Confidence bands in quantile regression and generalized dynamic semiparametric factor models

A semiparametric factor model for CDO surfaces dynamics

Leveraged ETF Options Implied Volatility Paradox: A Statistical Study

Modelling the dynamics of credit derivatives is a challenging task in nance and economics. The recent crisis has shown that the standard market models fail to measure and forecast nancial risks and their characteristics. This work studies risk of collateralized debt obligations (CDOs) by investigating the evolution of tranche spread surfaces and base correlation surfaces using a dynamic semiparametric factor model (DSFM). The DSFM oers a combination of exible functional data analysis and dimension reduction methods, where the change in time is linear but the shape is nonparametric. The study provides an empirical analysis based on iTraxx Europe tranches and proposes an application to curve trading strategies. The DSFM allows us to describe the dynamics of all the tranches for all available maturities and series simultaneously which yields better understanding of the risk associated with trading

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.

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

In the last decade, most studies on the interaction between the financial sector and economic growth has focused mainly on the banks and the stock market. Recently, growing attention has shifted to the interaction between the non-bank financial intermediaries (NBFIs) such as the insurance companies and economic growth. In this study, we examined the impact of insurance contributions on economic growth in Nigeria over a twenty seven year period, between 1981 and 2008 using a dynamic factor model. The proposed technique described a number of methods designed to analyze a functional relationship between the volume of insurance contribution and economic growth in terms of underlying but unobservable random quantities called factors. The factor loadings indicate which common trend is related to which set of time series. The result obtained through this approach shows that the functional relationship between the volume of insurance contribution and economic growth in Nigeria is a first order vector autoregressive model. Key words: Insurance contribution, dynamic factor model, economic growth.

Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model

Dynamics of processes in the chemical industries requires substantiation of new approaches to vocational training programs for future mechanical engineers. These scientific and practical innovations should affect the entire system of training, spreading, primarily on professionally oriented disciplines, leading among them for direction « Mechanical Engineering » specialty « chemical production equipment and construction materials » is « processes, machines and machine area » . The paper presents a system-factor approach to enhance the training of future mechanical engineers in the direction of « Engineering » specialty « chemical production equipment and building materials » , the discipline « processes, machines and machine industry » on the basis of system-factor organizing training tasks, which are purposefully reinforce this training for each conditionally selected for the leading function of work, professional category: « Technology » , « mechanics » , « designers » . So, the training of mechanical engineers in the direction of « Engineeing » specialty « chemical production equipment and building materials » should be aimed at the preparation of various experts said the work on the classification of professional groups. Each of the factors proposed system-factor approach to discipline « processes, machines and machine industry » to enhance the training of future mechanical engineers in the direction of « Engineering » is given and should be taken into account, but if necessary, subjected to the necessary adjustments. Conditional inclusion of subjective factors in the objective-subjective, and those – in an objective manner the static measurement of the factor model application system of learning tasks in the preparation of future mechanical engineers specialty « chemical production equipment and building materials » , the discipline « processes, machines and machine industry » . The dynamic factor model as the measurement system application training tasks forms a gradual taking into account the following factors each with a mandatory check the completeness of all previous records. Thus, the implementation of system-factor approach for future mechanical engineers in the direction of « Engineering » specialty « chemical production equipment and building materials » , the discipline « processes, machines and machine industry » to contribute to enhance their professional skills in the specialty for each conditionally selected for leading function of employment, Professional, « technology » , « mechanics » , « designers » . system-factor approach; a mechanical engineer; equipment; chemicals manufacturing and construction materials; engineering direction, objectives; factors, system-factor model

A dynamic factor model to predict homicides with firearm in the United States

In this contribution we present a structure theory for generalized linear dynamic factor models. Generalized dynamic factor models have been proposed approximately a decade ago for modeling of high dimensional time series where the cross sectional dimension is of the same order of magnitude as the sample size. In these models the classical assumption for factor models, that the noise components are mutually uncorrelated, is relaxed by allowing for weak dependence. Structure theory turns out to be important for estimation and model selection. The results obtained heavily draw from linear system theory.The contribution consists of two main parts. In the first part we deal with “denoising”, i.e. with getting rid of the noise in the observations. In the second part we deal with constructing linear dynamic systems for the latent variables. Here an important result is the generic zerolessness of the transfer function relating the latent variables and the dynamic factors. This allows for modeling the latent variables by (singular) autoregressions which simplifies estimation.

Equity basket correlation can be estimated both using the physical measure from stock prices, and also using the risk neutral measure from option prices. The difference between the two estimates motivates a so-called “dispersion strategy”. We study the performance of this strategy on the German market and propose several profitability improvement schemes based on implied correlation (IC) forecasts. Modelling IC conceals several challenges. Firstly the number of correlation coefficients would grow with the size of the basket. Secondly, IC is not constant over maturities and strikes. Finally, IC changes over time. We reduce the dimensionality of the problem by assuming equicorrelation. The IC surface (ICS) is then approximated from the implied volatilities of stocks and the implied volatility of the basket. To analyze the dynamics of the ICS we employ a dynamic semiparametric factor model.

Synthetic wind speed time series generation by dynamic factor model

Dynamic mortality factor model with conditional heteroskedasticity

Since carbon price volatility is critical to the risk management of the CO2 emissions trading market, research has focused on energy prices and macroeconomic drivers which cause changes in carbon prices and make the carbon market more volatile than other markets. However, they have ignored whether the impact of carbon price determinants changes when the carbon price is at different levels. To fill this gap, this paper applies a semiparametric quantile regression model to explore the effects of energy prices and macroeconomic drivers on carbon prices at different quantiles. The model combines the advantages of parameter estimation, nonparametric estimation and quantile regression to describe the nonlinear relationship between carbon price and its fundamentals, which do not need to make any assumptions about the random error. Carbon prices are high–tailed and exhibit higher kurtosis, the traditional models which tend to assume that data are normally distributed can’t perform well. Furthermore, the semiparametric model doesn’t need to assume that the data are normally distributed. Therefore, the semiparametric model can effectively model the data. Some new evidence from China’s emission trading scheme (ETS) pilots shows that energy prices and macroeconomic drivers have different effects on carbon prices at high or low quantiles. First, the negative impact of coal prices on carbon prices was greater at the lower quantile of carbon prices in the Shenzhen ETS pilot. However, the effects of coal prices were positive in the Beijing ETS pilot, which may be attributed to great demand for coal. Second, oil prices had greater negative effects on carbon prices at higher quantiles in Beijing and Hubei ETS pilots. This can be attributed to the fact that businesses use less oil when carbon prices are high. For the Shenzhen ETS pilot, the effects of oil prices were positive. Third, natural gas prices have a stronger effect on carbon prices as quantiles increased in the Beijing and Hubei ETS pilots. Lastly, the effects of macroeconomic drivers on carbon prices at low quantiles were stronger in the Shenzhen ETS pilots and higher at the medium quantiles in Beijing and Hubei ETS pilots. These findings suggest that the impact of determinants on the carbon prices at different levels is not constant. Ignoring this issue will lead to a missed warning about the risks of the carbon market. This study will be of positive significance for China’s emission trading scheme (ETS) pilots, in order to accurately monitor the effects of carbon prices determinants and effectively avoid carbon market risks.

Improved index insurance design and yield estimation using a dynamic factor forecasting approach