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Maximum entropy evaluation of asymptotic hedging error under a generalised jump-diffusion model

Fard, Farzad Alavi; Doko Tchatoka, Firmin; … - In: Journal of risk and financial management : JRFM 14 (2021) 3/97, pp. 1-19

In this paper we propose a maximum entropy estimator for the asymptotic distribution of the hedging error for options. Perfect replication of financial derivatives is not possible, due to market incompleteness and discrete-time hedging. We derive the asymptotic hedging error for options under a...

Persistent link: https://www.econbiz.de/10012484861

Modeling exchange rate volatility : application of GARCH models with a Normal Tempered Stable distribution

Charfi, Sahar; Mselmi, Farouk - In: Quantitative finance and economics 6 (2022) 2, pp. 206-222

Persistent link: https://www.econbiz.de/10013498930

Asymptotics of realized volatility with non-Gaussian ARCH(∞) Microstructure noise

Taniai, Hiroyuki; Usami, Takashi; Suto, Nobuyuki; … - In: Journal of financial econometrics : official journal of … 10 (2012) 4, pp. 617-636

Persistent link: https://www.econbiz.de/10009671895

Forecasting value-at-risk with time-varying variance, skewness and kurtosis in an exponential weighted moving average framework

Gabrielsen, Alexandros; Kirchner, Axel; Liu, Zhuoshi; … - In: Annals of financial economics 10 (2015) 1, pp. 1-29

Persistent link: https://www.econbiz.de/10011382540

Forecasting value-at-risk with time-varying variance, skewnessn and kurtosis in an exponential weighted moving average framework

Gabrielsen, Alexandros; Zagaglia, Paolo; Kirchner, Axel; … - 2012 - This version: June 6, 2012

by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over …

Persistent link: https://www.econbiz.de/10011731521

Realized volatility forecasting based on dynamic quantile model averaging

Cai, Zongwu; Ma, Chaoqun; Mi, Xianhua - 2020

Persistent link: https://www.econbiz.de/10012312856

Data cloning estimation for asymmetric stochastic volatility models

Bermudez, P. de Zea; Marín, J. Miguel; Veiga, Helena - In: Econometric reviews 39 (2020) 10, pp. 1057-1074

Persistent link: https://www.econbiz.de/10012406209

A note on stochastic volatility model estimation

Abbara, Omar; Zevallos, Mauricio - In: Revista Brasileira de Finanças : RBFin 17 (2019) 4, pp. 22-32

Persistent link: https://www.econbiz.de/10012221531

Dimension reduction via penalized GLMs for non-Gaussian response : application to stock market volatility

Li, Tao; Desmond, Anthony F.; Stengos, Thanasēs - In: Journal of risk and financial management : JRFM 14 (2021) 12, pp. 1-26

financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG … indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate … us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10 …

Persistent link: https://www.econbiz.de/10012798738

An experiment on autoregressive and threshold autoregressive models with non-gaussian error with application to realized volatility

Zhang, Ziyi; Li, Wai Keung - In: Economies : open access journal 7 (2019) 2/58, pp. 1-11

structure. This is motivated by the problem of finding a possible probabilistic model for the realized volatility. A Gamma … random error is proposed to cater for the non-negativity of the realized volatility. With many good properties, such as … on empirical realized volatility data of 30 stocks, where one third of the cases are fitted quite well, suggesting that …

Persistent link: https://www.econbiz.de/10012021585