The Selenge basin contributes approximately 50% of the total inflow into Lake Baikal and is thus of high significance for the regional hydrological regime. Our study was conducted in the upper reaches of the basin, where the Selenge river and its tributaries flow through the Mongolian forest-steppe. Monthly and maximum runoff, precipitation, and air temperature data from 12 gauging stations collected between 1978 and 2015 were analyzed to characterize the hydrological regime response to climate change. Concomitant with rising temperatures and increased potential evaporation, river runoff in the Mongolian part of the Selenge basin has decreased from the first interval (1978)(1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995) of our study period compared with the consecutive interval from 1996 to 2015. The decrease in runoff throughout the study area was most likely caused by an increase in potential evapotranspiration (and not reduced precipitation or land use changes) for both summer rainfall-and snowmelt-dominated rivers. Annual maximum runoff has also strongly decreased suggesting that reduced flooding is a contemporary threat for Mongolia's riverine ecosystems, probably causing the replacement of wetland and mesic habitats.

This study introduces a stochastic multi-period dividend discount model (DDM) that includes (i) a compound nonhomogenous Poisson process for dividend growth and (ii) the probability of firm default. We obtain maximum likelihood (ML) estimators and confidence interval formulas of our model parameters. We apply the model to a set of firms from the S&P 500 index using historical dividend and price data over a 42-year period. Interestingly, stock price estimations calculated with the model are close to the observable prices. Overall, we prove that the model can be a useful tool for stock pricing.

This paper presents pricing and hedging methods for segregated funds and unit-linked life insurance products that are based on a Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process. Here we assumed that a regime-switching process is generated by a homogeneous Markov process. An advantage of our model is it depends on economic variables and not complicated.

In this study, we introduce new estimation methods for the required rate of return of the stochastic dividend discount model (DDM) and the private company valuation model, which will appear below. To estimate the required rate of return, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We apply the model to a set of firms from the S&P 500 index using historical dividend and price data over a 32-year period. Overall, suggested methods can be used to estimate the required rate of return. Suggested methods not only used to estimate required rate of return on stock, but also used to estimate required rate of return of debtholders.

For a public company, the option pricing models, hedging models, and pricing models of equitylinked life insurance products have been developed. However, for a private company, because of unobserved price, the option and the life insurance pricing, and the hedging are challenging tasks. For this reason, this paper introduces a log private company valuation model, which is based on the dynamic Gordon growth model. In this paper, we obtain closed-form option pricing formulas, hedging formulas, and net premium formulas of equity-linked life insurance products for a private company. Also, the paper provides ML estimators of our model, EM algorithm, and valuation formula for the private company. The suggested model can be used not only by private companies but also by public companies