CDO Surfaces Dynamics

A semiparametric factor model for CDO surfaces dynamics

Implied volatility can be considered as a function of strike level and time to maturity. As it is calculated from the actual trading options, it contains dynamic, multi-dimensional information of options, modelling the implied volatility is an interesting task for researchers. Dynamic semiparametric factor models (DSFM) are used to model the implied volatility surface. It employs semiparametric factor functions and time variate loadings to reduce the dimensions of the data. This master thesis applies joint analysis with the time variate factor loadings resulted from DSFM, in order to discuss the relationship between index options and stock options. The data of DAX index option and its liquid components stock options will be applied in analysis. The result of the joint analysis shows, that the index option has long term relationship with its stock options. It is unlikely to disperse the risk by trading the stock options under the same index.

Leveraged ETF Options Implied Volatility Paradox: A Statistical Study

A Joint Analysis of the KOSPI 200 Option and ODAX Option Markets Dynamics

The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method.

For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.

Accurate mapping of leaf area index (LAI) is essential for mangrove conservation and restoration. This study proposes a new approach to the retrieval of the mangrove LAI by combining a one-dimensional convolutional neural network (1D-CNN) with adaptive ensemble learning regression (AELR) and deep learning regression (DNNR) algorithms. We further evaluated the performance of OHS (Zhuhai-1) hyperspectral and GF-3 SAR images in mapping the spatial distribution of the LAI in mangroves. Finally, the outputs of the AELR and DNNR models were interpreted, and the interactions between different image features were clarified to select the sensitive spectral ranges and vegetation indexes for estimating the mangrove LAI using SHAP (Shapley additive explanation). We confirmed that 1D-CNN + DNNR provided an effective approach to estimating the mangrove LAI, as it produced a higher-accuracy inversion (R 2 = 0.8685) than that of the AELR model. It was found in this study that the 1D-CNN improved the retrieval accuracy (R 2) of the mangrove LAI from 0.097 to 0.1297 when compared with the traditional data dimension reduction (DDR) method, which demonstrated that the 1D-CNN was able to improve the inversion accuracy of the mangrove LAI. This study revealed that the synergistic use of OHS hyperspectral and GF-3 SAR images (R 2 = 0.8685, RMSE = 0.134) outperformed any of the lone datasets in the inversion of the mangrove LAI. Finally, this study provided explanations and interpretations of the outputs of the DNNR and AELR models, and it was found that the optimal spectral ranges for estimating the mangrove LAI are 637–671 nm and 802–822 nm; H19 ((NIR/Red)/Red), the NDVI, H14 (Red edge/Red), and the EMVI ((Green-SWIR 2)/(SWIR1-Green)) provided important contributions for mapping the mangrove LAI. These results provide a scientific foundation for the preservation and restoration of coastal mangroves.

Feature selection is an important method of data dimensionality reduction widely used in machine learning. In this framework, the sparse representation based feature selection methods are very attractive. This is because of the nature of these methods which try to represent a data with as less as possible non-zero coefficients. In deep neural networks, a very high dimensional feature space is usually existed. In such a situation, one can take advantages of the feature selection approaches into account. In this paper, first, three sparse feature selection methods are compared. The Sparse Group Lasso (SGL) algorithm is one of the adopted approaches. This method is theoretically very well-organized and leads to good results for man-made features. The most important property of this method is that it highly induces the sparsity to the data. A main step of the SGL method is the features grouping step. In this paper, a k-means clustering based method is applied for grouping of the features. Our experimental results show that this sparse representation based method leads to very successful results in deep neural networks.

Aiming at the features that modem industrial processes always have some characteristics of complexity and nonlinearity and the process data usually contain both Gaussion and non-Gaussion information at the same time, a new process performance monitoring and fault diagnosis method based on subsystem division and kernel entropy component analysis (Sub-KECA) is proposed in this paper. KECA as a new method for data transformation and dimensionality reduction, which chooses the best principal component vector according to the maximal Renyi entropy rather than judging by the top eigenvalue and eigenvector of the kernel matrix simply. Besides, it can be optimized and anti-disturb due to the application of subsystem division. The proposed method is applied to process monitoring of the Tennessee Eastman(TE) process. The positive simulation results indicate that this method is more feasible and efficient when comparing with KPCA method and original KECA method.

Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And, the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.

Dimension reduction has been used to visualise the distribution of multidimensional microbiome data, but the composite variables calculated by the dimension reduction methods have not been widely used to investigate the relationship of the human gut microbiome with lifestyle and disease. In the present study, we applied several dimension reduction methods, including principal component analysis, principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), and non-negative matrix factorization, to a microbiome dataset from 186 subjects with symptoms of allergic rhinitis (AR) and 106 controls. All the dimension reduction methods supported that the distribution of microbial data points appeared to be continuous rather than discrete. Comparison of the composite variables calculated from the different dimension reduction methods showed that the characteristics of the composite variables differed depending on the distance matrices and the dimension reduction methods. The first composite variables calculated from PCoA and NMDS with the UniFrac distance were strongly associated with AR (FDR adjusted P = 2.4 × 10–4 for PCoA and P = 2.8 × 10–4 for NMDS), and also with the relative abundance of Bifidobacterium and Prevotella. The abundance of Bifidobacterium was also linked to intake of several nutrients, including carbohydrate, saturated fat, and alcohol via composite variables. Notably, the association between the composite variables and AR was much stronger than the association between the relative abundance of individual genera and AR. Our results highlight the usefulness of the dimension reduction methods for investigating the association of microbial composition with lifestyle and disease in clinical research.

To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.

Chemical process modeling is the basis for research and applications in related fields. With the development of industrial informatization, data-driven process modeling techniques are increasingly applied in chemical processes, helping to obtain more accurate results with less model development costs. However, due to the high-dimensional nonlinear characteristics of most chemical processes, problems such as the “curse of dimensionality” and information redundancy will render the models more prone to overfitting with reduced accuracies and weaker generalization abilities. Many data dimensionality reduction methods are adopted to mitigate the above problems, but most are limited by inaccurate association measurements and weak redundancy exclusion. In this paper, the extensive existence of data associations and information redundancies is first revealed through the analysis from an information-theoretic perspective. Then, a feature selection method based on conditional refined maximal information coefficient maximization (CRMICM) is proposed to improve the consistency of association measurement and the accuracy of redundancy estimation with limited samples. The final prediction modeling test for an actual fluidized catalytic cracking (FCC) process proves the extensive association between the variables and targets. Only a few variables are essential for the modeling, while the rest are redundant. Compared with other methods, CRMICM achieves the best dimensionality reduction effects on the FCC process data regarding the number of features and model prediction accuracy, showing its good applicability for chemical processes.

A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint

최근(2008년) 국제금융위기를 초래한 여러 변수들 중, 사회적인 소득불균형와 금융위기에 대한 대응책의 미흡 등의 문제들을 포함한 거시경제변수들 중, 특히 2가지 요인들에 주목할 필요가 있다. 즉, 2000년 이후부터 지속되어 온, 국제적 무역 불균형과 통화정책 상의 오류라는 요인들이다. 특히 후자의 경우, 2000년대 초반부터 지속된 경제의 불확실성 확대가 연방중앙은행이 저금리정책을 유지하게 된 주된 요인이 되었으며, 이로 인한 자본시장의 과도한 유동성이 시장참여자들로 하여금 시중 금리이상의 수익률을 추구하는 금융상품을 찾기에 비정상적 노력을 기울이도록 유도하였다. 본 논문에서는 상기 거시경제적인 변수와 관련하여, 국제금융위기의 직접적이고 미시적인 원인으로 간주되고 있는 장외파생상품에 대한 분석과 실증적 연구를 수행하였다. 금융위기 상황 이전의 장외파생상품들에 대한 과도한 투자열기, 특히, 신용부도스왑(Credit Default Swap, CDS)의 국제 자본시장에서의 과열된 매매가 최근의 국제금융위기를 초래한 직접적인 원인이 되었다고 판단되기 때문이다. 본 논문의 기여점으로는 자본선진국들에서는 CDS의 스프레드(프리미엄)에 대한 결정요인들에 대한 연구가 지속적으로 이루어 졌으나, 국내 기업들에 대한 결정요인에 대한 연구는 상대적으로 미약하였다는 점이라 할 수 있다. 본 연구의 방법론으로서 산업효과를 고려한 다중회귀식을 이용한 결정요인들에 대한 유의성 검정이며, 설명변수들 중에서, 국고채(만기 3년) 수익률, 시가-장부가 비율, 그리고 기업규모가 국내 기업들의 CDS 스프레드 의 유의한 결정요인이었다. 첫 장에서는 최근의 국제금융위기관련, 그에 대한 원인분석을 서술하였으며, 둘째 장에서 부채담보부증권과 신용부도스왑에 대한 구체적 개념을 설명하였다. 다음으로 국내 기업들 중, 국제적으로 CDS 스프레드가 거래되는 표본기업들을 대상으로 결정요인 분석을 실증적으로 수행하였다. 마지막 장의 결론 부분에서는 요약과 향후 연구에 대한 내용을 서술, 제시하였다. Among several macroeconomic missteps blamed for the recent global financial crisis including the social problems of income distribution and the lack of proper financial remedies, two of them have received particular attention: the global BOP(Balance of Payment) imbalance and the misguided monetary policy. Such BOP imbalance was blamed for massive foreign exchange investment flows from Asia into the U.S., triggering the financial and real estate bubble in America. The latter refers to the excessively loose monetary policy of the U.S. Federal Reserve, which pushed financial institutions and households into reckless investment behavior in search of higher returns. Given the abuse of certain innovative financial techniques and new investment instruments that have been created in recent decades, both collateralized debt obligations (CDOs) and credit default swaps (CDS) enjoyed a symbiotic and toxic relationship prior to the financial crisis This paper is organized as follows: The first section analyzes the real causes of the recent financial crisis. The second details the role of CDOs and CDS. Then, to identify key determinants of the CDS spreads in an emerging capital market, the sample data of major Korean firms' CDS spreads are used to estimate the risk premium by utilizing the multiple regression analysis. The empirical test result indicates that Korean 3-year treasury bond rate(TYIELD), market to book value ratio(MV/BV), and assets size(INASSETS) are shown to demonstrate statistically significant influences on the changes of the CDS premium for sample firms.