Markov regime-switching quantile regression models and financial contagion detection

In this paper, we propose a Markov regime-switching quantile regression model, which considers the case where there may exist equilibria jumps in quantile regression. The parameters are estimated by the maximum likelihood estimation (MLE) method. A simulation study of this new model is conducted covering many scenarios. The simulation results show that the MLE method is efficient in estimating the model parameters. An empirical analysis is also provided, which focuses on the detection of financial crisis contagion between United States and some European Union countries during the period of sub-prime crisis from the angle of financial risk. The degree of financial contagion between markets is subsequently measured by utilizing the quantile regression coefficients. The empirical results show that in a crisis situation, the interdependence between United States and European Union countries dramatically increases.     

基于局部相关系数的美国次贷危机传染分析

全球经济金融一体化的不断深入使得全球性的金融危机频频爆发。因此,金融危机传染的分析与检验便变得十分重要。本文首先运用非参数回归模型,通过局部多项式方法对股指收益率之间的局部相关系数进行估计,并在不同的置信水平下对局部相关系数的变化进行假设检验,通过定量地判断局部相关系数是否突然增大来检验危机传染的存在性,同时简单地刻画了危机传染的程度,并且指出了危机传染的具体时间段。最后应用上述方法对美国次债危机在各个国家或地区之间的传染效应进行了实证检验,证实了本文给出的检验方法的可行性,并得到了一些有意义的结论。     

Worst-case optimal investment with a random number of crashes

We study a portfolio optimization problem in a market which is under the threat of crashes. At random times, the investor receives a warning that a crash in the risky asset might occur. We construct a strategy which renders the investor indifferent about an immediate crash of maximum size and no crash at all. We then verify that this strategy outperforms every other trading strategy using a direct comparison approach. We conclude with numerical examples and calculating the costs of hedging against crashes.     

Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II

Electre is an important outranking method developed in the area of decision-aiding. Data mining is a vital developing technique that receives contributions from lots of disciplines such as databases, machine learning, information retrieval, statistics, and so on. Techniques in outranking approaches, e.g. Electre, could also contribute to the development of data mining. In this research, we address the following two issues: a) why and how to combine Electre with case-based reasoning (CBR) to generate corresponding hybrid models by extending the fundamental principles of Electre into CBR; b) the effect on predictive performance by employing evidence vetoing the assertion on the base of evidence supporting the assertion. The similarity measure of CBR is implemented by revising and fulfilling three basic ideas of Electre, i.e. assertion that two cases are indifferent, evidence supporting the assertion, and evidence vetoing the assertion. Two corresponding CBR models are constructed by combining principles of the Electre decision-aiding method with CBR. The first one, named Electre-CBR-I, derives from evidence supporting the assertion. The other one, named Electre-CBR-II, derives from both evidence supporting and evidence vetoing the assertion. Leave-one-out cross-validation and hold-out method are integrated to form 30-times hold-out method. In financial distress mining, data was collected from Shanghai and Shenzhen Stock Exchanges, ANOVA was employed to select features that are significantly different between companies in distress and health, 30-times hold-out method was used to assess predictive performance, and grid-search technique was utilized to search optimal parameters. Original data distributions were kept in the experiment. Empirical results of long-term financial distress prediction with 30 initial financial ratios and 135 initial pairs of samples indicate that Electre-CBR-I outperforms Electre-CBR-II and other comparative CBR models, and Electre-CBR-II outperforms the other comparative CBR models.     

Joint logistics and financial services by a 3PL firm

Integrated logistics and financial services have been practiced by third party logistics (3PL) firms for years; however, the literature has been silent on the value of 3PL firms as credit providers in budget-constrained supply chains. This paper investigates an extended supply chain model with a supplier, a budget-constrained retailer, a bank, and a 3PL firm, in which the retailer has insufficient initial budget and may borrow or obtain trade credit from either a bank (traditional role) or a 3PL firm (control role). Our analysis indicates that the control role model yields higher profits not only for the 3PL firm but also for the supplier, the retailer, and the entire supply chain. In comparison with a supplier credit model where the supplier provides the trade credit, the control role model yields a better performance for the supply chain as long as the 3PL firm’s marginal profit is greater than that of the supplier. We further demonstrate that, for all players, both the control role and supplier credit models can outperform the classic newsvendor model without budget constraint.     

No-arbitrage bounds for financial scenarios

We derive no-arbitrage bounds for expected excess returns to generate scenarios used in financial applications. The bounds allow to distinguish three regions: one where arbitrage opportunities will never exist, a second where arbitrage may be present, and a third, where arbitrage opportunities will always exist. No-arbitrage bounds are derived in closed form for a given covariance matrix using the least possible number of scenarios. Empirical examples illustrate the practical potential of knowing these bounds.     

信用传染违约Aalen加性风险模型

本文考虑了基于加性风险模型的信用风险违约预报模型,不但考虑了宏观因素和公司个体因素,并且通过引入行业因素来刻画公司间可能存在的不同于宏观因素的信用传染效应,由此克服了以往模型对违约相关性的低估.本文在参数加性风险模型下给出极大似然估计及渐近性,提出两种估计方法并比较二者表现,得到最优权估计更加有效.同时本文还考虑了半参数的风险模型,并基于鞅的估计方程得到其估计及渐近性,均得到不错的结果.     

Heuristic option pricing with neural networks and the neuro-computer synapse 3

Today’s option and warrant pricing is based on models developed by Black, Scholes and Merton in 1973 and Cox, Ross and Rubinstein in 1979. The price movement of the underlying asset is modeled by continuous-time or discrete-time stochastic processes. Unfortunately these models are based on severely unrealistic assumptions. Permanently an unsatisfactory and quite artificial adaption to the true market conditions is necessary (future volatility of the underlying price). Here, an alternative heuristic approach with a highly accurate neural network approximation is presented. Market prices of options and warrants and the values of the influence variables form the usually very large output/ input data set. Thousands of multi-layer perceptrons with various topologies and with different weight initializations are trained with a fast sequential quadratic programming (SQP) method. The best networks are combined to an expert council network to synthesize market prices accurately. All options and warrants can be compared to single out overpriced and underpriced ones for each trading day. For each option and warrant overpriced and underpriced trading days can be used to ascertain a better buy and sell timing. Furthermore the neural model gains deep insight into the market price sen-sitivities (option Greeks), e.g., ?, Г, Θ and Ω. As an illustrative example we inves-tigate BASF stock call warrants. Time series from the beginning of 1996 to mid 1997 of 74 BASF call warrant prices at the Frankfurter Wertpapierborse (Frankfurt Stock Exchange) form the data basis. Finally a possible speed up of the training with the neuro-computer SYNAPSE 3 is briefly discussed     

Treatment of kurtosis in financial markets

Since Mandelbrot (1963) [2] highlighted the fact that data on the yield of financial assets exhibit leptokurtosis, different distributions have been presented as alternatives to the normal distribution. So far little consideration has been given to the capacity that these distributions have to recover the kurtosis of the sample data. Our work aims to present distributions which, given the broad range of their kurtosis, have the capacity to perform adjustment on many occasions where other distributions fail, while also being capable of recovering the peakedness of the empirical data. Another key characteristic of these distributions is that they are defined within a bounded domain in the same way as the sample data. An empirical application of these distributions is presented within the financial field by using daily returns.