Journal of Empirical Finance最新文献

We use Markov chain Monte Carlo (MCMC) sampling to draw model coefficients to generate LGD distributions. We find that applying this Bayesian method on a sophisticated model, such as the zero-one-inflated beta (ZOIB) model, that accounts for the bi-modal distribution of the LGDs can generate LGD distributions that mimic the observed distributions well. By contrast, applying this Bayesian sampling approach on a simple model such as Tobit cannot capture the bi-modal LGD distributions accurately. Finally, we argue that this Bayesian sampling approach to generate LGD distributions is better fit for the stress testing purpose than the typical approach to estimate LGD model coefficients and then stress the macro variables. The latter approach yields stressed LGDs that may not be conservative enough, even if the macro variables are stressed to their worst historical values.

A low frequency factor model regression uses changes or returns computed at a lower frequency than data available. Using overlapping observations to estimate low frequency factor model regressions results in more efficient estimates of OLS coefficients and standard errors, relative to using independent observations or high frequency estimates. I derive the relevant inference and propose a new method to correct for the induced autocorrelation. I present a series of simulations and empirical examples to support the theoretical results. In tests of asset pricing models, using overlapping observations results in lower pricing errors, compared to existing alternatives.

We apply the machine-learning technique proposed by Zhou et al. (2024) to analyze credit rating reports in China’s bond markets, identifying featured vocabulary and generating text analysis scores. Compared with the traditional bag-of-words text analysis, evidence suggests three advantages of machine-learning scoring. Firstly, it covers featured vocabulary that compensates for missing information; secondly, it reduces misclassification of words’ sentiments; moreover, it mitigates the problem of equal weighting inherent in the bag-of-words method. Our findings indicate that the featured vocabulary neglected in the bag-of-words method plays a crucial role in text analysis and significantly contributes to bond pricing. Additionally, we find that machine-learning text analysis can address AAA rating inflation within China’s bond markets to some extent. In contrast, the bag-of-words method exhibits limited efficacy in mitigating this issue.

This paper examines theoretically and empirically a variance-dependent pricing kernel in the continuous-time two-factor stochastic volatility (SV) model. We investigate the relevance of such a kernel in the joint modeling of index returns and option prices. We contrast the pricing performance of this model in capturing the term structure effects and smile/smirk patterns to discrete-time GARCH models with similar variance-dependent kernels. We find negative and significant risk premium for both volatility factors, implying that investors are willing to pay for insurance against increases in volatility risk, even if it has little persistence. In-sample, the component GARCH model exhibits a slightly better fit overall and across all maturity buckets than the two-factor SV model. However, the two-factor SV model reduces strike price bias, giving rise to the model’s ability in reconciling the physical and risk-neutral distribution. Out-of-sample, the two-factor SV model has better fit to data.

We examine how the 2008 U.S. short-selling ban on the stocks of financial institutions impacted their equity tail risk. Using propensity score matching and difference-in-difference regressions, we show that the ban was not effective in restoring financial stability as measured by the stocks’ dynamic Marginal Expected Shortfall. In contrast, especially large institutions, those who were most vulnerable to market downturns in the preban period, as well as those equities with associated put option contracts, experienced sharp increases in their exposure to market downturns during the ban period, contrary to regulators’ intentions.

Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters.

This paper proposes a new graph-based conditional moments (GRACE) method to do portfolio selection based on thousands of stocks or even more. The GRACE method first learns the conditional quantiles and mean of stock returns via a factor-augmented temporal graph convolutional network, which is guided by the set of stock-to-stock relations as well as the set of factor-to-stock relations. Next, the GRACE method learns the conditional variance, skewness, and kurtosis of stock returns from the learned conditional quantiles via the quantiled conditional moment method. Finally, the GRACE method uses the learned conditional mean, variance, skewness, and kurtosis to construct several performance measures, which are criteria to sort the stocks to proceed the portfolio selection in the well-known 10-decile framework. An application to NASDAQ and NYSE stock markets shows that the GRACE method performs much better than its competitors, particularly when the performance measures are comprised of conditional variance, skewness, and kurtosis.

We examine U.S. equity trader use of inverted versus maker-taker venues. Inverted (maker-taker) venues charge fees for maker (taker) executions and pay rebates for taker (maker) executions. Researchers argue maker fee orders can be used to front run same price maker rebate orders. We find maker fee orders are often routed with the intent to set market prices. They execute quicker and are more informed than maker rebate orders. Conversely, taker rebate orders execute slower and are less informed than taker fee orders. Our results suggest that maker and taker fee orders are more likely to convey information.

The paper studies the role of information in cross-border trading by using the Stock Connect as a novel laboratory. We present evidence that northbound investors have an additional informational advantage over domestic institutional investors regarding firm fundamentals. A long-short strategy earns an average weekly return of 0.34% after adjusting for the Chinese three-factor model. Furthermore, the information advantage of northbound investors is likely to work to a greater effect in asymmetric information environments. Additionally, northbound flows are useful in explaining the subsequent trading activities of domestic investors, which becomes more salient over time and among firms experiencing more attention-induced copycat trading.

We document a sharp and persistent decline in bond issuances following covenant violations also called technical defaults. However, we find no evidence that firms’ investment and performance change after technical defaults. Furthermore, we document that most of the technical defaults are waived off by bondholders through the debenture holders’ meetings. Although covenants serve as tripwires for renegotiation between bond issuers and investors, control rights are rarely transferred from shareholders to bond investors following technical defaults.