Riesgo operacional en el proceso de pago del Procampo Un enfoque bayesiano

This paper identifies and quantifies through a Bayesian Network model (BN) the various factors of Operational Risk (OR) associated with the payment process PROCAMPO. The BN model is calibrated with data from events that occurred during the period 2008-2011. Unlike classical methods, the BN model calibration sources include both objective and subjective ones, allowing to more adequately capture the relationship (cause and effect) between the several elements of operational risk.     

Regulators as ‘agents’: power and personality in risk regulation and a role for agent‐based simulation

We critically examine how evidence and knowledge are brokered between the various actors (agents) in regulatory decisions on risk. Following a précis of context and regulatory process, we explore the role power and personality might play as evidence is synthesised and used to inform risk decisions, providing a review of the relevant literature from applied psychology, agent‐based simulation and regulatory science. We make a case for the adoption of agent‐based tools for addressing the sufficiency of evidence and resolving uncertainty in regulatory decisions. Referring to other environmental applications of agent‐based decision‐making, we propose how an agent model might represent power structures and personality characteristics with the attending implications for the brokering of regulatory science. This critical review has implications for the structuring of evidence that informs environmental decisions and the personal traits required of modern regulators operating in facilitative regulatory settings.     

银行业面对互联网金融挑战的应对策略——基于大数据背景下的思考

伴随着信息技术及互联网的快速发展,社会正在走向全面数字化。大数据、云计算等技术的异军突起使得居民的生活行为方式和企业的生产经营方式都发生了显著变化,科技进步带来的数字化变革正在影响并形成全新的生活习惯和商业模式。在这种大背景下,以网络为载体的互联网金融改变了金融运营模式,传统的银行业正在面临严峻考验。商业银行必须全面认识大数据技术变革带来的影响,转变经营策略,积极应对互联网金融带来的深刻变革。     

An analysis of a three-factor model proposed by the Danish Society of Actuaries for forecasting and risk analysis

This paper provides the explicit solution to the three-factor diffusion model recently proposed by the Danish Society of Actuaries to the Danish industry of life insurance and pensions. The solution is obtained by use of the known general solution to multidimensional linear stochastic differential equation systems. With offset in the explicit solution, we establish the conditional distribution of the future state variables which allows for exact simulation. Using exact simulation, we illustrate how simulation of the system can be improved compared to a standard Euler scheme. In order to analyze the effect of choosing the exact simulation scheme over the traditional Euler approximation scheme frequently applied by practitioners, we carry out a simulation study. We show that due to its recursive nature, the Euler scheme becomes computationally expensive as it requires a small step size in order to minimize discretization errors. Using our exact simulation scheme, one is able to cut these computational costs significantly and obtain even better forecasts. As probability density tail behavior is key to expected investment portfolio performance, we further conduct a risk analysis in which we compare well-known risk measures under both schemes. Finally, we conduct a sensitivity analysis and find that the relative performance of the two schemes depends on the chosen model parameter estimates.     

互联网金融背景下的商业银行业务模式创新--以高校学生贷款为例

从互联网金融的定义和特点出发,总结互联网金融的两大优势,即大规模数据处理能力和资源配置过程“去中介化”,这对传统商业银行产生了较大冲击。鉴于此,互联网金融浪潮下传统商业银行的一个可能发展机遇是,针对中小企业开展大数据征信和网络贷款业务。又以互联网金融模式下的助学贷款业务为例,展示了大数据征信和网络贷款的具体应用方案。     

基于蒙特卡罗模拟的多标的资产期权定价研究

在单标的资产价格随机模型的基础上,推导了具相关性的多标的资产价格的随机过程公式,以此构造蒙特卡罗模拟高维欧式期权定价的随机模型,给出模拟算法,并分析了影响蒙特卡罗模拟效果的几个关键因素.模拟算例的结果显示模拟效果较好.     

亚式期权定价的模拟方法研究

由于算术平均价格亚式期权的定价没有解析公式，所以文章用Monte Carlo模拟方法通过Matlab软件编写程序对亚式期权进行了定价。发现在某些情况下，亚式期权的价值并不是国内外一些研究者所认为的低于相应的欧式期权的价值。     

Valuation Ratios and Stock Return Predictability in South Africa: Is It There?

Using monthly South African data for January 1990 through October 2009, this paper, to the best of our knowledge, is the first to examine the predictability of real stock return based on valuation ratios, namely, price-dividend and price-earnings ratios. We cannot detect either short-horizon or long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at both short and long horizons based on bootstrapped critical values constructed from linear representations of the data. We find, via Monte Carlo simulations, that the power to detect predictability in finite samples tends to decrease at long horizons in a linear framework. Although Monte Carlo simulations applied to exponential smooth-transition autoregressive models of the price-dividend and price-earnings ratios show increased power, the ability of the nonlinear framework in explaining the pattern of stock return predictability in the data does not show any promise at either short or long horizons, just as in the linear predictive regressions.     

A computational definition of financial randomness

The aim of this study is to present an efficient and easy framework for the application of the Least Squares Monte Carlo methodology to the pricing of gas or power facilities as detailed in Boogert and de Jong [J. Derivatives, 2008, 15, 81–91]. As mentioned in the seminal paper by Longstaff and Schwartz [Rev. Financ. Stud. 2001, 113–147], the convergence of the Least Squares Monte Carlo algorithm depends on the convergence of the optimization combined with the convergence of the pure Monte Carlo method. In the context of the energy facilities, the optimization is more complex and its convergence is of fundamental importance in particular for the computation of sensitivities and optimal dispatched quantities. To our knowledge, an extensive study of the convergence, and hence of the reliability of the algorithm, has not been performed yet, in our opinion this is because the apparent infeasibility and complexity uses a very high number of simulations. We present then an easy way to simulate random trajectories by means of diffusion bridges in contrast to Dutt and Welke [J. Derivatives, 2008, 15 (4), 29–47] that considers time-reversal Itô diffusions and subordinated processes. In particular, we show that in the case of Cox-Ingersoll-Ross and Heston models, the bridge approach has the advantage to produce exact simulations even for non-Gaussian processes, in contrast to the time-reversal approach. Our methodology permits performing a backward dynamic programming strategy based on a huge number of simulations without storing the whole simulated trajectory. Generally, in the valuation of energy facilities, one is also interested in the forward recursion. We then design backward and forward recursion algorithms such that one can produce the same random trajectories by the use of multiple independent random streams without storing data at intermediate time steps. Finally, we show the advantages of our methodology for the valuation of virtual hydro power plants and gas storages.     

大数据应用于信用评分模型的实践与启示

传统征信业务必因大数据而发生改变,大数据将为现有征信体系增加海量数据来源并推动普惠金融的发展。但是,由于存在个人隐私权保护、信贷风险控制及管理等限制因素,大数据技术最终如何实现与征信业务的完美结合以及究竟对传统征信业带来何种程度的影响,仍需要时间的检验。     

Internet Finance: Its Uncertain Legal Foundations and the Role of Big Data in Its Development

Internet finance has made significant progress in China. At the same time, it also suffers from legal gaps and inconsistencies. Traditionally, legislation regulates the emerging internet financial market by distinguishing between legal and illegal activities. Users of internet finance engage in regulatory arbitrage and pursue short-term profits, which distort the market. Regulations over internet finance should conform to market logic and utilize informational mechanisms and big data to reduce fraudulent information and market friction, ensuring market transparency, competition, and fair pricing.