技术和价格不确定下企业研发投资决策研究

随着科学技术的迅速发展,研发(RD)活动对企业的生存与发展日益重要.本文将企业研发投资项目所承受的风险分为两种:研发前期不可对冲的技术风险,以及研发产品投入市场时因价格和需求的不确定性所承受的价格风险,而后者是可以部分对冲的.本文考虑一个以创新研发投资为主的企业,如何进行相应的投资消费问题.通过建立效用最大化模型,运用随机最优控制理论与方法得到关于指数效用函数的最优策略,同时阐述了研发投资价值及其投资/中止阈值.数值结果表明:较大的技术风险,会产生更大的信息生成价值,使得研发的总价值不减反增;风险厌恶态度对企业的研发价值以及研发/中止阈值都有较明显的影响;因研发信息生成价值的存在,使得研发/中止阈值不同于传统净现值(NPV)降为零的阈值.     

基于SD-SEM模型的消费者食品安全风险感知的信息搜寻行为

本文利用系统动力学模型和结构方程模型来研究消费者食品安全风险感知和消费者信息搜寻行为之间的关系,在总结消费者食品安全风险感知和消费者食品安全信息搜寻行为研究文献的基础上,利用结构方程确定消费者食品安全风险感知的影响因素及其之间的路径系数,进而建立系统动力学模型模拟信息搜寻行为对消费者食品安全风险感知的影响,以提出降低消费者食品安全风险感知的建议.     

基于离散时间风险模型下的亏损破产概率的研究

新定义离散时间风险模型下的亏损破产概率为初始盈余u,亏损额度不大于y的破产概率。利用离散时间风险模型下的终时破产概率的计算规律,得到初始盈余水平在不同条件下的亏损破产概率的具体表达形式,并且数值模拟了一定条件下不同参数取值对亏损破产概率的影响情况,数据表明当亏损边界固定时,随着初始盈余水平的增加,亏损破产概率水平逐渐减小;当初始盈余水平固定时,随着亏损边界的增加,亏损破产概率水平逐渐增多。     

Forecasting with Micro Panels: The Case of Health Care Costs

Micro panels characterized by large numbers of individuals observed over a short time period provide a rich source of information, but as yet there is only limited experience in using such data for forecasting. Existing simulation evidence supports the use of a fixed‐effects approach when forecasting but it is not based on a truly micro panel set‐up. In this study, we exploit the linkage of a representative survey of more than 250,000 Australians aged 45 and over to 4 years of hospital, medical and pharmaceutical records. The availability of panel health cost data allows the use of predictors based on fixed‐effects estimates designed to guard against possible omitted variable biases associated with unobservable individual specific effects. We demonstrate the preference towards fixed‐effects‐based predictors is unlikely to hold in many practical situations, including our models of health care costs. Simulation evidence with a micro panel set‐up adds support and additional insights to the results obtained in the application. These results are supportive of the use of the ordinary least squares predictor in a wide range of circumstances. Copyright © 2016 John Wiley & Sons, Ltd.     

基于BEMD-Copula-GARCH模型的股票投资组合VaR风险度量研究

鉴于股票波动具有显著的多尺度特征,本文引入二元经验模态分解(EMD)与二元CopulaGARCH算法,提出一种新的VaR风险度量模型,即BEMD-Copula-GARCH模型.具体地,新BEMD-Copula-GARCH模型可分为三个主要步骤:数据分析,分风险估计和总风险集成.首先,基于二元EMD模型,将复杂且相互作用的股票对分解为若干组较为简单且相互独立的分量,以降低建模难度.其次,引入二元Copula-GARCH模型,刻画各组分量间的相互关系,以度量股票投资组合在不同尺度上的分VaR值.最后,集成各分VaR值以得出最终VaR风险度量结果.实证研究以恒生指数与上证综指为数据样本构造投资组合,结果表明:本文所构建的新模型能有效度量投资组合风险,其估计精度显著优于DCC-GARCH和Copula-GARCH等现有模型.     

The US Dollar/Euro Exchange Rate: Structural Modeling and Forecasting During the Recent Financial Crises

The paper investigates the determinants of the US dollar/euro within the framework of the asset pricing theory of exchange rate determination, which posits that current exchange rate fluctuations are determined by the entire path of current and future revisions in expectations about fundamentals. In this perspective, we innovate by conditioning on Fama–French and Carhart risk factors, which directly measures changing market expectations about the economic outlook, on new financial condition indexes and macroeconomic variables. The macro‐finance augmented econometric model has a remarkable in‐sample and out‐of‐sample predictive ability, largely outperforming a standard autoregressive specification. We also document a stable relationship between the US dollar/euro Carhart momentum conditional correlation (CCW) and the euro area business cycle. CCW signals a progressive weakening in economic conditions since June 2014, consistent with the scattered recovery from the sovereign debt crisis and the new Greek solvency crisis exploded in late spring/early summer 2015. Copyright © 2016 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

A decision rule to minimize daily capital charges in forecasting value‐at‐risk

Under the Basel II Accord, banks and other authorized deposit‐taking institutions (ADIs) have to communicate their daily risk estimates to the monetary authorities at the beginning of the trading day, using a variety of value‐at‐risk (VaR) models to measure risk. Sometimes the risk estimates communicated using these models are too high, thereby leading to large capital requirements and high capital costs. At other times, the risk estimates are too low, leading to excessive violations, so that realized losses are above the estimated risk. In this paper we analyze the profit‐maximizing problem of an ADI subject to capital requirements under the Basel II Accord as ADIs have to choose an optimal VaR reporting strategy that minimizes daily capital charges. Accordingly, we suggest a dynamic communication and forecasting strategy that responds to violations in a discrete and instantaneous manner, while adapting more slowly in periods of no violations. We apply the proposed strategy to Standard & Poor's 500 Index and show there can be substantial savings in daily capital charges, while restricting the number of violations to within the Basel II penalty limits. Copyright © 2009 John Wiley & Sons, Ltd.     

Forward Rates,Monetary Policy and the Economic Cycle

The short end of the yield curve incorporates essential information to forecast central banks' decisions, but in a biased manner. This article proposes a new method to forecast the Fed and the European Central Bank's decision rate by correcting the swap rates for their cyclical economic premium, using an affine term structure model. The corrected yields offer a higher out‐of‐sample forecasting power than the yields themselves. They also deliver forecasts that are either comparable or better than those obtained with a factor‐augmented vector autoregressive model, underlining the fact that yields are likely to contain at least as much information regarding monetary policy as a dataset composed of economic data series. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting VaR models under Different Volatility Processes and Distributions of Return Innovations

This paper provides clear‐cut evidence that the out‐of‐sample VaR (value‐at‐risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime‐switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime‐switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered. Copyright © 2014 John Wiley & Sons, Ltd.