The frequency of regime switching in financial market volatility

BenSaïda, Ahmed

doi:10.1016/j.jempfin.2015.03.005

Cited by 22 publications

(21 citation statements)

References 43 publications

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“…A solution was recently proposed by BenSaïda () and proved to overcome the path dependency problem encountered in the numerical estimation of regime‐switching conditional volatility models. It consists of dissociating between the diverse volatility states; hence each conditional volatility process depends only on its lagged values and the residuals within the same generating regime.…”

Section: Theoretical Developmentmentioning

confidence: 99%

“…To construct a tractable likelihood (BenSaïda, ), the conditional variance of a regime‐switching msAPARCH model at time t in Equation can be written as

\begin{matrix} right (\begin{matrix} h_{t, 1}^{δ_{1} false/ 2} \\ ⋮ \\ h_{t, K}^{δ_{K} false/ 2} \end{matrix}) & left = (\begin{matrix} κ_{1} \\ ⋮ \\ κ_{K} \end{matrix}) + \sum_{i = 1}^{P} (\begin{matrix} β_{i, 1} \\ ⋮ \\ β_{i, K} \end{matrix}) ⊙ (\begin{matrix} h_{t - i, 1}^{δ_{1} false/ 2} \\ ⋮ \\ h_{t - i, K}^{δ_{K} false/ 2} \end{matrix}) & right \\ right & left + \sum_{j = 1}^{Q} (\begin{matrix} α_{j, 1} \\ ⋮ \\ α_{j, K} \end{matrix}) ⊙ [|\begin{matrix} r_{t} - c_{t, 1} \\ ⋮ \\ r_{t} - c_{t, K} \end{matrix}| - (\begin{matrix} γ_{j, 1} \\ ⋮ \\ γ_{j, K} \end{matrix}) \\ right & left ⊙ (\begin{matrix} r_{t} - c_{t, 1} \\ ⋮ \end{matrix})] \end{matrix}

…”

Section: Theoretical Developmentmentioning

confidence: 99%

“…The conditional distribution of z t in Equation can be of any type. An essential condition, however, is that the employed distribution must be defined over the whole real interval

double-struckR

, and must have zero mean and unit variance (BenSaïda, ; Slim et al, ). Bali and Theodossiou () and Cheng and Hung () suggest using the skewed generalized t (SGT) distribution for VaR forecasting owing to its attractiveness in nesting a variety of other distributions.…”

Section: Theoretical Developmentmentioning

confidence: 99%

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Value‐at‐risk under market shifts through highly flexible models

BenSaïda

Boubaker

Nguyen

et al. 2018

Journal of Forecasting

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Managing market risk under unknown future shocks is a critical issue for policymakers, investors, and professional risk managers. Despite important developments in market risk modeling and forecasting over recent years, market participants are still skeptical about the ability of existing econometric designs to accurately predict potential losses, particularly in the presence of hidden structural changes. In this paper, we introduce Markov‐switching APARCH models under the skewed generalized t and the generalized hyperbolic distributions to fully capture the fuzzy dynamics and stylized features of financial market returns and to generate value‐at‐risk (VaR) forecasts. Our empirical analysis of six major stock market indexes shows the superiority of the proposed models in detecting and forecasting unobservable shocks on market volatility, and in calculating daily capital charges based on VaR forecasts.

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Section: Theoretical Developmentmentioning

confidence: 99%

“…To construct a tractable likelihood (BenSaïda, ), the conditional variance of a regime‐switching msAPARCH model at time t in Equation can be written as

\begin{matrix} right (\begin{matrix} h_{t, 1}^{δ_{1} false/ 2} \\ ⋮ \\ h_{t, K}^{δ_{K} false/ 2} \end{matrix}) & left = (\begin{matrix} κ_{1} \\ ⋮ \\ κ_{K} \end{matrix}) + \sum_{i = 1}^{P} (\begin{matrix} β_{i, 1} \\ ⋮ \\ β_{i, K} \end{matrix}) ⊙ (\begin{matrix} h_{t - i, 1}^{δ_{1} false/ 2} \\ ⋮ \\ h_{t - i, K}^{δ_{K} false/ 2} \end{matrix}) & right \\ right & left + \sum_{j = 1}^{Q} (\begin{matrix} α_{j, 1} \\ ⋮ \\ α_{j, K} \end{matrix}) ⊙ [|\begin{matrix} r_{t} - c_{t, 1} \\ ⋮ \\ r_{t} - c_{t, K} \end{matrix}| - (\begin{matrix} γ_{j, 1} \\ ⋮ \\ γ_{j, K} \end{matrix}) \\ right & left ⊙ (\begin{matrix} r_{t} - c_{t, 1} \\ ⋮ \end{matrix})] \end{matrix}

…”

Section: Theoretical Developmentmentioning

confidence: 99%

“…The conditional distribution of z t in Equation can be of any type. An essential condition, however, is that the employed distribution must be defined over the whole real interval

double-struckR

Section: Theoretical Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Value‐at‐risk under market shifts through highly flexible models

BenSaïda

Boubaker

Nguyen

et al. 2018

Journal of Forecasting

Self Cite

View full text Add to dashboard Cite

show abstract

“…Seasonality and long-memory can be filtered out by a flexible Fourier form (FFF) technique and fractionally integrated volatility models [29]. Still, we choose to neglect the effect of volatility in our methodology since it is the distribution of the returns which has a direct effect on volatility dynamics [9]. Results are presented in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…In recent studies [6][7][8], the SGT is simply compared to the normal or other low-flexible distribution; BenSaïda [9] has studied its efficiency into detecting the real distribution of a given data via extensive simulations, and developed a closed-form distribution and quantile functions essential to improve the implementation of the value-at-risk (VaR) in financial risk analysis [6,7]. Similarly, applications on the GH are carried essentially on the nested versions [10][11][12][13][14], and scarcely on the general form [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Highly flexible distributions to fit multiple frequency financial returns

BenSaïda

Slim

2016

Physica A: Statistical Mechanics and its Applications

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GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks

Buczyński

Chlebuś

2023

Comput Econ

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This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be observed. In particular, their high value is often praised in Value-at-Risk. However, the lack of nonlinear structure in most approaches means that conditional variance is not adequately represented in the model. On the contrary, the recent rapid development of deep learning methods is able to describe any nonlinear relationship in a clear way. We propose GARCHNet, a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators in GARCH. The variance distributions considered in the paper are normal, t and skewed t, but the approach allows extension to other distributions. To evaluate our model, we conducted an empirical study on the logarithmic returns of the WIG 20 (Warsaw Stock Exchange Index), S&P 500 (Standard & Poor’s 500) and FTSE 100 (Financial Times Stock Exchange) indices over four different time periods from 2005 to 2021 with different levels of observed volatility. Our results confirm the validity of the solution, but we provide some directions for its further development.

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The frequency of regime switching in financial market volatility

Cited by 22 publications

References 43 publications

Value‐at‐risk under market shifts through highly flexible models

Value‐at‐risk under market shifts through highly flexible models

Highly flexible distributions to fit multiple frequency financial returns

GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks

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