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Full-Text Articles in Economics
Volatility Puzzle: Long Memory Or Anti-Persistency, Shuping Shi, Jun Yu
Volatility Puzzle: Long Memory Or Anti-Persistency, Shuping Shi, Jun Yu
Research Collection School Of Economics
The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1,d,01,d,0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d α close to 0 and d close to 0.5) from Model 2Model 2 (ARFIMA(1,d,01,d,0) with α close to unity and d close to –0.5). An intuitive explanation is given. For the 10 financial assets considered, despite that no definitive conclusions …
Asymptotic Theory For Explosive Fractional Ornstein–Uhlenbeck Processes, Hui Jiang, Yajuan Pan, Weilin Liao, Qingshan Yang, Jun Yu
Asymptotic Theory For Explosive Fractional Ornstein–Uhlenbeck Processes, Hui Jiang, Yajuan Pan, Weilin Liao, Qingshan Yang, Jun Yu
Research Collection School Of Economics
This paper proposes estimators for the parameters of an explosive fractional Ornstein-Uhlenbeck process. The asymptotic properties for the diffusion estimators are developed under the in-fill asymptotic scheme, while the asymptotic properties for the drift estimators are developed under the double asymptotic scheme for the full range of the Hurst parameter. Simulation results demonstrate the effectiveness of the proposed estimators, and the asymptotic distributions provide a good approximation in finite samples. Empirical applications are presented to demonstrate the model’s usefulness and the practical value of the asymptotic theory.
Modeling And Forecasting Realized Volatility With The Fractional Ornstein-Uhlenbeck Process, Xiaohu Wang, Weilin Xiao, Jun Yu
Modeling And Forecasting Realized Volatility With The Fractional Ornstein-Uhlenbeck Process, Xiaohu Wang, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This paper proposes to model and forecast realized volatility (RV) using the fractional Ornstein-Uhlenbeck (fO-U) process with a general Hurst parameter, H. A two-stage method is introduced for estimating parameters in the fO-U process based on discrete-sampled observations. In the first stage, H is estimated based on the ratio of two second-order differences of observations from different frequencies. In the second stage, with the estimated , the other parameters of the model are estimated by the method of moments. All estimators have closed-form expressions and are easy to implement. A large sample theory of the proposed estimators is derived. Extensive …
Volatility Puzzle: Long Memory Or Antipersistency, Shuping Shi, Jun Yu
Volatility Puzzle: Long Memory Or Antipersistency, Shuping Shi, Jun Yu
Research Collection School Of Economics
The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1, d, 0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d