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Data-driven jump detection thresholds for application in jump regressions
Davies, Robert; Tauchen, George Eugene - 2015
jumps over a grid of thresholds and selects the optimal threshold at what we term the “take-off” point in the estimated … number of jumps. We show that this method consistently estimates the jumps and their indices as the sampling interval goes to … jumps and its ability to distinguish between true jumps and large diffusive moves. In one of these Monte Carlo studies we …
Persistent link: https://www.econbiz.de/10011524214
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Data-driven jump detection thresholds for application in jump regressions
Davies, Robert; Tauchen, George Eugene - In: Econometrics : open access journal 6 (2018) 2, pp. 1-25
jumps over a grid of thresholds and selects the optimal threshold at what we term the ‘take-off’ point in the estimated … number of jumps. We show that this method consistently estimates the jumps and their indices as the sampling interval goes to … jumps and its ability to distinguish between true jumps and large diffusive moves. In one of these Monte Carlo studies we …
Persistent link: https://www.econbiz.de/10011823308
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Mixed-scale jump regressions with bootstrap inference
Li, Jia; Todorov, Viktor; Tauchen, George Eugene; Chen, Rui - In: Journal of econometrics 201 (2017) 2, pp. 417-432
Persistent link: https://www.econbiz.de/10011920538
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Jump regressions
Li, Jia; Todorov, Viktor; Tauchen, George Eugene - In: Econometrica : journal of the Econometric Society, an … 85 (2017) 1, pp. 173-195
Persistent link: https://www.econbiz.de/10011738476
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Portfolio Construction When Regimes are Ambiguous
Kritzman, Mark; Kulasekaran, Cel; Turkington, David - 2023
Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...
Persistent link: https://www.econbiz.de/10014348956
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A penalized two-pass regression to predict stock returns with time-varying risk premia
Bakalli, Gaetan; Guerrier, Stéphane; Scaillet, Olivier - 2021
We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no arbitrage restrictions by regularizing appropriate groups of coefficients. The second...
Persistent link: https://www.econbiz.de/10012487589
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GARCH Models for Daily Stock Returns : Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts
Ardia, David - 2020
We analyze the impact of the estimation frequency - updating parameter estimates on a daily, weekly, monthly or quarterly basis - for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily returns for constituents of the S&P 500 index. We assess the...
Persistent link: https://www.econbiz.de/10012857089
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Properties of Range-Based Volatility Estimators
Molnár, Peter - 2015
Volatility is not directly observable and must be estimated. Estimator based on daily close data is imprecise. Range-based volatility estimators provide significantly more precision, but still remain noisy volatility estimates, something that is sometimes forgotten when these estimators are used...
Persistent link: https://www.econbiz.de/10013011883
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Efficiently Weighted Estimation of Tail and Interquantile Expectations
Barendse, Sander - 2020
Tail expectations have recently attracted much attention in economics for their ability to capture risk. We develop a semiparametric estimator for the joint estimation of (nonlinear) models of tail expectations with some tail quantile as left or right threshold, and interquantile expectations,...
Persistent link: https://www.econbiz.de/10012854515
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On Maximum Likelihood Estimates for the Shape Parameter of the Generalized Pareto Distribution
Kouider, Mohammed Ridha - 2019
The general Pareto distribution (GPD) has been widely used a lot in the extreme value for example to model exceedance over a threshold. Feature of The GPD that when applied to real data sets depends substantially and clearly on the parameter estimation process. Mostly the estimation is preferred...
Persistent link: https://www.econbiz.de/10012860148