Analysis of the Efficacy of Public Spending on Research and Development in State Programs

This paper presents an approach to assessing the efficacy of public spending on research and development in state programs. At present, there is a need to develop a unified approach to the analysis and evaluation of the efficacy of such spending from the perspective of participants in state programs (federal executive bodies). The proposed approach does not allow evaluating the efficacy of public spending on the development of research areas assigned to the authors.     

Ultrasonic Technique for Assessment of the Rim Tightness of Hydro Generator Rotor

At the hydroelectric unit No. 11 of the Nizhnekamsk hydroelectric power plant, the filling wedges of a rotor were investigated by ultrasound in the free st     

The US financial crisis and corporate dividend reactions: for better or for worse?

Review of Quantitative Finance and Accounting - We examine how changes in dividend policy in 2008 as the financial crisis was unfolding influenced firm risk-adjusted returns in the following years....     

Operational Safety of the Zagorsk PSP After Leveling of Its Powerhouse

Power Technology and Engineering - The work done on the test site to ensure the operational safety of the powerhouse of the Zagorsk PSP-2 after its leveling is described. The controlled...     

A Real Options Model of Real Estate Development with Entitlement Risk

Most development projects encounter a highly uncertain entitlement process that is largely uncontrollable by developers. In this study, entitlement is modeled as a separate stage within a compound real option, where developers begin with minimal control (maximum risk) and each successful stage increases control (decreases risk). We solve the model analytically, provide three‐dimensional numerical comparisons, and empirically test the model's predictions using hand collected rezoning petitions. Our main result refines the classic development option model: developers first invest early (secure entitlements) in order to obtain the option to subsequently delay investment (construct the optimal building at the optimal time).     

Entry limiting agreements: First‐mover advantage,authorized generics,and pay‐for‐delay deals

During patent litigation, pay‐for‐delay (P4D) deals involve a payment from a patent holder of a branded drug to a generic drug manufacturer to delay entry and withdraw the patent challenge. In return for staying out of the market, the generic firm receives a payment, and/or an authorized licensed entry at a later date, but before the patent expiration. We examine why such deals are stable when there are multiple potential entrants. We combine the first‐mover advantage for the first generic with the ability of the branded manufacturer to launch an authorized generic (AG) to show when P4D deals are an equilibrium outcome. We further show that limiting a branded firm's ability to launch an AG before entry by a successful challenger will deter such deals. However, removing exclusivity period for the first generic challenger will not.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.