Model Assisted Statistics and Applications

In this paper, we propose a semiparametric method for modeling the volatility in financial time series. The aim is to improve the forecasting capabilities of the most popular parametric volatility models and we also compare our approach to two recent semiparametric models in the literature. Our method is based on the bivariate Bernstein basis polynomials and the functional gradient descent (FGD) algorithm. We evaluate our method through simulated and real datasets. The results demonstrate its good predictive potential for financial volatility.

The British option was first introduced by G. Peskir and F. Samee (2011). In a British option, the holder can enjoy the early exercise feature of American option whereupon his payoff is the ’best prediction’ of the European payoff given all the information up to the exercise date under the hypothesis that the true drift of the stock equals a specified contract drift. Consistent with the plain vanilla option, the authors considered the constant interest rate. In this paper, we will consider the pricing of the British put option in a stochastic interest rate environment, particularly the Vasicek model. We will derive a closed form expression for the arbitrage-free price in terms of the rational exercise boundary.

The capital adequacy ratio is one of the important regulatory requirement for banks, which indicates its willingness to cover losses in the event of borrowers’ defaults. The Probability of Default (PD) and Loss Given Default (LGD) are two core parameters of the internal risk rating models used to calculate regulatory capital under the assumption that PD and LGD are independent. Papers based on developed countries data provide evidence the dependence to be positive. It causes that banks underestimate the level of a risk of its loan portfolio, while they do not take into account the existence of such relationship. This is the first paper which aims to estimate the relationship between PD and LGD for Russian public companies. A major conclusion of the research is that using Russian data one cannot argue for the presence of risk parameter dependence whereas research using developed countries’ data suggests there is a positive one. This implies there is no need to overcharge capital for Russian banks compared to their counterparts from developed countries.

Fluctuation in mortgage default rates provides vital information to financial institutions and is a key indicator of the state of the economy. Using a decade’s worth (2002–2010) of data on prime and subprime mortgage portfolios, we propose and compare two models for mortgage defaults. The first, the Weibull-Gamma segmentation model (WGS), was utilized by Fader and Hardie (2007) in forecasting customer retention. Though effective in that setting, Markov chain Monte Carlo simulations suggest that the WGS suffers from over-parameterization. The Weibull segmentation model (WS) provides a simplified alternative that accurately forecasts default rates while identifying latent classes of “risky” prime and subprime mortgages characterized by increased hazard rates.

Multivariate Poisson processes have many important applications in Insurance, Finance, and many other areas of Applied Probability. In this paper we study the backward simulation approach to modelling multivariate Poisson processes and analyze the connection to the extreme measures describing the joint distribution of the processes at the terminal simulation time. Multivariate Poisson processes have many important applications in Insurance, Finance, and many other areas of Applied Probability. In this paper we study the backward simulation approach to modelling multivariate Poisson processes and analyze the connection to the extreme measures describing the joint distribution of the processes at the terminal simulation time.

This describes a statistical technique called “tonsuring” for exploratory data analysis in finance. Instead of rejecting “outlier” data that conflicts with the model, this strips out “inlier” data to get a clearer picture of how the market changes for larger moves.

High-technology business acts as an important driver of any economy. Microeconomic factors influencing employment in small high-technology companies are identified and assessed in the paper. The case of high-technology manufacturing and knowledge-intensive services in Russian transition economy is discussed. The empirical part of this research is based on data provided by Business Environment and Enterprise Performance Survey (BEEPS). A two-step assessment procedure was applied in order to determine and estimate the factors of growth. Significant factors were selected with the help of best subsets regression and then these factors were further analyzed using OLS. Such an approach enables an increased explanatory power of the obtained results. It was found that younger companies have greater influence on job creation than older ones. Significant differences in growth factors between companies in high-technology manufacturing and knowledge-intensive services were demonstrated. This difference constitutes a new result in the research of growth of high-technology companies in transition economies. The suggested model could help to construct the companies’ rating, which would be useful for investors in the emerging markets with high volatility of assets’ prices and lack of information for investment analysis.