Development and calibration of a currency trading strategy using global optimization

We have developed a new financial indicator—called the Interest Rate Differentials Adjusted for Volatility (IRDAV) measure—to assist investors in currency markets. On a monthly basis, we rank currency pairs according to this measure and then select a basket of pairs with the highest IRDAV values. Under positive market conditions, an IRDAV based investment strategy (buying a currency with high interest rate and simultaneously selling a currency with low interest rate, after adjusting for volatility of the currency pairs in question) can generate significant returns. However, when the markets turn for the worse and crisis situations evolve, investors exit such money-making strategies suddenly, and—as a result—significant losses can occur. In an effort to minimize these potential losses, we also propose an aggregated Risk Metric that estimates the total risk by looking at various financial indicators across different markets. These risk indicators are used to get timely signals of evolving crises and to flip the strategy from long to short in a timely fashion, to prevent losses and make further gains even during crisis periods. Since our proprietary model is implemented in Excel as a highly nonlinear “black box” computational procedure, we use suitable global optimization methodology and software—the Lipschitz Global Optimizer solver suite linked to Excel—to maximize the performance of the currency basket, based on our selection of key decision variables. After the introduction of the new currency trading model and its implementation, we present numerical results based on actual market data. Our results clearly show the advantages of using global optimization based parameter settings, compared to the typically used “expert estimates” of the key model parameters.     

The economy and loss given default: evidence from two UK retail lending data sets

Loss given default (LGD) models predict losses as a proportion of the outstanding loan, in the event a debtor goes into default. The literature on corporate sector LGD models suggests LGD is correlated to the economy and so changes in the economy could translate into different predictions of losses. In this work, the role of macroeconomic variables in loan-level retail LGD models is examined by testing the inclusion of macroeconomic variables in two different retail LGD models: a two-stage model for a residential mortgage loans data set and an ordinary least squares model for an unsecured personal loans data set. To improve loan-level predictions of LGD, indicators relating to the macroeconomy are considered with mixed results: the selected macroeconomic variable seemed able to improve the predictive performance of mortgage loan LGD estimates, but not for personal loan LGD. For mortgage loan LGD, interest rate was most beneficial but only predicted better during downturn periods, underestimating LGD during non-downturn periods. For personal loan LGD, only net lending growth is statistically significant but including this variable did not bring any improvement to R².     

Modelling banking sector stability with multicriteria approaches

Banking crises can be damaging for the economy, and as the recent experience has shown, nowadays they can spread rapidly across the globe with contagious effects. Therefore, the assessment of the stability of a county’s banking sector is important for regulators, depositors, investors and the general public. In the present study, we propose the development of classification models that assign the banking sectors of various countries in three classes, labelled “low stability”, “medium stability”, and “high stability”. The models are developed using three multicriteria decision aid techniques, which are well-suited to ordinal classification problems. We use a sample of 114 banking sectors (i.e., countries), and a set of criteria that includes indicators of the macroeconomic, institutional and regulatory environment, as well as basic characteristics of the banking and financial sector. The models are developed and tested using a tenfold cross-validation approach and they are benchmarked against models developed with discriminant analysis and logistic regression.     

基于时间相依Cox回归的动态财务预警模型及实证

本文以中小企业为研究对象,从偿债能力、盈利能力等财务因素,加之公司治理、宏观环境等非财务因素出发,利用共线性检验和时间相依Cox回归构建动态财务预警模型,并与经典的Cox模型、logit模型进行对比分析。本研究的特色有二:一是通过时间相依Cox回归模型,构建随时间而变化的预警指标数据与财务危机之间的函数关系。利用偏似然估计、Breslow估计量分别拟合回归系数和基准危险强度,构建财务预警模型,预测企业在未来一段时间内每个时间点上的财务危机概率。相比于基于传统Cox模型的预警研究仅用一期的截面数据建模,本研究考虑了预警指标的动态变化对财务风险的影响,涵盖了更多的历史信息,达到提高预警精度的目的。二是考虑第一类错误“危机企业判为正常”与第二类错误“正常企业判为危机”给投资者造成的损失差异,衡量预警的“错误成本”,以错误成本最低为目标,反推出财务正常和财务危机之间的预警阈值,实现了对财务危机发生与否的提前预警功能。经过实证,本研究的财务预警模型精度较高,尤其对财务危机企业的正确识别率达到75%。相较于传统的Cox回归、logit模型,危机企业的正确识别率更高、错误成本更低。盈利能力、公司治理水平是对企业财务风险影响最为显著的因素。     

A scenario-based stochastic model for supplier selection in global context with multiple buyers,currency fluctuation uncertainties,and price discounts

Suppliers network in the global context under price discounts and uncertain fluctuations of currency exchange rates have become critical in today’s world economy. We study the problem of suppliers’ selection in the presence of uncertain fluctuations of currency exchange rates and price discounts. We specifically consider a buyer with multiple sites sourcing a product from heterogeneous suppliers and address both the supplier selection and purchased quantity decision. Suppliers are located worldwide and pricing is offered in suppliers’ local currencies. Exchange rates from the local currencies of suppliers to the standard currency of the buyer are subject to uncertain fluctuations overtime. In addition, suppliers offer discounts as a function of the total quantity bought by the different customer’ sites over the time horizon irrespective of the quantity purchased by each site.     

Prediction of financial distress: An empirical study of listed Chinese companies using data mining

The deterioration in profitability of listed companies not only threatens the interests of the enterprise and internal staff, but also makes investors face significant financial loss. It is important to establish an effective early warning system for prediction of financial crisis for better corporate governance. This paper studies the phenomenon of financial distress for 107 Chinese companies that received the label ‘special treatment’ from 2001 to 2008 by the Shanghai Stock Exchange and the Shenzhen Stock Exchange. We use data mining techniques to build financial distress warning models based on 31 financial indicators and three different time windows by comparing these 107 firms to a control group of firms. We observe that the performance of neural networks is more accurate than other classifiers, such as decision trees and support vector machines, as well as an ensemble of multiple classifiers combined using majority voting. An important contribution of the paper is to discover that financial indicators, such as net profit margin of total assets, return on total assets, earnings per share, and cash flow per share, play an important role in prediction of deterioration in profitability. This paper provides a suitable method for prediction of financial distress for listed companies in China.     

Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case

The objective of this paper is to propose a methodological framework for constructing the integrated early warning system (IEWS) that can be used as a decision support tool in bank examination and supervision process for detection of banks, which are experiencing serious problems. Sample and variable set of the study contains 40 privately owned Turkish commercial banks (21 banks failed during the period 1997–2003) and their financial ratios. Well known multivariate statistical technique (principal component analysis), was used to explore the basic financial characteristics of the banks, and discriminant, logit and probit models were estimated based on these characteristics to construct IEWS. Also, importance of early warning systems in bank examination was evaluated with respect to cost of failure. Results of the study show that, if IEWS was effectively employed in bank supervision, it can be possible to avoid from the bank restructuring costs at a significant amount of rate in the long run.     

Credit scoring with macroeconomic variables using survival analysis

Survival analysis can be applied to build models for time to default on debt. In this paper, we report an application of survival analysis to model default on a large data set of credit card accounts. We explore the hypothesis that probability of default (PD) is affected by general conditions in the economy over time. These macroeconomic variables (MVs) cannot readily be included in logistic regression models. However, survival analysis provides a framework for their inclusion as time-varying covariates. Various MVs, such as interest rate and unemployment rate, are included in the analysis. We show that inclusion of these indicators improves model fit and affects PD yielding a modest improvement in predictions of default on an independent test set.     

Currency Denomination in Long-Term Debt Financing and Refinancing: A Cross- Hedging Paradigm

Under conditions of chronic exchange rate overshooting and mildly segmented capital markets, optimal currency denomination decision rules for international debt financing are derived for risk-neutral and risk-averse borrowers. For the latter, an inter-temporal expected utility framework yields the risk-adjusted cost of foreign debt, which allows for the pricing of currency cross-hedging effects in multi-currency debt portfolios, artificial currency unit-denominated debt instruments as well as currency swaps.     

Markov-modulated jump-diffusions for currency option pricing

This paper introduces dynamic models for the spot foreign exchange rate with capturing both the rare events and the time-inhomogeneity in the fluctuating currency market. For the rare events, we use a compound Poisson process with log-normal jump amplitude to describe the jumps. As for the time-inhomogeneity in the market dynamics, we particularly stress the strong dependence of the domestic/foreign interest rates, the appreciation rate and the volatility of the foreign currency on the time-varying sovereign ratings in the currency market. The time-varying ratings are formulated by a continuous-time finite-state Markov chain. Based on such a spot foreign exchange rate dynamics, we then study the pricing of some currency options. Here we will adopt a so-called regime-switching Esscher transform to identify a risk-neutral martingale measure. By determining the regime-switching Esscher parameters we then get an integral expression on the prices of European-style currency options. Finally, numerical illustrations are given.     

不正常航班管理的可拓预警模型

本文从危机管理的角度。采用可拓学的思想和方法，分析导致航班计划运行发生不正常的危机信号的发散性、可扩性、相关性及蕴含性，建立预警模型对相关信息进行收集、监控和分析，为防范危机发生和减少危机损失提供形式化的可拓处理方法。     

短期出口信用保险报损率宏观经济影响因素研究

影响短期出口信用保险报损率的主要因素是海外信用风险水平.选取欧洲地区工业增加值、进口额、货币供应量、利率、汇率等宏观经济指标作为研究欧洲地区的短期险报损率的解释变量,探讨其宏观经济因素同报损率的内在关系.利用偏最小二乘方法建立模型,实证结果表明,工业增加值、进口额、货币供应量、利率与报损率有负相关关系,美元兑欧元汇率与报损率有正相关关系.     

Shaping the manufacturing industry performance: MIDAS approach

We aim to find out whether the exchange rate (against US dollar) or the interest rate (in local currency) is a better variable in predicting the capacity utilization rate of manufacturing industry (CUR) of Turkey after the 2008 global financial crisis. In that manner, we implement dynamic mixed data sampling (MIDAS) regression model to forecast monthly changes in CUR by using daily changes in the exchange rate and the interest rate separately. The results show that exchange rate has a better forecast performance suggesting that it is a stronger determinant in shaping the manufacturing industry.     

不确定需求下的企业最优外汇持有量模型研究

把外汇作为存货看待,考虑了外汇的持有成本、转换成本、汇率风险,外汇存款利率以及利息税率等因素,通过建立数学模型来研究不确定需求下的企业最优外汇持有量问题.     

宏观调控“两难”新环境下中国CPI运行定量分析——基于改进的BP神经网络

当前,我国经济继续回升向好,但宏观调控面临的"两难"问题增多.如何防止经济快速下滑和防范通货膨胀成为当前宏观经济决策的重要内容,准确预见我国价格运行趋势对宏观决策具有十分重要的意义.1997年和2008年分别发生了对世界经济发展影响深远的两次金融危机.两次金融危机发生后,中国政府均实施了大规模的经济刺激计划,但反映经济运行的主要宏观经济指标CPI月度指数从危机发生当年的次年开始,步入了长达数月负区间运行.研究发现1997年金融危机发生前后月度CPI指数运行相关度非常高,2008年金融危机发生当年月度CPI指数与2007年、2009年月度CPI指数运行相关度也非常高.对此,运用改进的BP神经网络模型对1995-1999年中国月度CPI指数进行学习,以2006-2009年月度CPI指数数据为基础,对2010年中国下半年月度CPI指数进行了预测,显示全年CPI指数预计为102.85.对此,认为2010年我国面临适度的通胀压力,提出了应对政策建议.     

A non random walk theory of exchange rate dynamics with applications to option pricing

A two dimensional stochastic process is developed to model exchange rate dynamics. We incorporate the non random walk influence of pur–chasing power parity, to synthesise the theories of international trade and foreign currency options. Our results, which include a closed form expression for the transition density function of the exchange rate and an exact formula to price currency options, offer a theoretical framework for further study of foreign exchange markets     

中国企业OFDI研究：基于制度质量和政府参与的视角

在分析传统对外直接投资决定理论基础上,揭示了母国制度因素和政府作用对于发展中国家和转型经济体OFDI的重要作用.以中国企业OFDI为例,建立计量经济学模型,检验和分析了中国制度质量和政府参与两类变量与OFDI之间的长期均衡关系.研究发现:表征制度质量的法律与秩序、政府稳定性、官僚体系质量及腐败等四个基本因素和表征政府参与的所有权程度与政府政策两个变量均对中国企业OFDI产生了显著正向影响,控制变量中的经济增长对OFDI有的影响是正向的,汇率和出口依存度对OFDI的影响是负向的.据此,提出要通过加强对国家经济风险的评估预警系统建设、建立健全双边和多边投资保护体系等来化解国家经济风险的不利影响.     

A comparison of dynamic hazard models and static models for predicting the special treatment of stocks in China with comprehensive variables

The stock exchanges in China give a stock special treatment in order to indicate its risk warning if the corresponding listed company cannot meet some requirements on financial performance. To correctly predict the special treatment of stocks is very important for the investors. The performance of the prediction models is mainly affected by the selection of explanatory variables and modelling methods. This paper makes a comparison between the multi-period hazard models and five widely used single-period static models by investigating a comprehensive category of variables including accounting variables, market variables, characteristic variables and macroeconomic variables. The empirical result shows that the performance of the models is sensitive to the choice of explanatory variables but the performance between the multi-period hazard models and the single-period static models has no significant difference.     

Treasury Management Model with Foreign Exchange Exposure

In this paper we formulate a model for foreign exchange exposure management and (international) cash management taking into consideration random fluctuations of exchange rates. A vector error correction model (VECM) is used to predict the random behaviour of the forward as well as spot rates connecting dollar and sterling. A two-stage stochastic programming (TWOSP) decision model is formulated using these random parameter values. This model computes currency hedging strategies, which provide rolling decisions of how much forward contracts should be bought and how much should be liquidated.The model decisions are investigated through ex post simulation and backtesting in which value at risk (VaR) for alternative decisions are computed. The investigation (a) shows that there is a considerable improvement to “spot only” strategy, (b) provides insight into how these decisions are made and (c) also validates the performance of this model.     

比较和分析不同计算法中的人民币有效汇率

本文在用商品贸易权重计算有效汇率的基础上，探讨了用资本移动权重和债务权重计算有效汇率的新模型，并调查和实证分析了由这些模型决定的人民币有效汇率时序列运动以及它们与宏观经济变量之间的长期关系，以解决外汇管理中人民币升值压力上升问题。我们得出：在我国，基于贸易权重的实际有效汇率与基于债务权重的人民币实际有效汇率有较高的相关性，但基于贸易权重的与基于资本移动权重的没有相关性的结论。因此，我们认为在人民币升值压力较大时，货币当局可以用调整我国对外债务结构来替代词整对外贸易差额。