Thomas H McCurdy
Biographic Data
| ID | 8880804 |
|---|---|
| NAME | Thomas H McCurdy |
| GIVEN NAMES | Thomas H |
| FAMILY NAME | McCurdy |
| SIGNATURE | MCCURDY T H |
| AFFILIATIONS | University of Toronto |
| ORCID | 0000-0001-7303-8614 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 1994 |
| LATEST PUBLICATION YEAR | 2009 |
| H-INDEX | 0 |
How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution
We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The empirical results strongly reject igno…
Nonlinear Features of Realized FX Volatility
This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to estimate ex post latent volatility imply that standard time series models of the conditional variance become variants of an ARMAX model. We explore nonlinear departures from these linear specifications using a doubly stochastic process…
Identifying Bull and Bear Markets in Stock Returns
This article uses a Markov-switching model that incorporates duration dependence to capture nonlinear structure in both the conditional mean and the conditional variance of stock returns. The model sorts returns into a high-return stable state and a low-return volatile state. We label these as bull and bear markets, respectively. The filter identifies all major stock-market downturns in over 160 years of monthly data. Bull markets have a declinin…
Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth
Hamilton's nonlinear Markovian filter is extended to allow state transitions to be duration dependent. Restrictions are imposed on the state transition matrix associated with a τ-order Markov system such that the corresponding first-order conditional transition probabilities are functions of both the inferred current state and also the number of periods the process has been in that state. High-order structure is parsimoniously summarized by the i…
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Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth
Hamilton's nonlinear Markovian filter is extended to allow state transitions to be duration dependent. Restrictions are imposed on the state transition matrix associated with a τ-order Markov system such that the corresponding first-order conditional transition probabilities are functions of both the inferred current state and also the number of periods the process has been in that state. High-order structure is parsimoniously summarized by the i…
Identifying Bull and Bear Markets in Stock Returns
This article uses a Markov-switching model that incorporates duration dependence to capture nonlinear structure in both the conditional mean and the conditional variance of stock returns. The model sorts returns into a high-return stable state and a low-return volatile state. We label these as bull and bear markets, respectively. The filter identifies all major stock-market downturns in over 160 years of monthly data. Bull markets have a declinin…
Nonlinear Features of Realized FX Volatility
This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to estimate ex post latent volatility imply that standard time series models of the conditional variance become variants of an ARMAX model. We explore nonlinear departures from these linear specifications using a doubly stochastic process…
How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution
We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The empirical results strongly reject igno…
Econometrics (4 works) · Economics (4 works) · Mathematics (3 works) · Complex Systems and Time Series Analysis (2 works) · Financial economics (2 works) · Financial Markets and Investment Strategies (2 works) · Financial Risk and Volatility Modeling (2 works) · Monetary Policy and Economic Impact (2 works) · Nonlinear system (2 works) · Physics (2 works)