LATENT VARIABLE MODELS: UNLOCKING INSIGHTS IN FINANCIAL RANGE DATA

Authors

  • Giuseppe P. Marino University of Salerno. Address: Dipartimento di Scienze Economiche e Statistiche (DISES), Universita’ degli Studi di Salerno, Via Giovanni Paolo II, 84084, Fisciano (SA), Italy

Keywords:

Volatility, range, importance sampling, indirect inference.

Abstract

In this paper we introduce a latent variable based model for the dynamics of financial range, the stochastic conditional range (SCR). We propose to estimate its parameters by Kalman filter, indirect inference and simulated maximum likelihood depending on the hypotheses on the distributional form of the innovations. The model is applied to a large subset of the S&P 500 components. A comparison of its fitting and forecasting abilities with the conditional autoregressive range (CARR) model shows that the new approach can provide a competitive alternative

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Published

2024-06-21

Issue

Vol. 10 No. 2 (2022)

Section

Articles