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Working Paper
Regular Variation of Popular GARCH Processes Allowing for Distributional Asymmetry
Prono, Todd (2017-09-22)
Linear GARCH(1,1) and threshold GARCH(1,1) processes are established as regularly varying, meaning their heavy tails are Pareto like, under conditions that allow the innovations from the, respective, processes to be skewed. Skewness is considered a stylized fact for many financial returns assumed to follow GARCH-type processes. The result in this note aids in establishing the asymptotic properties of certain GARCH estimators proposed in the literature.
Finance and Economics Discussion Series , Paper 2017-095

Working Paper
Better the Devil You Know: Improved Forecasts from Imperfect Models
Oh, Dong Hwan; Patton, Andrew J. (2021-11-05)
Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- specied model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecication of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we nd signicant fore- cast improvements from ...
Finance and Economics Discussion Series , Paper 2021-071

Working Paper
Dynamic Factor Copula Models with Estimated Cluster Assignments
Oh, Dong Hwan; Patton, Andrew J. (2022-05-06)
This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model ...
Finance and Economics Discussion Series , Paper 2021-029r1

Working Paper
Interconnectedness in the Corporate Bond Market
Brunetti, Celso; Carl, Matthew; Gerszten, Jacob; Scotti, Chiara; Shin, Chaehee (2024-08-16)
Does interconnectedness improve market quality? Yes.We develop an alternative network structure, the assets network: assets are connected if they are held by the same investors. We use several large datasets to build the assets network for the corporate bond market. Through careful identification strategies based on the COVID-19 shock and “fallen angels,” we find that interconnectedness improves market quality especially during stress periods. Our findings contribute to the debate on the role of interconnectedness in financial markets and show that highly interconnected corporate bonds ...
Finance and Economics Discussion Series , Paper 2024-066

Report
Liquidity and volatility in the U.S. treasury market
Fleming, Michael J.; Nguyen, Giang; Engle, Robert; Ghysels, Eric (2012-12-01)
We model the joint dynamics of intraday liquidity, volume, and volatility in the U.S. Treasury market, especially through the 2007-09 financial crisis and around important economic announcements. Using various specifications based on Bauwens and Giot?s (2000) Log- ACD(1,1) model, we find that liquidity, volume, and volatility are highly persistent, with volatility having a lower short-term persistence than the other two. Market liquidity and volume are important to explaining volatility dynamics but not vice versa. In addition, market dynamics change during the financial crisis, with all ...
Staff Reports , Paper 590

Report
Deconstructing the yield curve
Gospodinov, Nikolay; Crump, Richard K. (2019-04-01)
We introduce a novel nonparametric bootstrap for the yield curve which is agnostic to the true factor structure of interest rates. We deconstruct the yield curve into primitive objects, with weak cross-sectional and time-series dependence, that serve as building blocks for resampling the data. We analyze the properties of the bootstrap for mimicking salient features of the data and conducting valid inference. We demonstrate the benefits of our general method by revisiting the predictability of bond returns based on slow-moving fundamentals. We find that trend inflation, but not the ...
Staff Reports , Paper 884

Working Paper
High-Dimensional Copula-Based Distributions with Mixed Frequency Data
Patton, Andrew J.; Oh, Dong Hwan (2015-05-19)
This paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence, and a new class of copulas designed to capture nonlinear dependence among the resulting uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted using composite likelihood, facilitating applications involving hundreds of variables. In- and out-of-sample tests confirm ...
Finance and Economics Discussion Series , Paper 2015-50

Working Paper
Systemic Tail Risk: High-Frequency Measurement, Evidence and Implications
Erdemlioglu, Deniz; Neely, Christopher J.; Yang, Xiye (2023-07-20)
We develop a new framework to measure market-wide (systemic) tail risk in the cross-section of high-frequency stock returns. We estimate the time-varying jump intensities of asset prices and introduce a testing approach that identifies multi-asset tail risk based on the release times of scheduled news announcements. Using high-frequency data on individual U.S. stocks and sector-specific ETF portfolios, we find that most of the FOMC announcements create systemic left tail risk, but there is no evidence that macro announcements do so. The magnitude of the tail risk induced by Fed news varies ...
Working Papers , Paper 2023-016

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