C14 - Semiparametric and Nonparametric Methods: General
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Identification and Estimation of Risk Aversion in First-Price Auctions with Unobserved Auction Heterogeneity
This paper shows point identification in first-price auction models with risk aversion and unobserved auction heterogeneity by exploiting multiple bids from each auction and variation in the number of bidders. The required exclusion restriction is shown to be consistent with a large class of entry models. -
Estimating Systematic Risk Under Extremely Adverse Market Conditions
This paper considers the problem of estimating a linear model between two heavy-tailed variables if the explanatory variable has an extremely low (or high) value. We propose an estimator for the model coefficient by exploiting the tail dependence between the two variables and prove its asymptotic properties. -
Early Warning of Financial Stress Events: A Credit-Regime-Switching Approach
We propose an early warning model for predicting the likelihood of a financial stress event for a given future time, and examine whether credit plays an important role in the model as a non-linear propagator of shocks. -
Testing for the Diffusion Matrix in a Continuous-Time Markov Process Model with Applications to the Term Structure of Interest Rates
The author proposes a test for the parametric specification of each component in the diffusion matrix of a d-dimensional diffusion process. Overall, d (d-1)/2 test statistics are constructed for the off-diagonal components, while d test statistics are constructed for the main diagonal components. -
Bootstrap Tests of Mean-Variance Efficiency with Multiple Portfolio Groupings
We propose double bootstrap methods to test the mean-variance efficiency hypothesis when multiple portfolio groupings of the test assets are considered jointly rather than individually. -
Predicting Financial Stress Events: A Signal Extraction Approach
The objective of this paper is to propose an early warning system that can predict the likelihood of the occurrence of financial stress events within a given period of time. To achieve this goal, the signal extraction approach proposed by Kaminsky, Lizondo and Reinhart (1998) is used to monitor the evolution of a number of economic indicators that tend to exhibit an unusual behaviour in the periods preceding a financial stress event. -
Sheep in Wolf’s Clothing: Using the Least Squares Criterion for Quantile Estimation
Estimation of the quantile model, especially with a large data set, can be computationally burdensome. This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model. -
A Distributional Approach to Realized Volatility
This paper proposes new measures of the integrated variance, measures which use high-frequency bid-ask spreads and quoted depths. The traditional approach assumes that the mid-quote is a good measure of frictionless price. -
Volatility Forecasting when the Noise Variance Is Time-Varying
This paper explores the volatility forecasting implications of a model in which the friction in high-frequency prices is related to the true underlying volatility. The contribution of this paper is to propose a framework under which the realized variance may improve volatility forecasting if the noise variance is related to the true return volatility. -
Volatility and Liquidity Costs
Observed high-frequency prices are contaminated with liquidity costs or market microstructure noise. Using such data, we derive a new asset return variance estimator inspired by the market microstructure literature to explicitly model the noise and remove it from observed returns before estimating their variance.