher‐order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.

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“…The skewness and kurtosis appears to deviate from the normal level, which indicates that the market exhibits significant abnormal return changes event. Some potential causes may be the time varying risk attitude and speculative activities prevalent in the financial markets [25]. Besides, since the null hypothesis of both Jarque-Bera test of normality and BDS test of independence are rejected, this further indicates that the market return contains unknown nonlinear dynamics, not easily captured by traditional linear models [26,27].…”

Section: Empirical Studiesmentioning

confidence: 99%

A Curvelet based Approach to Time Series Forecasting

Zou

Meiying

2015

Procedia Computer Science

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In this paper we propose a new Curvelet based methodology for modeling the financial time series data, addressing and incorporating the diverse range of data characteristics. With the proposed methodology, we analyze and model the geometric multi scale and chaotic data characteristics. Empirical studies with some typical financial time series data show that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed algorithm demonstrates the superior performance compared with the benchmark models.

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Time-Varying Risk Attitude and Conditional Skewness

Cited by 8 publications

References 42 publications

Flutuações na aversão ao risco podem prever crises no mercado de ações brasileiro?

Flutuações na aversão ao risco podem prever crises no mercado de ações brasileiro?

Modeling time‐varying higher‐order conditional moments: A survey

A Curvelet based Approach to Time Series Forecasting

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