Open Access. Powered by Scholars. Published by Universities.®
Finance and Financial Management Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Multiple bubbles (2)
- Bubble duration (1)
- Consistency (1)
- Date-stamping strategy (1)
- Dating algorithm (1)
-
- Excess volatility (1)
- Financial-markets (1)
- Finite (1)
- Flexible window (1)
- Generalized sup ADF test (1)
- Levy processes (1)
- Limit theory (1)
- Periodically collapsing bubbles (1)
- Predictability (1)
- Rational bubble (1)
- Real time detector (1)
- Returns (1)
- Risk (1)
- Simulation (1)
- Stock-prices (1)
- Sup ADF test (1)
Articles 1 - 3 of 3
Full-Text Articles in Finance and Financial Management
Testing For Multiple Bubbles: Limit Theory Of Real-Time Detectors, Peter C. B. Phillips, Shuping Shi, Jun Yu
Testing For Multiple Bubbles: Limit Theory Of Real-Time Detectors, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
This article provides the limit theory of real-time dating algorithms for bubble detection that were suggested in Phillips, Wu, and Yu (PWY; International Economic Review 52 [2011], 201-26) and in a companion paper by the present authors (Phillips, Shi, and Yu, 2015; PSY; International Economic Review 56 [2015a], 1099-1134. Bubbles are modeled using mildly explosive bubble episodes that are embedded within longer periods where the data evolve as a stochastic trend, thereby capturing normal market behavior as well as exuberance and collapse. Both the PWY and PSY estimates rely on recursive right-tailed unit root tests (each with a different recursive …
Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shuping Shi, Jun Yu
Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
Recent work on econometric detection mechanisms has shown the effectiveness of recursive procedures in identifying and dating financial bubbles in real time. These procedures are useful as warning alerts in surveillance strategies conducted by central banks and fiscal regulators with real-time data. Use of these methods over long historical periods presents a more serious econometric challenge due to the complexity of the nonlinear structure and break mechanisms that are inherent in multiple-bubble phenomena within the same sample period. To meet this challenge, this article develops a new recursive flexible window method that is better suited for practical implementation with long …
Self-Exciting Jumps, Learning, And Asset Pricing Implications, Andras Fulop, Junye Li, Jun Yu
Self-Exciting Jumps, Learning, And Asset Pricing Implications, Andras Fulop, Junye Li, Jun Yu
Research Collection School Of Economics
The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real-time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. We also find that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting, and option pricing.