Dong Hwan Oh

Senior Economist 

Quantitative Risk Analysis Section 

Division of Reserve Bank Operations and Payment Systems 

Board of Governors of the Federal Reserve System

Email: donghwan.oh@frb.gov

Phone: (202) 973-7334

(CV), (Google Scholar)


WORKING PAPERS


PUBLICATION 


ABSTRACT


Default Clustering Risk Premium and its Cross-Market Asset Pricing Implications, with Kiwoong Byun and Baeho Kim, 2023.


This study examines the market-implied premiums for bearing default clustering risk by analyzing credit derivatives contracts on the CDX North American Investment Grade (CDX.NA.IG) portfolio between September 2005 and March 2021. Our approach involves constructing a time series of reference tranche rates exclusively derived by single-name CDS spreads. The default clustering risk premium (DCRP) is captured by comparing the original and reference tranche spreads, with the former exceeding the latter when investors require greater compensation for correlated defaults at the portfolio level. The fitted DCRP level significantly increased in response to the 2007-9 global financial crisis and remained relatively stable for a period, followed by a gradual decline beginning in 2016. Notably, the COVID-19 shock caused another sharp rise in the DCRP level. Our empirical analysis finds that the estimated DCRP has significant implications for asset pricing, particularly in affecting the investment opportunities available to U.S. stock investors during times of instability in the financial system. 


Better the Devil You Know: Improved Forecasts from Imperfect Models, with Andrew J. Patton, 2024, Journal of Econometrics, forthcoming. 


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- specified 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 misspecification of the model. We theoretically consider the forecast environments in which our approach is likely to offer improvements over standard methods, and we find significant forecast improvements from applying the proposed method across four distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting. 


GARCH Option Pricing with Volatility Derivatives, with Yang-Ho Park, 2023, Journal of Banking and Finance, 146.

This paper studies benefits of joint estimations for GARCH option pricing that utilize both stock returns and volatility derivatives. The joint estimations provide more realistic volatility term structures than the returns-based estimation. Furthermore, the joint estimations are good at capturing the flat skewness term structures and pricing particularly put options. The latter finding can be explained by the fact that the joint estimations yield a highly persistent volatility process, which allows the leverage effect to hold up at long horizons. Overall, the joint estimations can significantly improve option pricing over the returns-based estimation, as they involve different volatility states and persistence.


Dynamic Factor Copula Models with Estimated Cluster Assignments, with Andrew J. Patton, 2023, Journal of Econometrics, 237.

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 with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes. 


Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads, with Andrew J. Patton, 2018, Journal of Business and Economic Statistics, 36(2), 181-195.

This paper proposes a new class of copula-based dynamic models for high dimension conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our proposed models draw on successful ideas from the literature on modeling high dimension covariance matrices and on recent work on models for general time-varying distributions. Our use of copula-based models enables the estimation of the joint model in stages, greatly reducing the computational burden. We use the proposed new models to study a collection of daily credit default swap (CDS) spreads on 100 U.S. firms over the period 2006 to 2012. We find that while the probability of distress for individual firms has greatly reduced since the financial crisis of 2008-09, the joint probability of distress (a measure of systemic risk) is substantially higher now than in the pre-crisis period.


Accurate Evaluation of Expected Shortfall for Linear Portfolios with Elliptically Distributed Risk Factors, with Dobrislav Dobrev and Travis Nesmith, 2017,  Journal of Risk and Financial Management, 10(1), 5.

We provide an accurate closed-form expression for the expected shortfall of linear portfolios with elliptically distributed risk factors. Our results aim to correct inaccuracies that originate in Kamdem (2005) and are present also in at least thirty other papers referencing it, including the recent survey by Nadarajah et al. (2014) on estimation methods for expected shortfall. In particular, we show that the correction we provide in the popular multivariate Student t setting eliminates understatement of expected shortfall by a factor varying from at least four to more than 100 across different tail quantiles and degrees of freedom. As such, the resulting economic impact in financial risk management applications could be significant. We further correct such errors encountered also in closely related results in Kamdem (2007 and 2009) for mixtures of elliptical distributions. More generally, our findings point to the extra scrutiny required when deploying new methods for expected shortfall estimation in practice.


Modelling Dependence in High Dimensions with Factor Copulas, with Andrew J. Patton, 2017, Journal of Business and Economic Statistics, 35(1), 139-154.

This paper presents new models for the dependence structure, or copula, of economic variables based on a factor structure. The proposed models are particularly attractive for high dimensional applications, involving fifty or more variables. This class of models generally lacks a closed-form density, but analytical results for the implied tail dependence can be obtained using extreme value theory, and estimation via a simulation-based method using rank statistics is simple and fast. We study the finite-sample properties of the estimation method for applications involving up to 100 variables, and apply the model to daily returns on all 100 constituents of the S&P 100 index. We find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. We also show that the proposed factor copula model provides superior estimates of some measures of systemic risk.


High-Dimensional Copula-Based Distributions with Mixed Frequency Data, with Andrew J. Patton, 2016, Journal of Econometrics, 193, 349-366.

This paper proposes a new general model for high dimension distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, which enables the use of high frequency data to accurately measure and forecast linear dependence, and the use of a new class of copulas designed to capture nonlinear dependence among the resulting linearly uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted using composite likelihood, making this approach feasible even for hundreds of variables. A realistic simulation study verifies that multistage estimation with composite likelihood results in small loss in efficiency and large gain in computation speed. In- and out-of-sample tests confirm the statistical superiority of the proposed models applied to daily returns on all constituents of the S&P 100 index.


Simulated Method of Moments Estimation for Copula-Based Multivariate Models, with Andrew J. Patton, 2013, Journal of the American Statistical Association, 108(502), 689-700.

This article considers the estimation of the parameters of a copula via a simulated method of moments type approach. This approach is attractive when the likelihood of the copula model is not known in closed form, or when the researcher has a set of dependence measures or other functionals of the copula that are of particular interest. The proposed approach naturally also nests method of moments and generalized method of moments estimators. Drawing on results for simulation based estimation and on recent work in empirical copula process theory, we show the consistency and asymptotic normality of the proposed estimator, and obtain a simple test of overidentifying restrictions as a specification test. The results apply to both iid and time series data. We analyze the finite-sample behavior of these estimators in an extensive simulation study. We apply the model to a group of seven financial stock returns and find evidence of statistically significant tail dependence, and mild evidence that the dependence between these assets is stronger in crashes than booms.