TDRSS接收机码分多址干扰抑制方案设计

为了在跟踪与数据中继卫星系统（TDRSS）中实现码分多址（CDMA）应用,需要完成多用户检测（MUD）及码分多址干扰（MAI）抑制。采用减性多级迭代对消算法,在数字信号处理芯片中完成信号再生、延时校正、时域相减对消,可以完成至少12通道的多用户检测,并能将多址干扰抑制至最小1 dB以内。该算法可作为通用MUD算法的有效补充,并能应用于各类非最优的CDMA系统中,具有迭代级数可选、通道扩展性强、工程实现性好的优点。     

NO‐ARBITRAGE PRICING UNDER SYSTEMIC RISK: ACCOUNTING FOR CROSS‐OWNERSHIP

We generalize Merton’s asset valuation approach to systems of multiple financial firms where cross‐ownership of equities and liabilities is present. The liabilities, which may include debts and derivatives, can be of differing seniority. We derive equations for the prices of equities and recovery claims under no‐arbitrage. An existence result and a uniqueness result are proven. Examples and an algorithm for the simultaneous calculation of all no‐arbitrage prices are provided. A result on capital structure irrelevance for groups of firms regarding externally held claims is discussed, as well as financial leverage and systemic risk caused by cross‐ownership.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

Theoretical,empirical, and operational models in hotel location research

This paper aims to review past literature on hotel location models and evaluate the state of the art, as well as set out future directions. This study divides hotel location models into three major categories: theoretical models, empirical models, and operational models. Four theoretical hotel location models are reviewed and discussed, including the tourist-historic city model, the mono-centric model, the agglomeration model, and the multi-dimensional model. Based on previous literature, six empirical models and three operational models of hotel location are elaborated. Furthermore, some challenges related to hotel location studies are discussed, and future research directions are provided. In particular, we advocate the development of more sophisticated hotel location models and the use of Geographic Information System (GIS) in hotel location analysis.     

Knowing your boundaries: Integration opportunities in international professional service firms

This study presents three different business models (continuous, repetitious, and unique) identified in international professional service firms that pursue a transnational strategy. These business models have varying opportunities for global integration. We extend the integration–responsiveness framework by offering a framework for analyzing how to balance global integration with local responsiveness when pursuing a transnational strategy. By identifying the content, structure, and governance transactions of the three business models, we can determine when to pursue headquarters-initiated global integration and when to choose strategies that ensure local responsiveness and subsidiary competitiveness in local markets.     

Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models

We extend the recently introduced latent threshold dynamic models to include dependencies among the dynamic latent factors which underlie multivariate volatility. With an ability to induce time-varying sparsity in factor loadings, these models now also allow time-varying correlations among factors, which may be exploited in order to improve volatility forecasts. We couple multi-period, out-of-sample forecasting with portfolio analysis using standard and novel benchmark neutral portfolios. Detailed studies of stock index and FX time series include: multi-period, out-of-sample forecasting, statistical model comparisons, and portfolio performance testing using raw returns, risk-adjusted returns and portfolio volatility. We find uniform improvements on all measures relative to standard dynamic factor models. This is due to the parsimony of latent threshold models and their ability to exploit between-factor correlations so as to improve the characterization and prediction of volatility. These advances will be of interest to financial analysts, investors and practitioners, as well as to modeling researchers.     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?     

CONVERTIBLE BOND PRICING MODELS

Convertible bonds are an important segment of the corporate bond market, with worldwide outstandings approaching US$235 billion. Simple pricing models value a convertible bond as being equivalent to a straight bond with an embedded option that enables the bond holder to convert to a specific amount of common stock. The straight bond is subject to both interest rate and credit risk, whereas the option to convert is dependent on the underlying stock price, which exposes the convertible bond holder to equity risk. The complexity of these features means that convertible bonds tend to be treated casually in major derivatives and corporate finance textbooks. This paper presents a survey of the theoretical and empirical aspects of convertible bond pricing. The limitations of these studies are highlighted to identify those areas of research that may improve the valuation process and facilitate the application of these securities for corporate financing.