Score-driven stochastic seasonality of the Russian rouble: an application case study for the period of 1999 to 2020

Ayala, Astrid; Blazsek, Szabolcs; Licht, Adrián

doi:10.1007/s00181-021-02103-6

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…In the literature, EGB2 distribution has been widely used in empirical studies, such as the modeling of income distribution [27], stock returns [32], future markets' sentiment [33], currency exchange rates [34,35], and carbon emission inequality [36].…”

Section: Review Of the Egb2 Distributionmentioning

confidence: 99%

“…Example A1. Underlying price S t = 5; strike price K ranges from 4 to 6 by step size 0.1, i.e., K = [4, 4.1, 4.2, • • • , 5.8, 5.9, 6]; time to maturity ranges from 20 to 60 days by step size 5, i.e., T − t = [20,25,30,35,40,45,50,55,60]; risk-free rate r = 0.03; volatility σ = 0.15.…”

Section: Further Study Directionsmentioning

confidence: 99%

“…We show our results by providing p-p plots, pricing bias plots, and reporting the RMSE, MAE for each case. Underlying price S t = 5; strike price K ranges from 4 to 6 by step size 0.1, i.e., K = [4, 4.1, 4.2, • • • , 5.8, 5.9, 6]; time to maturity ranges from 20 to 60 days by step size 5, i.e., T − t =[20,25,30,35, 40, 45, 50, 55, 60]; risk-free rate r = 0.03; a t,T = 0, b t,T = 0.1, p t,T = q t,T = 2. Underlying price S t = 5; strike price K ranges from 4 to 6 by step size 0.1, i.e., K = [4, 4.1, 4.2, • • • , 5.8, 5.9, 6]; time to maturity ranges from 20 to 60 days by step size 5, i.e., T − t =[20,25,30,35, 40, 45, 50, 55, 60]; risk-free rate r = 0.03; a t,T = 0, b t,T = 0.1, p t,T = 2, q t,T = 1.…”

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confidence: 99%

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Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

2023

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Connecting derivative pricing with tail risk management has become urgent for financial practice and academia. This paper proposes a novel option pricing model based on the exponential generalized beta of the second kind (EGB2) distribution. The newly proposed model is of generality, simplicity, robustness, and financial interpretability. Most importantly, one can detect tail risk signals by calibrating the proposed model. The model includes the seminal Black–Scholes (B−S) formula as a limit case and can perfectly “replicate” the option prices from Merton’s jump-diffusion model. Based on the proposed pricing model, three tail risk warning measures are introduced for tail risk signals detection: the EGB2 implied tail index, the EGB2 implied Value-at-Risk (EGB2-VaR), and the EGB2 implied risk-neutral density (EGB2 R.N.D.). Empirical results show that the new pricing model can yield higher pricing accuracy than existing models in normal and crisis periods, and three model-based tail risk measures can perfectly detect tail risk signals before financial crises.

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Section: Review Of the Egb2 Distributionmentioning

confidence: 99%

Section: Further Study Directionsmentioning

confidence: 99%

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Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

2023

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Score-driven multi-regime Markov-switching EGARCH: empirical evidence using the Meixner distribution

Blazsek

Haddad

2022

Studies in Nonlinear Dynamics &Amp; Econometrics

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In this paper, statistical and volatility forecasting performances of the non-path-dependent score-driven multi-regime Markov-switching (MS) exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models are explored. Three contributions to the existing literature are provided. First, we use all relevant score-driven distributions from the literature - namely, the Student’s t-distribution, general error distribution (GED), skewed generalized t-distribution (Skew-Gen-t), exponential generalized beta distribution of the second kind (EGB2), and normal-inverse Gaussian (NIG) distribution. We then introduce the score-driven Meixner (MXN) distribution-based EGARCH model to the literature on score-driven models. Second, proving the sufficient conditions of the asymptotic properties of the maximum likelihood (ML) estimator for non-path-dependent score-driven MS-EGARCH models is an unsolved problem. We provide a partial solution to that problem by proving necessary conditions for the asymptotic theory of the ML estimator. Third, to the best of our knowledge, this work includes the largest number of international stock indices from the G20 countries in the literature, covering the period of 2000–2022. We provide a discussion on the major events which caused common or non-common switching to the high-volatility regime for the G20 countries. The statistical performance and volatility forecasting results support the adoption of score-driven MS-EGARCH for the G20 countries.

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Modelling volatility dependence with score copula models

Alanya-Beltran

2022

Studies in Nonlinear Dynamics &Amp; Econometrics

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I study score-driven models for modelling high persistence dependence between financial volatility series. I model this persistence dependence with two components, one for the long memory and the other for the short-term process. The addition of components offers a parsimonious solution for modelling high persistence and also allows for a short-term component for the transient shocks. I apply the model to emerging equities in the Americas. The estimates are robust to the advent of the pandemic. In addition, data resampling and marginal alternatives deliver similar parameter estimates. The proposed two-component model improves the in-sample diagnostics and generates more accurate out-of-sample forecasts.

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Score-driven stochastic seasonality of the Russian rouble: an application case study for the period of 1999 to 2020

Cited by 4 publications

References 43 publications

Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

Score-driven multi-regime Markov-switching EGARCH: empirical evidence using the Meixner distribution

Modelling volatility dependence with score copula models

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