Asset pricing, spatial linkages and contagion in real estate stocks

Following recent methodological developments, we estimate a spatial multi-factor model (SMFM) which combines asset pricing techniques with spatial econometrics to assess systemic implications for REIT index returns. We distinguish between comovement due to market risk exposure (systematic risk) and comovement due to linkages between markets (spillover risk). We find that the spillover risk dramatically increases during the global financial crisis and can explain up to 60% of total asset variation. In the rest of the time, idiosyncratic risks have been the predominant type of risk in real estate stocks. Our results have implications for investors showing that the market can channel asset volatility leading to contagion during crisis periods and therefore residual linkages between country indices need to be accounted for as a means of assessing the diversification benefits of a global portfolio

Text-Based Linkages and Local Risk Spillovers in the Equity Market

This paper uses extensive text data to construct firms' links via which local shocks transmit. Using the novel text-based linkages, I estimate a heterogeneous spatial-temporal model which accommodates the contemporaneous and dynamic spillover effects at the same time. I document a considerable degree of local risk spillovers in the market plus sector hierarchical factor model residuals of S&P 500 stocks. The method is found to outperform various previously studied methods in terms of out-of-sample fit. Network analysis of the spatial-temporal model identifies the major systemic risk contributors and receivers, which are of particular interest to microprudential policies. From a macroprudential perspective, a rolling-window analysis reveals that the strength of local risk spillovers increases during periods of crisis, when, on the other hand, the market factor loses its importance

Spatial panel data models with structural change

Spatial panel data models are widely used in empirical studies. The existing theories of spatial models so far have largely confine the analysis under the assumption of parameters stabilities. This is unduely restrictive, since a large number of studies have well documented the presence of structural changes in the relationship of economic variables. This paper proposes and studies spatial panel data models with structural change. We consider using the quasi maximum likelihood method to estimate the model. Static and dynamic models are both considered. Large-$T$ and fixed-$T$ setups are both considered. We provide a relatively complete asymptotic theory for the maximum likelihood estimators, including consistency, convergence rates and limiting distributions of the regression coefficients, the timing of structural change and variance of errors. We study the hypothesis testing for the presence of structural change. The three super-type statistics are proposed. The Monte Carlo simulation results are consistent with our theoretical results and show that the maximum likelihood estimators have good finite sample performance

Spatial panel data models with structural change

Spatial panel data models are widely used in empirical studies. The existing theories of spatial models so far have largely confine the analysis under the assumption of parameters stabilities. This is unduely restrictive, since a large number of studies have well documented the presence of structural changes in the relationship of economic variables. This paper proposes and studies spatial panel data models with structural change. We consider using the quasi maximum likelihood method to estimate the model. Static and dynamic models are both considered. Large-$T$ and fixed-$T$ setups are both considered. We provide a relatively complete asymptotic theory for the maximum likelihood estimators, including consistency, convergence rates and limiting distributions of the regression coefficients, the timing of structural change and variance of errors. We study the hypothesis testing for the presence of structural change. The three super-type statistics are proposed. The Monte Carlo simulation results are consistent with our theoretical results and show that the maximum likelihood estimators have good finite sample performance

Pricing Offshore Services: Evidence from the Paradise Papers

The Paradise Papers represent one of the largest public data leaks comprising 13.4 million con_dential electronic documents. A dominant theory presented by Neal (2014) and Gri_th, Miller and O'Connell (2014) concerns the use of these offshore services in the relocation of intellectual property for the purposes of compliance, privacy and tax avoidance. Building on the work of Fernandez (2011), Billio et al. (2016) and Kou, Peng and Zhong (2018) in Spatial Arbitrage Pricing Theory (s-APT) and work by Kelly, Lustig and Van Nieuwerburgh (2013), Ahern (2013), Herskovic (2018) and Proch_azkov_a (2020) on the impacts of network centrality on _rm pricing, we use market response, discussed in O'Donovan, Wagner and Zeume (2019), to characterise the role of offshore services in securities pricing and the transmission of price risk. Following the spatial modelling selection procedure proposed in Mur and Angulo (2009), we identify Pro_t Margin and Price-to-Research as firm-characteristics describing market response over this event window. Using a social network lag explanatory model, we provide evidence for social exogenous effects, as described in Manski (1993), which may characterise the licensing or exchange of intellectual property between connected firms found in the Paradise Papers. From these findings, we hope to provide insight to policymakers on the role and impact of offshore services on securities pricing

Three Essays in Financial Econometrics

Understanding how cross-sectional units interact with each other in a panel setting is an important question, given we are living in a more and more interconnected world. The effort to provide a solution to this question involves proposing statistical models that capture such features and obtain network datasets that characterize interdependency among entities. With a hope to contribute to this discipline, this thesis looks into the cross-sectional dependence in panel both theoretically and empirically. The first chapter develops a multi-country contagion model where the individual-specific Markov chains are interdependent. The second chapter studies a spatial factor model, which accommodates two distinct types of cross-sectional dependence in a panel. The chapter also utilizes a novel network dataset and empirically shows local interactions play a vital role in explaining comovement in equity returns. Chapter 3 studies peer groups of arbitrage characteristics. The details of the three chapters are summarized below: Sovereign Risk Contagion in Eurozone with Mutual Exciting Regime-Switching Model This paper proposes a new mutual exciting regime-switching model where crises can spread contagiously across countries. Each country has its own hidden stochastic process that determines whether the country is in a normal or crisis regime. Contagion is defined as a rise in the transition probability to the crisis regime when other countries are in crisis in the past state. Using this new approach, I revisit the sovereign risk contagion in the euro area. I find that there are striking shifts in market pricing functions for the sovereign bond spreads. Multi-country contagion plays a dominant role in driving such shifts, while common risk factors and country-specific fundamentals are much less important. News-Implied Linkages and Local Dependency in the Equity Market This paper studies a heterogeneous coefficient spatial factor model that separately addresses both common factor risks (strong cross-sectional dependence) and local dependency (weak cross-sectional dependence) in the equity returns. For a high-dimensional panel of equity returns, it is challenging to measure firm-to-firm connectivity. We use extensive business news to construct firms’ links via which local shocks transmit, and we use those news-implied linkages as a proxy for the connectivity among firms. We document a considerable degree of local dependency among S&P 500 stocks. From the asset pricing perspective, we derive the theoretical implications of no asymptotic arbitrage for the heterogeneous spatial factor model. Empirically, we show that adding spatial interactions to factor models significantly reduces mispricing and estimation errors. We also show that our news-implied linkages provide a comprehensive and integrated proxy for firm-to-firm connectivity, and it out-performs other existing networks in the literature. Dynamic Peer Groups of Arbitrage Characteristics This chapter proposes an asset pricing factor model constructed with semi-parametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-splines coefficients, with the strengthened power approaches to one asymptotically. We also investigate the structure of mispricing components through Hierarchical K-means Clusterings. We apply our methodology to CRSP (Center for Research in Security Prices) and Compustat data for the US stock market with one-year rolling windows during 1967-2017. This empirical study shows the presence of mispricing functions in certain time blocks. We also find that distinct clusters of the same characteristics lead to similar arbitrage returns, forming a “peer group” of arbitrage characteristics

Essays on Empirical Asset Pricing Models

This thesis examines co-movement across industry return and value and momentum asset price anomalies through a new perspective and uses machine learning and spatial econometrics approaches. The first chapter examines the main approaches developed in the cross-section asset pricing literature for finding risk variables. The second chapter focuses on spatial co-movement across US industry returns. We show that spatial co-movement explains the variance in US industry returns after accounting for exposure to common variables, serial dynamics, and industry sector-specific characteristics using a dynamic spatial panel data model. The results show that an investment strategy that buys industry portfolios with high own-return and high spatially connected (neighboring) portfolio return and sells industry portfolios with low own-return and low spatially connected (neighboring) portfolio return generates an annual non-market return of approximately 8%. In the third chapter of the dissertation, we propose a multi-factor model in which the extra variables (apart from the standard market factor) are the innovation in each sparse principal component. Our findings demonstrate that our suggested hedging factors, which include Production (PR), Housing (H), Yield (Y), and Yield Spread (YS), explain a significant portion of the spread in average equity premia of the momentum portfolio deciles and value portfolio returns. In addition, the paper examines whether the multi-factor model is consistent with Merton\u27s (1973) ICAPM framework