投资者理性预期、流动性约束与股价崩盘传染研究

“传染性”是股价崩盘三大基本特征之一,会加剧股价崩盘负面影响,甚至引发系统性金融风险,因此,本文重点关注股价崩盘传染机制研究。首先,本文基于两阶段理性预期均衡模型,提出股价崩盘传染两大假设,即投资者理性预期与流动性约束导致传染;其次,基于2000-2016年全球28个国家或地区资本市场数据,实证检验股价崩盘传染机制和传染渠道。研究显示:(1)投资者理性预期、流动性约束会导致股价崩盘发生传染;(2)股价崩盘事件会在资本市场关联国家或地区传染;(3)提高资本市场信息透明度、加强金融管制有助于降低受关联国家或地区股价崩盘传染。     

Stock market crashes,firm characteristics,and stock returns

A number of studies have investigated the causes and effects of stock market crashes. These studies mainly focus on the factors leading to a crash and on the volatility and co-movements of stock market indexes during and after the crash. However, how a stock market crash affects individual stocks and if stocks with different financial characteristics are affected differently in a stock market crash is an issue that has not received sufficient attention. In this paper, we study this issue by using data for eight major stock market crashes that have taken place during the December 31, 1962–December 31, 2007 period with a large sample of US firms. We use the event-study methodology and multivariate regression analysis to study the determinants of stock returns in stock market crashes.     

金融同业活动与实体企业经营风险——来自地区层面同业存单业务的证据

本文研究了地区层面金融同业活动对实体企业经营风险的影响。基于各省份金融机构开启同业存单业务的研究表明,地区层面金融同业活动显著提升了当地上市企业股价大幅下跌的风险。进一步研究表明,随着同业存单业务的发展,当地上市企业债务融资成本和风险水平有所上升,而业绩表现和市场价值有所下降。上述发现表明,企业融资链条变长后,信贷市场道德风险上升,部分企业因风险偏好增强导致经营风险上升,更容易突然出现负面事件而导致股价大幅下跌。本文的发现揭示了金融同业活动存在监管规避的可能性及其影响实体企业的潜在路径,凸显了完善金融监管以更好服务实体经济发展的重要现实意义。     

Skewness-based market integration: A systemic risk measure across international equity markets

This study develops a novel skewness-based integration measure to assess systemic risk across international equity markets. We exploit skewness to further consider the tail information of return distribution, thereby extend the return-based integration measure of Pukthuanthong and Roll (2009). The empirical results indicate that the skewness-based integration measure is closely correlated with market crashes, Value-at-Risk, and leading indicators of equity markets. Moreover, the skewness-based integration measure improves the information content of systemic risk relative to the return-based integration measure, implying that tail information plays an indispensable role in early warning of systemic risk.     

金融同业活动与实体企业经营风险——来自地区层面同业存单业务的证据

本文研究了地区层面金融同业活动对实体企业经营风险的影响。基于各省份金融机构开启同业存单业务的研究表明,地区层面金融同业活动显著提升了当地上市企业股价大幅下跌的风险。进一步研究表明,随着同业存单业务的发展,当地上市企业债务融资成本和风险水平有所上升,而业绩表现和市场价值有所下降。上述发现表明,企业融资链条变长后,信贷市场道德风险上升,部分企业因风险偏好增强导致经营风险上升,更容易突然出现负面事件而导致股价大幅下跌。本文的发现揭示了金融同业活动存在监管规避的可能性及其影响实体企业的潜在路径,凸显了完善金融监管以更好服务实体经济发展的重要现实意义。     

Bank loan loss accounting treatments,credit cycles and crash risk

Banks that follow conditional conservatism in their loan loss accounting treatments benefit from a reduction in crash risk. The key discretionary loan loss accounting channels are provisions and allowances. We show that conditional conservatism reduces crash risk of small banks during periods of credit contraction and boom. Interestingly, for large banks, crash risk is not reduced by more conservative accounting even for those with higher levels of opacity. Hence regulation prompting for more conservative bank loan loss accounting does not present a significant opportunity to limit systemic effects arising from abrupt price declines in the stocks of large banks.     

Systemic risk in the European sovereign and banking system

We investigate the systemic risk of the European sovereign and banking system during 2008–2013. We utilize a conditional measure of systemic risk that reflects market perceptions and can be intuitively interpreted as an entity’s conditional joint probability of default, given the hypothetical default of other entities. The measure of systemic risk is applicable to high dimensions and not only incorporates individual default risk characteristics but also captures the underlying interdependent relations between sovereigns and banks in a multivariate setting. In empirical applications, our results reveal significant time variation in systemic risk spillover effects for the sovereign and banking system. We find that systemic risk is mainly driven by risk premiums coupled with a steady increase in physical default risk.     

Systemic risk of commodity markets: A dynamic factor copula approach

This study aims to measure the systemic risk of commodity markets and investigate its causal relationship with the macroeconomy. First, we propose a novel measure called the joint probability of abnormal changes (JPAC) to measure the systemic risk of commodity markets. Second, we introduce two new measures, the expected proportion of commodities in abnormal price change (EPAPC) and the contribution to systemic risk (CSR), to identify the systemic importance of each commodity. Third, we examine the relationship between JPAC and some key macroeconomic variables using mixed-frequency Granger causality tests. We conduct an empirical study using 24 commodity indices from 2015 to 2021. Our results reveal that: (1) JPAC can well capture the risk events in the real world; (2) the systemic risk of commodity markets has increased significantly since the start of the US-China trade war; (3) EPAPC and CSR indicate that energy commodities have higher systemic importance than others and are most likely to cause market fluctuations; and (4) some causal relationships between the systemic risk of commodity markets and the macroeconomy are identified. Overall, this study improves our understanding of systemic risk in commodity markets and provides important implications for policymakers in China and the US.     

Contagion as a domino effect in global stock markets

This paper shows that stock market contagion occurs as a domino effect, where confined local crashes evolve into more widespread crashes. Using a novel framework based on ordered logit regressions we model the occurrence of local, regional and global crashes as a function of their past occurrences and financial variables. We find significant evidence that global crashes do not occur abruptly but are preceded by local and regional crashes. Besides this form of contagion, interdependence shows up by the effect of interest rates, bond returns and stock market volatility on crash probabilities. When it comes to forecasting global crashes, our model outperforms a binomial model for global crashes only.     

Economic policy uncertainty and stock price crash risk

We examine the impact of economic policy uncertainty (EPU) on firm-specific crash risk. Based on a large sample of Chinese listed firms over the period from 2000 to 2017, we provide empirical evidence that firms are more likely to experience stock price crashes when EPU increases. Cross-sectionally analysis further reveals that the impact of EPU on stock price crash risk is stronger for firms whose returns are more sensitive to EPU. More specifically, young stocks, small stocks, high volatility stocks, and growth stocks, which have higher valuation uncertainty per se, are more sensitive to EPU and are more affected by EPU in terms of crash risk. We further show that EPU is significantly and positively associated with aggregated stock price crash risk at the market level.     

The role of multivariate skew-Student density in the estimation of stock market crashes

By combining the multivariate skew-Student density with a time-varying correlation GARCH (TVC-GARCH) model, this paper investigates the spread of crashes in the regional stock markets. The regional index series of European, USA, Latin American and Asian markets are modeled jointly, and the maximum likelihood estimates show that a TVC-GARCH model with multivariate skew-Student density outperforms that with multivariate normal density substantially. Depending on the past information set, the conditional 1-day crash probabilities are computed, and the forecast performances of the TVC-GARCH model with both multivariate skew-Student and normal densities are evaluated. In both bilateral and global environments, multivariate skew-Student density has better predictive accuracy than normal density. In global crash probability forecasts, multivariate skew-Student density attains much higher hit rate and Kuipers score than multivariate normal density, thus it can be used to improve early-warning systems.     

The impact of terrorism on financial markets: An empirical study

The main focus of this paper is to study empirically the impact of terrorism on the behavior of stock, bond and commodity markets. We consider terrorist events that took place in 25 countries over an 11-year time period and implement our analysis using different methods: an event-study approach, a non-parametric methodology, and a filtered GARCH-EVT approach. In addition, we compare the effect of terrorist attacks on financial markets with the impact of other extreme events such as financial crashes and natural catastrophes. The results of our analysis show that a non-parametric approach is the most appropriate method among the three for analyzing the impact of terrorism on financial markets. We demonstrate the robustness of this method when interest rates, equity market integration, spillover and contemporaneous effects are controlled. We show how the results of this approach can be used for investors’ portfolio diversification strategies against terrorism risk.     

Stock Crashes and Jumps Reactions to Information Demand and Supply: An Intraday Analysis

The objective of this paper is to examine the possible linkage between the intraday stock price crashes and jumps and public information by using data from the Chinese stock market and Baidu Index. We divided public information into two kinds of information: supply through online media and information demand across inquiries by individual investors. Using a large sample from Chinese listed firms from 2013 to 2019, our evidence clearly indicates that online information supply and demand both have a positive impact on the intraday crashes and jumps; this is, the firm with higher information supply and demand more likely to experience intraday crashes and jumps. The results are robust to an alternative measure of crash risk. Moreover, we further examine whether the market conditions have an impact on the relationship between information flow and intraday crashes and jumps, and find that the marginal effect of information supply on intraday price crashes and jumps is smaller in the bull market phase. Moreover, the bull market phase enhances the effect of information demand on intraday price crashes and jumps.

     

Local risk factors in emerging markets: Are they separately priced?

We provide new evidence on the pricing of local risk factors in emerging stock markets. We investigate whether there is a significant local currency premium together with a domestic market risk premium in equity returns within a partial integration asset pricing model. Given previous evidence on currency risk, we conduct empirical tests in a conditional setting with time-varying prices of risk. Our main results support the hypothesis of a significant exchange risk premium related to the local currency risk. Exchange rate and domestic market risks are priced separately for our sample of seven emerging markets. The empirical evidence also suggests that although statistically significant, local currency risk is on average smaller than domestic market risk but it increases substantially during crises periods, when it can be almost as large as market risk. Disentangling these two factors is thus important in tests of international asset pricing for emerging markets.     

Market liquidity in the financial crisis: The role of liquidity commonality and flight-to-quality

We examine the dynamics and the drivers of market liquidity during the financial crisis, using a unique volume-weighted spread measure. According to the literature we find that market liquidity is impaired when stock markets decline, implying a positive relation between market and liquidity risk. Moreover, this relationship is the stronger the deeper one digs into the order book. Even more interestingly, this paper sheds further light on so far puzzling features of market liquidity: liquidity commonality and flight-to-quality. We show that liquidity commonality varies over time, increases during market downturns, peaks at major crisis events and becomes weaker the deeper we look into the limit order book. Consistent with recent theoretical models that argue for a spiral effect between the financial sector’s funding liquidity and an asset’s market liquidity, we find that funding liquidity tightness induces an increase in liquidity commonality which then leads to market-wide liquidity dry-ups. Therefore our findings corroborate the view that market liquidity can be a driving force for financial contagion. Finally, we show that there is a positive relationship between credit risk and liquidity risk, i.e., there is a spread between liquidity costs of high and low credit quality stocks, and that in times of increased market uncertainty the impact of credit risk on liquidity risk intensifies. This corroborates the existence of a flight-to-quality or flight-to-liquidity phenomenon also on the stock markets.     

Extreme dependence with asymmetric thresholds: Evidence for the European Monetary Union

Existing papers on extreme dependence use symmetrical thresholds to define simultaneous stock market booms or crashes such as the joint occurrence of the upper or lower one percent return quantile in both stock markets. We show that the probability of the joint occurrence of extreme stock returns may be higher for asymmetric thresholds than for symmetric thresholds. We propose a non-parametric measure of extreme dependence which allows capturing extreme events for different thresholds and can be used to compute different types of extreme dependence. We find that extreme dependence among the stock markets of ten initial EMU member countries, the United Kingdom, and the United States is largely asymmetrical in the pre-EMU period (1989–1998) and largely symmetrical in the EMU period (1999–2010). Our findings suggest that ignoring the possibility of asymmetric extreme dependence may lead to an underestimation of the probability of co-booms and co-crashes.     

A statistical model of speculative bubbles,with applications to the stock markets of the United States,Japan, and China

It is common knowledge that the more prices deviate from fundamentals, the more likely it is for prices to reverse. Taking this into account, we propose a simple statistical model to identify speculative bubbles in financial markets. Through the estimates of the time varying parameters, including transition probabilities, we can identify when and how newly born bubbles grow and burst over time. The model can be estimated by recursive computations, which require a huge storage capacity for standard computers. For this reason, we introduce an approximation in the computation, maintaining the recursive nature of our estimation technique. We then apply this model to the stock markets of the United States, Japan, and China, estimate its parameters and the probabilities of a bubble crash, and obtain several interesting results: the time series data of the stock price bubble show an inherently non-stationary development and the probability of a bubble crash indeed increases as the stock price becomes too high or too low.     

How Does Investor Sentiment Affect Stock Market Crises? Evidence from Panel Data

We test the impact of investor sentiment on a panel of international stock markets. Specifically, we examine the influence of investor sentiment on the probability of stock market crises. We find that investor sentiment increases the probability of occurrence of stock market crises within a one‐year horizon. The impact of investor sentiment on stock markets is more pronounced in countries that are culturally more prone to herd‐like behavior, overreaction and low institutional involvement.     

R around the world: New theory and new tests

Morck, Yeung and Yu show that R² is higher in countries with less developed financial systems and poorer corporate governance. We show how control rights and information affect the division of risk bearing between managers and investors. Lack of transparency increases R² by shifting firm-specific risk to managers. Opaque stocks with high R²s are also more likely to crash, that is, to deliver large negative returns. Using stock returns from 40 stock markets from 1990 to 2001, we find strong positive relations between R² and several measures of opaqueness. These measures also explain the frequency of crashes.     

The cross-sectional relation between conditional heteroskedasticity,the implied volatility smile,and the variance risk premium

This paper estimates how the shape of the implied volatility smile and the size of the variance risk premium relate to parameters of GARCH-type time-series models measuring how conditional volatility responds to return shocks. Markets in which return shocks lead to large increases in conditional volatility tend to have larger variance risk premia than markets in which the impact on conditional volatility is slight. Markets in which negative (positive) return shocks lead to larger increases in future volatility than positive (negative) return shocks tend to have downward (upward) sloping implied volatility smiles. Also, differences in how volatility responds to return shocks as measured by GARCH-type models explain much, but not all, of the variations in excess kurtosis and multi-period skewness across different markets.