A multi-covariate semi-parametric conditional volatility model using probabilistic fuzzy systems

2012 ◽  
Author(s):  
Rui Jorge Almeida ◽  
Nalan Basturk ◽  
Uzay Kaymak ◽  
Viorel Milea
Keyword(s):  
Fuzzy Systems ◽  
Conditional Volatility ◽  
Volatility Model

Determinism and Non-linear Behaviour of Log-return and Conditional Volatility: Empirical Analysis for 26 Stock Markets

2021 ◽  
pp. 227797872199565
Author(s):  
Zouhaier Dhifaoui
Keyword(s):  
Time Series ◽  
Stochastic Volatility ◽  
Linear Prediction ◽  
Stochastic Volatility Model ◽  
Robust Estimator ◽  
Conditional Volatility ◽  
Stochastic Behaviour ◽  
Volatility Model ◽  
Non Linear ◽  
Linear Behaviour

Determinism and non-linear behaviour in log-return and conditional volatility time series of the stock market index is examined for twenty-six countries. For this goal, the principal statistical techniques used in this study are a robust estimator of correlation dimension, a normalized non-linear prediction error, and pseudo-periodic surrogate data method. The proposed approach indicates, first, the stochastic behaviour of all log-return time series. Second, the inability of local linear, ARMA, or state- dependent noise models (such as ARCH, GARCH, and EGARCH) to describe its structure for the frontier, emerging, and developed markets. The same stochastic behaviour of conditional volatility time series, estimated by the stochastic volatility model with moving average innovations, is detected. This finding proves the efficiency of the stochastic volatility model compared with some analysed types of GARCH models for all studied markets. JEL Classification: C12, C52, D53, E44

scholarly journals Asymptotic Theory for Extended Asymmetric Multivariate GARCH Processes

2017 ◽  
Vol 6 (6) ◽  
pp. 13
Author(s):  
Manabu Asai ◽  
Michael McAleer
Keyword(s):  
Asymptotic Theory ◽  
Asymptotic Properties ◽  
Autoregressive Process ◽  
Multivariate Garch ◽  
Regularity Conditions ◽  
Conditional Volatility ◽  
Empirical Estimation ◽  
Equivalent Representation ◽  
Volatility Model ◽  
Quasi Maximum Likelihood

The paper considers various extended asymmetric multivariate conditional volatility models, and derives appropriate regularity conditions and associated asymptotic theory. This enables checking of internal consistency and allows valid statistical inferences to be drawn based on empirical estimation. For this purpose, we use an underlying vector random coefficient autoregressive process, for which we show the equivalent representation for the asymmetric multivariate conditional volatility model, to derive asymptotic theory for the quasi-maximum likelihood estimator. As an extension, we develop a new multivariate asymmetric long memory volatility model, and discuss the associated asymptotic properties.

scholarly journals The Efficiency of GARCH Models in Realizing Value at Risk Estimates

2020 ◽  
Vol 14 (1) ◽  
pp. 32-50
Author(s):  
Tomáš Jeřábek
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Risk ◽  
Market Risk ◽  
Conditional Volatility ◽  
Maximum Loss ◽  
Specific Distribution ◽  
Volatility Models ◽  
Volatility Model ◽  
Traditional Approaches

Market risk is an important type of financial risk that is usually caused by price fluctuations in financial markets. One determinant of market risk comprises Value at Risk (VaR), which is defined as the maximum loss that can be achieved within a certain time horizon and at a given reliability level. The aim of the article is to determine the importance of selecting conditional volatility model within the parametric and semi-parametric approach for VaR estimation. The results ascertained show that the application of these models tends to provide more accurate predictions of actual losses as compared to traditional approaches to VaR estimates. Overall, the application of conditional volatility models ensures that VaR estimates are more flexible to adapt to changing market conditions – especially in the periods associated with higher return volatility. Furthermore, the results show that the differences between individual models of contingent volatility are primarily determined by selecting the specific distribution of the standardized residue series

H∞ control for Takagi–Sugeno fuzzy systems

2001 ◽  
Vol 32 (7) ◽  
pp. 915-924 ◽  
Author(s):  
Jun Yoneyama ◽  
Masahiro Nishikawa ◽  
Hitoshi Katayama ◽  
Akira Ichikawa
Keyword(s):  
Fuzzy Systems ◽  
Takagi Sugeno

Improvement of AODV Routing on MANETs Using Fuzzy Systems

2011 ◽  
Vol 7 (2) ◽  
pp. 102-106 ◽  
Author(s):  
Taqwa Odey Fahad ◽  
Abduladhim A. Ali
Keyword(s):  
Fuzzy Systems

Intrinsic Liquidity in Conditional Volatility Models

2016 ◽  
pp. 225
Author(s):  
Serge Darolles ◽  
Gaëlle Le Fol ◽  
Christian Francq ◽  
Jean-Michel Zakoïan
Keyword(s):  
Conditional Volatility ◽  
Volatility Models
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