1/8/2021 — We specify and estimate a time-varying Markov model of COVID-19 cases for the US in 2020. We find that the estimated level of undetected infections spiked in March and remained elevated through May. However, since late April estimated undetected infections have generally declined though it was not until June or July that detected cases exceeded the estimated number of undetected cases. 

• KEYWORD(S):
• covid-19

4/7/2020 — We apply advances in analysis of mix frequency and sparse data to estimate “unsmoothed” private equity (PE) Net Asset Values (NAVs) at the weekly frequency for individual funds. Using simulations and a large sample of buyout and venture funds, we show that our method yields superior estimates of fund asset values than a simple approach based on comparable public asset and as-reported NAVs.

• KEYWORD(S):
• private capital
• private equity
• venture capital

10/1/2020 — This paper proposes a new test for a large set of zero restrictions in regression models based on a seemingly overlooked, but simple, dimension reduction technique. The procedure involves multiple parsimonious regression models where key regressors are split across simple regressions.

• KEYWORD(S):
• data project

5/19/2020 — We provide an innovative methodological contribution to the measurement of returns on infrequently traded assets using a novel approach to repeat-sales regression estimation. The model for price indices we propose allows for correlation with other markets, typically with higher liquidity and high frequency trading. Using the new econometric approach, we propose a monthly art market index, as well as sub-indices for impressionist, modern, post-war, and contemporary paintings based on repeated sales at a monthly frequency. The correlations enable us to update the art index via observed transactions in other markets that have a link with the art market.

• KEYWORD(S):
• asset pricing
• real assets

9/24/2020 — The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at different frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates.

• KEYWORD(S):
• finance
• machine learning

8/1/2020 — 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 & Giot (2000)'s 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 variables exhibiting increased responsiveness to their most recent realizations.

• KEYWORD(S):
• economics
• finance

6/24/2020 — We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time series data structures we focus on the sparse-group LASSO estimator, which includes the LASSO and the group LASSO as special cases.

• KEYWORD(S):
• finance

4/21/2021 — This article introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails.

4/19/2020 — Using a sample of the 48 contiguous United States, we consider the problem of forecasting state and local governments’ revenues and expenditures in real time using models that feature mixed-frequency data. We find that single-equation mixeddata sampling (MIDAS) regressions that predict low-frequency fiscal outcomes using high-frequency economic data historically outperform both traditional fiscal forecasting models and theoretically motivated multi-equation models.

• KEYWORD(S):
• covid-19

1/17/2020 — Hasbrouck (2018) takes advantage of the fact that U.S. equity market data are timestamped to nanosecond precision, and explores models of price dynamics at resolutions sufficient to capture the reactions of the fastest agents. The paper therefore addresses the econometric analysis of multivariate time series models at sub-millisecond frequencies and relies on long distributed lag models to alleviate the computational complexity while still taking advantage of the inherent sparsity of price transitions.

12/25/2022 — In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm, called tensor principal component analysis (PCA), which generalizes the traditional PCA applicable to panel data.

• KEYWORD(S):
• economics
• finance

9/21/2020 — This article presents tests for the existence of common factors spanning two large panels/groups of macroeconomic and financial variables, and the estimation of common and group-specific factors. New analytical results are derived regarding (i) the difference in the asymptotic distribution of the test statistics when aggregating the data first and then extracting the principal components (PCs), or vice versa, as well as (ii) the estimation of the common factor and its asymptotic distribution, extending the work of Andreou et al. (2019).

• KEYWORD(S):
• finance

2/5/2023 — Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Applications, particularly in finance, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a formal test for the question whether the factor spaces of latent and observable (economic) factors are equal.

• KEYWORD(S):
• economics
• finance

2/1/2021 — This paper presents an innovative approach to extract Volatility Factors which predict the VIX, the S&P500 Realized Volatility (RV) and the Variance Risk Premium (VRP). The approach is innovative along two different dimensions, namely: (1) we extract Volatility Factors from panels of filtered volatilities — in particular large panels of univariate ARCH-type models and propose methods to estimate common Volatility Factors in the presence of estimation error and (2) we price equity volatility risk using factors which go beyond the equity class namely Volatility Factors extracted from panels of volatilities of short-run funding spreads.

• KEYWORD(S):
• asset pricing

3/11/2019 — On January 31 and February 1, 2019, the Frank H. Kenan Institute of Private Enterprise (Kenan Institute) hosted its Frontiers of Entrepreneurship Conference at The Breakers Palm Beach Resort. The conference brought together more than 150 academic research scholars, policy experts and private sector professionals to discuss and debate the most challenging current issues in the field of entrepreneurship in order to set the agenda for future research and policy.