Is a pure jump process fitting the high frequency data better than a jump-diffusion process?

2013 ◽  
Vol 143 (2) ◽  
pp. 315-320 ◽  
Author(s):  
Xin-Bing Kong
Keyword(s):  
Diffusion Process ◽  
High Frequency ◽  
Jump Process ◽  
Jump Diffusion ◽  
High Frequency Data ◽  
Frequency Data ◽  
Jump Diffusion Process ◽  
Better Than

scholarly journals Adaptive nonparametric drift estimation of an integrated jump diffusion process

2018 ◽  
Vol 22 ◽  
pp. 236-260 ◽  
Author(s):  
Benedikt Funke ◽  
Émeline Schmisser
Keyword(s):  
Diffusion Process ◽  
High Frequency ◽  
Jump Diffusion ◽  
High Frequency Data ◽  
Compact Set ◽  
Penalized Least Squares ◽  
Adaptive Estimator ◽  
Drift Estimation ◽  
Jump Diffusion Process ◽  
Jump Diffusion Model

In the present article, we investigate nonparametric estimation of the unknown drift function b in an integrated Lévy driven jump diffusion model. Our aim will be to estimate the drift on a compact set based on a high-frequency data sample. Instead of observing the jump diffusion process V itself, we observe a discrete and high-frequent sample of the integrated process Xt := ∫0t Vsds Based on the available observations of Xt, we will construct an adaptive penalized least-squares estimate in order to compute an adaptive estimator of the corresponding drift function b. Under appropriate assumptions, we will bound the L2-risk of our proposed estimator. Moreover, we study the behavior of the proposed estimator in various Monte Carlo simulation setups.

NONPARAMETRIC ESTIMATION FOR SECOND-ORDER JUMP-DIFFUSION MODEL IN HIGH FREQUENCY DATA

2017 ◽  
Vol 65 (04) ◽  
pp. 1033-1063 ◽  
Author(s):  
YUPING SONG
Keyword(s):  
High Frequency ◽  
Jump Diffusion ◽  
Small Sample ◽  
Second Order ◽  
Stock Index ◽  
High Frequency Data ◽  
Monte Carlo Experiment ◽  
Frequency Data ◽  
Sample Paths ◽  
Jump Diffusion Model

We provide the nonparametric estimators of the infinitesimal coefficients of the second-order continuous-time models with discontinuous sample paths of jump-diffusion models. Under the mild conditions, we obtain the weak consistency and the asymptotic normality of the estimators. A Monte Carlo experiment demonstrates the better small-sample performance of these estimators. In addition, the estimators are illustrated empirically through stock index of Shanghai Stock Exchange in high frequency data.

scholarly journals Retornos Anormais no Ibovespa Utilizando Modelos para Dados de Alta Frequência

2012 ◽  
Vol 10 (2) ◽  
pp. 243
Author(s):  
Nelson Ferreira Fonseca ◽  
Wagner Moura Lamounier ◽  
Aureliano Angel Bressan
Keyword(s):  
High Frequency ◽  
Historical Data ◽  
Abnormal Returns ◽  
Equity Markets ◽  
Trading Strategies ◽  
High Frequency Data ◽  
Var Model ◽  
Frequency Data ◽  
Forecasting Models ◽  
Better Than

This article aims to identify profitable trading strategies based on the effects of leads and lags between the spot and futures equity markets in Brazil, using high frequency data. To achieve this objective and based on historical data of the Bovespa and the Bovespa Future indexes, four forecasting models have been built: ARIMA, ARFIMA, VAR, and VECM. The trading strategies tested were: net trading strategy, buy and hold strategy, and filter strategy – better than average predicted return. The period of analysis of this paper extends from August 1, 2006 to October 16, 2009. In this work, it was possible to obtain abnormal returns using trading strategies with the VAR model on the effects of leads and lags between the Bovespa index and Bovespa Future index.

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