Estimation and Inference for Higher‐Order Stochastic Volatility Models With Leverage

Md. Nazmul Ahsan, Jean‐Marie Dufour and Gabriel Rodriguez‐Rondon

Journal of Time Series Analysis, 2025, vol. 46, issue 6, 1064-1084

Abstract: Statistical inference—estimation and testing—for stochastic volatility models is challenging and computationally expensive. This problem is compounded when leverage effects are allowed. We propose efficient, simple estimators for higher‐order stochastic volatility models with leverage [SVL(p)$$ (p) $$], based on a small number of moment equations derived from ARMA representations associated with SVL models, along with the possibility of using “winsorization” to improve stability and efficiency (W‐ARMA estimators). The asymptotic distributional theory of the estimators is derived. The computationally simple estimators proposed allow one to easily perform simulation‐based (possibly exact) tests, such as Monte Carlo tests (MCTS) or bootstrap procedures. In simulation experiments, we show that: (1) the proposed W‐ARMA estimators dominate alternative estimators (including Bayesian estimators) in terms of bias, root‐mean‐square error, and computation time; (2) local and maximized Monte Carlo tests based on W‐ARMA estimators yield good control of the size and power of LR‐type tests; (3) taking into account leverage improves volatility forecasting. The methods developed are applied to daily returns for three major stock indices (S&P 500, Dow Jones, Nasdaq), confirming the superiority of SVL(p)$$ (p) $$ models over competing conditional volatility models in terms of forecast accuracy.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/jtsa.12851

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:46:y:2025:i:6:p:1064-1084

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0143-9782

Access Statistics for this article

Journal of Time Series Analysis is currently edited by M.B. Priestley

More articles in Journal of Time Series Analysis from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().