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Modeling autoregressive processes with moving-quantiles-implied nonlinearity

Ishida, Isao; Kvedaras, Virmantas - In: Econometrics : open access journal 3 (2015) 1, pp. 2-54

We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor's 500...

Persistent link: https://www.econbiz.de/10010478989

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Duong, Diep; Swanson, Norman R. - 2013

Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility...

Persistent link: https://www.econbiz.de/10009771770

Nonparametric Estimation and Parametric Calibration of Time-Varying Coefficient Realized Volatility Models

Chen, Xiangjin Bruce - 2013

This paper introduces a new specification for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coefficients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for...

Persistent link: https://www.econbiz.de/10013076694

Jump variation estimation with noisy high frequency financial data via wavelets

Zhang, Xin; Kim, Donggyu; Wang, Yazhen - In: Econometrics : open access journal 4 (2016) 3, pp. 1-26

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence … of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component …-step procedure with detection and estimation. In Step 1, we detect the jump locations by performing wavelet transformation on the …

Persistent link: https://www.econbiz.de/10011568279

Long run returns predictability and volatility with moving averages

Chang, Chia-Lin; Ilomäki, Jukka; Laurila, Hannu; … - In: Risks : open access journal 6 (2018) 4, pp. 1-18

This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The...

Persistent link: https://www.econbiz.de/10011906234

Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends

McCloskey, Adam - 2012 - This version: October 2012

-memory. -- stochastic volatility ; frequency domain estimation ; robust estimation ; spurious persistence ; long-memory ; level shifts …

Persistent link: https://www.econbiz.de/10009660446

Stationarity and ergodicity of univariate generalized autoregressive score processes

Blasques, Francisco; Koopman, Siem Jan; Lucas, André - 2012

Persistent link: https://www.econbiz.de/10009722625

A one line derivation of DCC : application of a vector random coefficient moving average process

Hafner, Christian M.; McAleer, Michael - 2014 - Revised: July 2014

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the...

Persistent link: https://www.econbiz.de/10010374571

The dynamic Skellam model with applications

Koopman, Siem Jan; Lit, Rutger; Lucas, André - 2014

We introduce a dynamic statistical model for Skellam distributed random variables. The Skellam distribution can be obtained by taking differences between two Poisson distributed random variables. We treat cases where observations are measured over time and where possible serial correlation is...

Persistent link: https://www.econbiz.de/10010253460

Asymmetry and leverage in conditional volatility models

McAleer, Michael - 2014

The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or...

Persistent link: https://www.econbiz.de/10010405194

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