基于企业基因重组理论的价值网络构建研究

全球化、信息化带来市场交易成本的急剧下降，并直接导致组织创造价值的方式发生了变化。本文借鉴企业基因和企业基因组的概念，对企业基因重组理论进行了系统地完善和整合。依此理论为基础，本文对能力要素驱动型价值网络的形成、特征和构建策略进行了深入地分析。     

Developing a Community of Practice: Michael Gaffikin and Critical Accounting Research

This paper demonstrates the role of a community of practice in academic endeavour, focusing on the influence of place and the role of thought leaders in guiding academic development. This is illustrated with reference to the influence of Emeritus Professor Michael Gaffikin in establishing a critical accounting community of practice at the University of Wollongong (UOW) through his PhD supervisions. Social network analysis (SNA) is used to visualize the 43 PhD supervisions undertaken by Gaffikin during his career, and subsequent PhD supervisions of his students, and students of those students. SNA illustrates the structure of relationships, and the paths through which scholars learnt from one another, which we combine with qualitative analysis of recollections, acknowledgments, and doctoral theses. We demonstrate the role of Gaffikin, as the intellectual thought leader, and UOW, as the intellectual place, in the development of the critical accounting community of practice. The development of critical accounting scholarship was a function of Gaffikin's intellectual and professional leadership, which he executed through PhD supervision, the annual Doctoral Consortium, and his direction at UOW. This paper highlights the importance of local communities for the development of research agendas, and the influence of PhD supervisors on the professional development of students.     

Moments of renewal shot-noise processes and their applications

In this paper, we study the family of renewal shot-noise processes. The Feynmann–Kac formula is obtained based on the piecewise deterministic Markov process theory and the martingale methodology. We then derive the Laplace transforms of the conditional moments and asymptotic moments of the processes. In general, by inverting the Laplace transforms, the asymptotic moments and the first conditional moments can be derived explicitly; however, other conditional moments may need to be estimated numerically. As an example, we develop a very efficient and general algorithm of Monte Carlo exact simulation for estimating the second conditional moments. The results can be then easily transformed to the counterparts of discounted aggregate claims for insurance applications, and we apply the first two conditional moments for the actuarial net premium calculation. Similarly, they can also be applied to credit risk and reliability modelling. Numerical examples with four distribution choices for interarrival times are provided to illustrate how the models can be implemented.     

Managing and financing metropolitan public services in China: experience of the Pearl River Delta region

Under China’s current fiscal policies and inter-governmental relations, it is a significant challenge to finance and deliver public services across jurisdictions. This challenge was met in the Pearl River Delta region in southern China with a collaborative governance approach. Directives from higher-level governments and horizontal inter-city fiscal arrangements were successfully combined to deliver public services. Effective networks should be developed to improve co-ordination and collaboration in delivering cross-jurisdictional public services.     

保险公司市场风险经济资本度量研究

经济资本(EC)是在既定期间和置信水平下,公司根据实际承担的风险计算的用以吸收非预期损失的资本额度,目前市场风险是整体经济资本测算体系中最为突出的风险.根据当前保险运营与资产投资的比例特征,同时对资产端与负债端建立市场风险投资模型,采用嵌套随机模拟方法进行两阶段情景生成,度量未来一年内不同风险测度下的市场风险经济资本需求,并对比不同情景数量下的测算稳定性.结果证明:随着内部或外部情景模拟次数的增加,市场风险经济资本测算结果对于极端风险的预测趋于稳定,在内外部情景数量乘积相同时运算时间基本一致.当内外部两阶段情景生成参数差异较大的情形下,应适当增加情景生成数量,以确保对于极端风险预测的准确性.     

Optimal pair-trading strategy over long/short/square positions—empirical study

Suzuki [Automatica, 2016, 67, 33–45] solves the optimal, finitely iterative, three-regime switching problem for investing in a mean-reverting asset that follows an Ornstein–Uhlenbeck price process and find explicit solutions. The remarkable feature of this model is that the investor can explicitly take either a long, short or square position and can switch the position, with transaction costs, during the investment period. We run empirical simulations of such multiple-regime switching models. There are very few such attempts in the existing literature because it is difficult to find, first, an explicit solution to the problem and second, appropriate financial assets that follow the artificial stochastic process required by the mathematical model. According to the Monte Carlo simulations of the optimal pair-trading strategy, the mean daily Sharp ratio is more than 2.3, whereas the mean Sharp ratio for the historical simulation of the ‘stub’ pairs (combinations of parent/subsidiary companies) is 0.6886. We believe that the results obtained from performing the empirical simulations are remarkable and consider that the optimal switching strategy of the rigorous mathematical model is applicable to businesses in the real world. For the reference many pseudo-program codes are added, which can help to replicate the optimal trading strategies.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

Export Sophistication: A Dynamic Panel Data Approach

In this article, we analyze export sophistication based on a large panel dataset (2001–2015; 101 countries) and using various estimation algorithms. Using Monte Carlo simulations, we evaluate the bias properties of estimators and show that GMM-type estimators outperform instrumental-variable and fixed-effects estimators. Based on our analysis we document that GDP per capita and the size of the economy exhibit significant and positive effects on export sophistication; weak institutional quality exhibits negative effect. We also show that export sophistication is path-dependent and stable even during a major economic crisis, which is especially important for emerging and developing economies.     

On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

Daily and weekly seasonalities are always taken into account in day-ahead electricity price forecasting, but the long-term seasonal component has long been believed to add unnecessary complexity, and hence, most studies have ignored it. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, this framework is based on linear models estimated using ordinary least squares. This paper shows that considering non-linear autoregressive (NARX) neural network-type models with the same inputs as the corresponding SCAR-type models can lead to yet better performances. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can outperform the latter significantly.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.