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Machine-learning-based semiparametric time series conditional variance : estimation and forecasting

Dang, Justin; Ullah, Aman - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-12

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...

Persistent link: https://www.econbiz.de/10012814196

Assessing the risk characteristics of the cryptocurrency market : a GARCH-EVT-Copula approach

Bruhn, Pascal; Ernst, Dietmar - In: Journal of risk and financial management : JRFM 15 (2022) 8, pp. 1-28

The cryptocurrency market offers significant investment opportunities but also entails higher risks as compared to other asset classes. This article aims to analyse the financial risk characteristics of individual cryptocurrencies and of a broad cryptocurrency market portfolio. We construct a...

Persistent link: https://www.econbiz.de/10013380409

Best-arm identification using extreme value theory estimates of the CVaR

Troop, Dylan; Godin, Frédéric; Yu, Jia Yuan - In: Journal of risk and financial management : JRFM 15 (2022) 4, pp. 1-15

We consider a risk-aware multi-armed bandit framework with the goal of avoiding catastrophic risk. Such a framework has multiple applications in financial risk management. We introduce a new conditional value-at-risk (CVaR) estimation procedure combining extreme value theory with automated...

Persistent link: https://www.econbiz.de/10013273036

Testing for a single-factor stochastic volatility in bivariate series

Chiba, Masaru; Kobayashi, Masahito - In: Journal of risk and financial management : JRFM 6 (2013) 1, pp. 31-61

This paper proposes the Lagrange multiplier test for the null hypothesis thatthe bivariate time series has only a single common stochastic volatility factor and noidiosyncratic volatility factor. The test statistic is derived by representing the model in alinear state-space form under the...

Persistent link: https://www.econbiz.de/10011555751

Estimating and forecasting generalized fractional long memory stochastic volatility models

Peiris, Shelton; Asai, Manabu; McAleer, Michael - In: Journal of risk and financial management : JRFM 10 (2017) 4, pp. 1-16

This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the corresponding statistical properties of this model,...

Persistent link: https://www.econbiz.de/10011854876

Recovering historical inflation data from postage stamps prices

Franses, Philip Hans; Janssens, Eva - In: Journal of risk and financial management : JRFM 10 (2017) 4, pp. 1-11

last no longer than 2 or 3 years. An Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model …

Persistent link: https://www.econbiz.de/10011854870

Asymmetry and leverage with news impact curve perspective in Australian stock returns' volatility during COVID-19

Iqbal, Najam; Manzoor, Muhammad Saqib; Bhatti, Muhammad … - In: Journal of risk and financial management : JRFM 14 (2021) 7, pp. 1-15

(EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on … 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage … that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative …

Persistent link: https://www.econbiz.de/10012622818

Is bitcoin a safe haven for Indian investors? : a GARCH volatility analysis

Murty, Sarika; Victor, Vijay; Fekete-Farkas, Maria - In: Journal of risk and financial management : JRFM 15 (2022) 7, pp. 1-13

the returns of Bitcoin. An asymmetric GARCH model (EGARCH) is used to investigate whether Bitcoin may be useful in risk …

Persistent link: https://www.econbiz.de/10013370988

COVID-19 pandemic & financial market volatility : evidence from GARCH models

Khan, Maaz; Kayani, Umar Nawaz; Khan, Mrestyal; Mughal, … - In: Journal of risk and financial management : JRFM 16 (2023) 1, pp. 1-20

-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series … positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate …

Persistent link: https://www.econbiz.de/10014289566

The effect of COVID-19 on cryptocurrencies and the stock market volatility : a two-stage DCC-EGARCH model analysis

Ampountolas, Apostolos - In: Journal of risk and financial management : JRFM 16 (2023) 1, pp. 1-17

exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the …-stage multivariate EGARCH model's results show that the conditional volatilities of both asset portfolios surge more after positive news …

Persistent link: https://www.econbiz.de/10014295230