A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants

Modelling loss severity from rare operational risk events with potentially catastrophic consequences has proved a difficult task for practitioners in the finance industry. Efforts to develop loss severity models that comply with the BASEL II Capital Accord have resulted in two principal model directions where one is based on scenario generated data and the other on scaling of pooled external data. However, lack of relevant historical data and difficulties in constructing relevant scenarios frequently raise questions regarding the credibility of the resulting loss predictions. In this paper we suggest a knowledge based approach for establishing severity distributions based on loss determinants and their causal influence. Loss determinants are key elements affecting the actual size of potential losses, e.g. market volatility, exposure and equity capital. The loss severity distribution is conditional on the state of the identified loss determinants, thus linking loss severity to underlying causal drivers. We suggest Bayesian Networks as a powerful framework for quantitative analysis of the causal mechanisms determining loss severity. Leaning on available data and expert knowledge, the approach presented in this paper provides improved credibility of the loss predictions without being dependent on extensive data volumes.     

基于Hilbert-Huang变换的金融收益政策风险因子识别方法

针对政策可能对金融收益产生风险问题,提出了基于Hilbert-Huang变换方法的政策风险因子识别检测方法。通过经验模态分解,Hilbert-Huang频谱分析得到金融时间序列的时域和频域特征,通过与量化处理后的政策进行匹配得到政策产生的异常波动情况,从而实现对政策因子风险的识别与处理。研究结果对于探究宏观政策对金融收益的影响具有重要参考意义。最后以国家房地产调控政策与地产指数为算例,发现本研究提出的方法识别精度高,具有非常好的应用前景。     

世界经合组织金融素质测量工具的实用性评估——基于甘肃省辖集中连片特殊困难地区实地调查

为了推进国家间国民金融素质比较研究,世界经合组织(OECD)构建了标准化的跨国金融素质测量工具,目前该工具已被30多个国家采信,对于后发国家具有重要的借鉴意义。然而,金融素质是高度情境化的构念,测量工具的情境化是保证测量结果稳健可靠的必要环节。为此,本文实施了针对该工具措辞和计分体系的实用性调查,此次调查的对象分别为相关领域专家和甘肃省辖集中连片特殊困难地区农户,旨在为该工具中国化的路径选择提供佐证。调查结果显示,对于受访农户而言,该工具中“通货膨胀的认知”等5个问题的措辞过于复杂,需要进一步修正;不同类型专家对该工具各构成要件的重要性评估未形成一致意见,需要进一步探寻上述差异产生的原因及其可能的弥合方式。     

Entropy-Based Behavioural Efficiency of the Financial Market

The most known and used abstract model of the financial market is based on the concept of the informational efficiency (EMH) of that market. The paper proposes an alternative which could be named the behavioural efficiency of the financial market, which is based on the behavioural entropy instead of the informational entropy. More specifically, the paper supports the idea that, in the financial market, the only measure (if any) of the entropy is the available behaviours indicated by the implicit information. Therefore, the behavioural entropy is linked to the concept of behavioural efficiency. The paper argues that, in fact, in the financial markets, there is not a (real) informational efficiency, but there exists a behavioural efficiency instead. The proposal is based both on a new typology of information in the financial market (which provides the concept of implicit information—that is, that information ”translated” by the economic agents from observing the actual behaviours) and on a non-linear (more exactly, a logistic) curve linking the behavioural entropy to the behavioural efficiency of the financial markets. Finally, the paper proposes a synergic overcoming of both EMH and AMH based on the new concept of behavioural entropy in the financial market.     

具有增益的概率网络金融计划模型

本文绘出一类具有增益的概率网络金融计划模型．许多多阶段金融计划问题可纳入这类模型．在这类模型中,随机变量的分布函数与Alexander过滤交易规则密切联系在一起,金融市场交易信号由神经网络产生,目标函数的最优值按其期望值计算．文中提出临界流和临界路的概念,给出目标函数下界等于其期望值的充分必要条件和期望最优解的求解方法．     

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.     

分数阶对称金融模型复杂度分析及有限时间同步

分析了一类分数阶对称金融非线性系统的复杂度特性,利用有限时间同步理论设计控制器,实现了有限时间同步.根据分数阶系统定义和Adomain分解法对该系统的非线性项进行Adomain分解,结合分解系数定义系统的表达式,将其离散化.基于谱熵复杂度及C0复杂度的基本算法,利用Matlab仿真其复杂度曲线及复杂度图谱.为进一步探究...     

用循环伏安法和同步荧光光谱技术研究肌红蛋白的电化学行为

用循环伏安法和同步荧光光谱技术研究了肌红蛋白的电化学行为，实验结果表明，高铁肌红高蛋白分子至少存在的一个可调节分子构象变化的氧分子，而且长时间通入高纯氮气可以除掉高铁肌红蛋白分子内的这个氧，当高肌红蛋白分子内的氧被彻底除去后，用循环伏安法可以观察到肌红蛋白在三氧化二铟电极上的准可逆的电学反应，同步荧光光谱实验表明，高铁肌红蛋白在彻底除氧后，分子构象发生了变化，而且这种构象变化是可逆的。     

大连商品交易所大豆一号合约动态保证金研究

大连商品交易所大豆一号期货合约长期实行静态的保证金制度,但在国际期货市场中动态保证金制度是未来的发展趋势。国内目前缺乏对于大豆期货保证金制度的深入研究,在估计期货收益波动率时也没有使用滚动测算的思想。从基于GARCH和EGARCH两种模型、T分布和NORMAL两种分布假设的四种保证金设定方法,以及大豆一号合约收益数据的实证结果来看,大豆一号现行的静态保证金制度已不适应市场发展,保证金比例总体偏高,但在市场剧烈波动时又略显不足,综合考虑准确性和效率,以及投资者的交易成本EGARCH-T是最佳的保证金计算方法。     

Characteristic time scales of tick quotes on foreign currency markets: an empirical study and agent-based model

Power spectrum densities for the number of tick quotes per minute (market activity) on three currency markets (USD/JPY, EUR/USD, and JPY/EUR) for periods from January 1999 to December 2000 are analyzed. We find some peaks on the power spectrum densities at a few minutes. We develop the double-threshold agent model and confirm that stochastic resonance occurs for the market activity of this model. We propose a hypothesis that the periodicities found on the power spectrum densities can be observed due to stochastic resonance.