As digital technologies proliferate, firms increasingly leverage digital transformation strategically, necessitating new orientations attuned to digital technological change. This study investigates how digital orientation (DORI)- the philosophy of harnessing digital technology scope, digital capabilities, digital ecosystem coordination, and digital architecture configuration for competitive advantage – influences firms’ environmental, social, and governance performance (ESG_per). Analysis of Chinese A-share firms from 2010-2019 reveals DORI is associated with superior ESG_per, operating through the mediating mechanism of enhanced digital finance (DIFIN) as a fund-providing facilitator for sustainability initiatives. Additional analysis uncovers important heterogeneities – private firms, centrally owned state-owned enterprises, politically connected, and emerging companies exhibit the strongest DORI - ESG_per linkages. Prominently, the study findings are validated through a battery of robustness tests, including instrumental variable methods, and propensity score matching. Overall, the results underscore the need for firms to purposefully develop multifaceted digital orientation and furnishes novel theoretical insights and practical implications regarding DORI’s role in improving ESG_per.

Purpose – The aim of this paper is to provide additional insights on the association between real earnings management (REM) and crash risk, particularly from the perspective of an emerging market economy. It also examines the moderation role that internal and external corporate governance may play in this area. Design/methodology/approach – Relying on archival data from the RESSETand CSMAR databases over a timeframe from 2010 to 2018 of China listed company, the authors test the hypotheses by regressing common measures of crash risk on the treatment variable (REM) and crash risk control variables identified in the prior crash risk literature. The authors also introduce monitoring proxies (internal controls as an internal governance and institutional ownership as an external governance) and assess how effective internal and external governance moderate the relation between REM and stock price crash risk. Findings – The results suggest firms with higher REM have a significantly greater stock price crash risk, and that this association is mitigated by external monitoring. That is, greater institutional ownership, particularly pressure insensitive owners, mitigates the impact of REM on stock price crash risk. However, internal control does not mitigate the association between REM and stock price crash risk. Originality/value – Following the passage of the Sarbanes–Oxley (SOX) Act, prior research has documented an increase in the use of REM and a positive association between REM and cash risk. The authors demonstrate that they persist in one of the largest emerging markets where institutional regulations, market conditions and corporate behaviors are different from those in developed markets. Also, the assessment of the moderation effect of internal and external governance mechanisms could have meaningful implications for investors and regulators in Chinese and other emerging markets.

In the era of big data, asset pricing is influenced by various factors, which are extracted from multi-source heterogeneous information, such as high frequency market and sentiment information, low frequency firm characteristic and macroeconomic information. Especially, low frequency policy information plays a significant role in the long-term pricing in China but it is barely investigated due to its textual form. To this end, we first extract policy variables from major national development plans (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using Natural Language Processing (NLP) technique and Dynamic Topic Model (DTM). However, traditional models are inadequate for mixed frequency data modeling and feature extraction. Then, we propose a mixed frequency deep factor asset pricing model (MIDAS-DF) that solves the asset pricing problems under the mixed frequency data environment through mixed data sampling (MIDAS) technique and deep learning architecture. Time-varying latent factors and factor loadings can be modeled from mixed frequency data directly in a nonlinear and data-driven way. Thus, the MIDAS-DF model is able to learn the nonlinear joint-patterns hidden in multi-source heterogeneous information. Our empirical studies of 4939 stocks on the Chinese A-share market from January 2003 to July 2022 demonstrate that low frequency policy information has profound impacts on asset pricing, which anchors the long-term pricing direction, and high frequency market and sentiment information have significant influences on stock prices, which optimize the short-term pricing accuracy, they together enhance the pricing effects. Consequently, pricing effects the MIDAS-DF model outperform the five competing models on individual stocks, various test portfolios, and investment portfolios. Our research about heterogeneous information provides implications to the government and regulators for decision-support in policy-making and our investment portfolio is of great importance for investors’ financial decisions.

提出了一种新的评价方法，用于评价波动率模型。该方法兼顾了模型拟合样本的能力和模型的平滑性，而避免了经典的评价方法只考虑模型拟合样本能力的缺陷。新方法有利于投资者挑选出交易成本相对较低的和风险对冲能力相对较强的波动率模型。实证例子是估计中国股票市场的行业时变风险：对三大类波动率模型进行了评价，并指出评价方法或标准的选择直接影响金融市场风险估计或预测的评价结果。

This study adopts the Markov-switching ARCH (hereafter SWARCH) model to examine the volatility nature and volatility linkages of four segmented Chinese stock indices (SHA, SZA, SHB, and SZB). Our empirical findings are consistent with the following notions. First, we find strong evidence of regime shift in the volatility of four segmented markets and SWARCH model appears to outperform standard GARCH family models. Second, although there are some common features of volatility switch in segmented markets, there exist a few difference: (i)compared with the A-share markets, B-share markets are more volatile and shift more frequently between high- and low-volatility states; (ii) B-share markets have longer stays at high volatility state than the A-share markets; (iii) the relative magnitude of the high volatility compared with that of the low volatility is much greater than the case in two A-share markets. Third, B-share markets are found to be more sensitive to international shocks, while the A-share markets seem immune to international spillovers of volatility. Finally, analyses of volatility spillover effect among the four stock markets indicate that the A-share markets play a dominant role in volatility in Chinese stock markets.

Early-stage ?rms usually have a single large Research and Development (R&D) project that requires multi-stage investment. Firms? volatility can dramatically change due to the evolvement of R&D e¤orts and stage clearing. First, the success (failure) of R&D e¤orts within each stage (jump risk) decreases (increases) the un- certainty (i.e. volatility) level of the ?rms?future returns ?"jump e¤ect". Second, at the end of each stage, ?rms decide whether to continue next stage investment upon re-evaluating the project prospect conditional on the resolution of technical uncertainty and other information; as ?rms survive each investment stage and are becoming mature, the uncertainty level of their future returns should eventually decrease in later investment stages that lead to maturity ?"stage-clearing e¤ect". Ignoring these e¤ects results in incorrect estimation of ?rms?future volatility, an important element for early-stage ?rm valuation. In this paper, I develop a gener- alized Markov-Switching EARCH methodology for early-stage ?rms with discrete stage-clearing and jumps. My methodology can identify structural changes in the idiosyncratic volatility and also explore the relation between price changes and future volatility. Using a hand-collected dataset of early-stage biotech ?rms, I con?rmed the existence of the "stage-clearing e¤ect" and the "jump e¤ect". In the second part of my paper, I model early-stage ?rms as sequences of nested call options with jumps that lead to mature ?rms. "Jump e¤ect" arises because the early-stage ?rms are modeled as compound call options with jumps on the underly- ing cash ?ows, the volatility of the early-stage ?rms at each stage is determined by the compound call option elasticity to the underlying cash ?ows. If the downside (upside) jump happens, the value of the underlying cash ?ows decreases (increases), which makes the compound call option elasticity go up (down). As a result, the compound call option becomes riskier (less risky). "Stage-clearing e¤ect" arises because as ?rms exercise their option to continue investment, the new options that ?rms enter into will eventually become a less risky option.

The global financial crisis is hitting China hard with great adversity. In response, China start to formulated the plan for dealing with the financial crisis and its possible fallout in June 2008 when China was in the critical stage of putting up the Olympic Games. The Chinese leadership judges the crisis is going to be a serious disaster but not as bad as the great depression of the 1930s. An America-type crisis is unlikely to happen in the country and the main threat would be the Chinese real sector being dragged down under, which in turn sparks a crisis in the financial sector. China’s strategy for combating the crisis therefore is to deal with the immediate crisis effects in the real economy in the first place, and looks for opportunities in the meantime. The overwhelming emphasis is placed on expanding domestic demand to fuel growth. Following this strategy, China has rolled out a comprehensive package of combating measures. The fiscal expansion hit the headlines with extensive government financial support for infrastructure and public service projects. Yet the Chinese monetary stimulus is actually more powerful. The stance of Chinese monetary policy has changed from being precautionary against inflation with flexibility to appropriate easing to promote growth. After several rounds of rate cuts, the Chinese version of quantitative easing takes the central stage. In China’s battle with the financial crisis, the monetary stimulus is playing a leading role at the moment. The international dimensions of China’s monetary policy typify how China turns a crisis into a world of opportunity. China has taken a conservative approach to managing her reserves in which the huge international reserves are taken as self insurance rather than an avenue for international leverage. Within this framework and if safety of these foreign assets can be assured, China can provide finance to countries in crisis through international financial organisations. In addition to the Panda Bonds, the chief way for China to make funding contribution is through IMF. For this matter, China supports the motion to increase the IMF’s lending capacity and would buy the bonds it issues. China is actively calling for reform of international financial architecture. Chinese advisers have publically argued that the increase in China’s funding contribution has to be paralleled by an increase in China’s profile in the power structure in the IMF. In many occasions, China has also acted as spokesman of the emerging and developing economies by making cases for increasing their say in world financial affairs. But on the whole, China has been cautious not to committing herself too much as she knows probably she has little to gain from international policy coordination. Against this backdrop, China has chosen to focus on regional financial cooperation proactively and considerable progress has been made in this area. China’s dealing with the current financial crisis is unassuming. What she has done is down-to-earth common sense. However, the Chinese approach is shown signs of working. Despite the early success of crisis handling, there remain fundamental problems in China’s structure of economic growth. How to redress structural imbalances in the economy, to boost domestic demand, to calm down the property market and, above all, to create millions of jobs, are still the major huge challenges China is facing.

本文对VaR的理论与计算模型进行了系统地介绍，并结合中国证券市场的数据进行了深度分析。针对其中具有代表性的Risk Metrics方法、ARCH类模型、混合正态以及广义误差分布中涉及到的有关参数进行估计，提出了适合中国证券市场的主要参数。具体结果如下： 1． 结合中国证券市场主要指数估计了Risk Metrics模型中的最优衰减因子，结果如下表： 最优衰减因子 上海综指 0.944 上证A指 0.942 深圳成份 0.920 深圳成A 0.917 均 值 0.93075 2． 就混合正态模型而言利用中国证券市场主要指数的标准化收益率估计出两个正态分布的参数值与概率值，结果如下表： 正态一 正态二 P值 均值1 标准差1 均值2 标准差2 上证综指 0.107349 0.711383 -0.214843 1.843125 0.775636 上证A指 0.107312 0.714149 -0.229415 1.864664 0.782019 深圳成份 0.044483 0.822448 -0.144945 2.196894 0.860187 深圳成A 0.057668 0.759801 -0.153388 1.795529 0.774296 3． 结合中国证券市场四个主要指数的标准化收益率提出了广义误差分布模型的参数v值，结果如下：上海综合的v=1.14； 深圳成份的v=1.19；上海A指的v=1.13；成份A指的v=1.24。但与其他发展中国家证券市场的v值相比，我国的证券市场稳定性较好，极端事件出现的概率相对较少。 4． 将AIC准则尝试性地应用到ARCH类模型的定阶中来，并取得了较好的效果；

We propose a new diagnostic test for linear and nonlinear time series models,using a generalized spectral approach+ Under a wide class of time series models that includes autoregressive conditional heteroskedasticity (ARCH) and autoregressive conditional duration (ACD) models, the proposed test enjoys the appealing“nuisance-parameter-free” property in the sense that model parameter estimation uncertainty has no impact on the limit distribution of the test statistic+ It is consistent against any type of pairwise serial dependence in the model standardized residuals and allows the choice of a proper lag order via data-driven methods. Moreover, the new test is asymptotically more efficient than the correlation integral?based test of Brock, Hsieh, and LeBaron (1991, Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence) and Brock, Dechert, Scheinkman, and LeBaron (1996, Econometric Reviews 15, 197?235), the well-known BDS test, against a class of plausible local alternatives (not including ARCH). A simulation study compares the finite-sample performance of the proposed test and the tests of BDS, Box and Pierce (1970, Journal of the American Statistical Association 65, 1509?1527), Ljung and Box (1978, Biometrika 65, 297?303), McLeod and Li (1983, Journal of Time Series Analysis 4, 269?273), and Li and Mak (1994, Journal of Time Series Analysis 15, 627? 636). The new test has good power against a wide variety of stochastic and chaotic alternatives to the null models for conditional mean and conditional variance. It can play a valuable role in evaluating adequacy of linear and nonlinear time series models. An empirical application to the daily S&P 500 price index highlights the merits of our approach.