C58 - Financial Econometrics
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Generalized Autoregressive Gamma Processes
We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does. -
Covariates Hiding in the Tails
We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data. -
Strategic Uncertainty in Financial Markets: Evidence from a Consensus Pricing Service
We look at the informational content of consensus pricing in opaque over-the-counter markets. We show that the availability of price data informs participants mainly about other participants’ valuations, rather than about the value of a financial security. -
On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity
Banks’ business interactions create a network of relationships that are hidden in the correlations of bank stock returns. But for policy interventions, we need causality to understand how the network changes. Thus, this paper looks for the causal network anticipated by investors. -
Tail Index Estimation: Quantile-Driven Threshold Selection
The most extreme events, such as economic crises, are rare but often have a great impact. It is difficult to precisely determine the likelihood of such events because the sample is small. -
Composite Likelihood Estimation of an Autoregressive Panel Probit Model with Random Effects
Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals. -
Challenges in Implementing Worst-Case Analysis
Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach. -
Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement
We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms. -
A Calibrated Model of Intraday Settlement
This paper estimates potential exposures, netting benefits and settlement gains by merging retail and wholesale payments into batches and conducting multiple intraday settlements in this hypothetical model of a single "calibrated payments system." The results demonstrate that credit risk exposures faced by participants in the system are largely dependent on their relative activity in the retail and wholesale payments systems.