676 research outputs found
Econometric Analysis of Realized Volatility and Its Use in Estimating Stochastic Volatility Models [Non-Gaussian Ornstein-Uhlenbeck-Based Models and Some of Their Uses in Financial Economics (with Discussion)].
No full textStochastic Volatility: Selected Readings
No full textCollects sixteen of the main papers that have influenced the econometrics of stochastic volatility, which is associated with financial economics and mathematical finance. Papers discuss a subordinated stochastic process model with finite variance for speculative prices; a study of daily sugar prices, 1961-79; the behavior of random variables with nonstationary variance and the distribution of security prices; the pricing of options on assets with stochastic volatilities; the dynamics of exchange rate volatility; multivariate stochastic variance models; stochastic autoregressive volatility; long memory in continuous-time stochastic volatility models; Bayesian analysis of stochastic volatility models; stochastic volatility, likelihood inference, and a comparison with ARCH models; estimation of stochastic volatility models with diagnostics; pricing foreign currency options with stochastic volatility; a closed-form solution for options with stochastic volatility, with applications to bond and currency options; a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation; the distribution of realized exchange rate volatility; and econometric analysis of realized volatility and its use in estimating stochastic volatility models. Neil Shephard is Professor of Economics and Official Fellow in Economics at Nuffield College, University of Oxford, on the Editorial Board of the Review of Economic Studies, and Associate Editor of Econometrica. Author and subject indexes
Stochastic Volatility: Selected Readings
No full textCollects sixteen of the main papers that have influenced the econometrics of stochastic volatility, which is associated with financial economics and mathematical finance. Papers discuss a subordinated stochastic process model with finite variance for speculative prices; a study of daily sugar prices, 1961-79; the behavior of random variables with nonstationary variance and the distribution of security prices; the pricing of options on assets with stochastic volatilities; the dynamics of exchange rate volatility; multivariate stochastic variance models; stochastic autoregressive volatility; long memory in continuous-time stochastic volatility models; Bayesian analysis of stochastic volatility models; stochastic volatility, likelihood inference, and a comparison with ARCH models; estimation of stochastic volatility models with diagnostics; pricing foreign currency options with stochastic volatility; a closed-form solution for options with stochastic volatility, with applications to bond and currency options; a unified approach to the joint estimation of objective and risk neutral measures for the purpose of options valuation; the distribution of realized exchange rate volatility; and econometric analysis of realized volatility and its use in estimating stochastic volatility models. Neil Shephard is Professor of Economics and Official Fellow in Economics at Nuffield College, University of Oxford, on the Editorial Board of the Review of Economic Studies, and Associate Editor of Econometrica. Author and subject indexes
ToruNiina/libasd: version 1.5.6
No full textsupport sdist (by Neil Shephard @ns-rse) #8, #11, #12
support python 3.11
update pybind11 to v2.10.
Comment on Garland B. Durham and A. Ronald Gallant's "Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes"
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Essays on Identification and Causality
No full textThis dissertation contains three chapters in econometrics. A common theme is identification analysis, with a particular focus on understanding what researchers can learn from data under weak assumptions on economic behavior and dynamic causal effects.
The first chapter characterizes the behavioral and econometric assumptions under which researchers can identify whether expert decision makers, such as doctors, judges, and managers, make systematic prediction mistakes in observational empirical settings like medical testing, pretrial release, and hiring. Under these assumptions, I provide a statistical test for whether the decision maker makes systematic prediction mistakes and methods for conducting inference on the ways in which the decision maker's predictions are systematically biased. As an empirical illustration, I analyze the pretrial release decisions of judges in New York City.
The second chapter, which is coauthored with Neil Shephard, develops the direct potential outcome system as a foundational framework for analyzing dynamic causal effects in observational time series settings. We place no functional form restrictions on the potential outcome process nor restrictions on the extent to which past assignments may causally affect outcomes. We provide novel conditions under which popular time series estimands, such as the impulse response function, local projections, and local projection with an instrumental variable, have nonparametric causal interpretations in terms of dynamic causal effects.
The third chapter, which is coauthored with Iavor Bojinov and Neil Shephard, proposes a rich class of finite population dynamic causal effects in panel experiments. We provide a nonparametric estimator that is unbiased for these dynamic causal effects over the randomization distribution of assignments, derive its finite population limiting distribution, and develop two methods for conducting inference. We further show that population linear fixed effect estimators do not recover causally interpretable estimands if there are dynamic causal effects and serial correlation in the assignment mechanism of the panel experiment
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