高效率长寿命太阳能节能照明系统的研究

针对目前市场上的太阳能照明灯存在“低效率、短寿命、高成本”（即“两低一高”）的缺点,提出了“超高亮度LED灯具”、“太阳强光实时自动跟踪装置”、“低光照度时仍能充电的变换器”、“低功耗的LED驱动／集成芯片”和“主辅LED工作模式”等解决方案,经试验,均取得令人满意的效果。     

Tourism,terrorism and political violence in Tunisia: Evidence from Markov-switching models

This study investigates the impact of terrorist attacks and political violence on the number of tourist arrivals and overnight stays in Tunisia. The dataset employed consists of monthly data that covers the period from January 2000 to September 2016, which includes several political and terrorist attacks in Tunisia and the region. Empirically, we investigate the true data generating process (DGP) of these two proxies of tourism activity by accounting for four statistical properties that characterize these series: (1) seasonality, (2) unit roots, (3) breaks, and (4) long memory behavior.Our empirical findings show strong evidence of stationarity, five breaks in the tourist arrival time series and spurious long memory behavior. By estimating a 3-state Markov switching model consisting of the mean, trend, and variance, we find that the Tunisian Jasmine revolution and two recent terrorist attacks, one at the Bardo National Museum on March 18, 2015 and the other at the tourist resort at Port El Kantaoui, Sousse on June 26, 2015, played an important role in influencing the tourism activity of the country. Our empirical findings show also that local shocks have a more important impact than international shocks in influencing tourism activity. Interestingly, we find that the effects of terrorist shocks have a long duration compared to political violence shocks. Several security, marketing, and economic policies have been proposed and discussed in the paper.     

A note on the long rate in factor models of the term structure

In this paper, we consider factor models of the term structure based on a Brownian filtration. We show that the existence of a nondeterministic long rate in a factor model of the term structure implies, as a consequence of the Dybvig–Ingersoll–Ross theorem, that the model has an equivalent representation in which one of the state variables is nondecreasing. For two‐dimensional factor models, we prove moreover that if the long rate is nondeterministic, the yield curve flattens out, and the factor process is asymptotically nondeterministic, then the term structure is unbounded. Finally, we provide an explicit example of a three‐dimensional affine factor model with a nondeterministic yet finite long rate in which the volatility of the factor process does not vanish over time.     

Medicaid and long‐term care: The effects of penalizing strategic asset transfers

Medicaid provides a critical source of insurance for long‐term care, and individuals may strategically offload assets (typically to children) to meet the means‐tested eligibility requirement. In this article, we quantify the extent of such behavior using variation in the penalty for improper parent‐to‐child transfers induced by the Deficit Reduction Act of 2005. We estimate difference‐in‐differences models based on the hypothesis that only individuals with high levels of nursing home risk (high risk) will alter transfers because of the Act. We find that over a 2‐year horizon, high‐risk individuals reduced transfers to children on the extensive margin by 11 percent and that the average total amount of transfers decreased by $4,860. The results hold only for coupled respondents. We also conduct a triple‐differences analysis to examine heterogeneity with financial literacy and find that even those with a low level of financial literacy responded to the penalty.     

The effects of anticipated goal‐inconsistent behavior on present goal choices

Prior work has examined how, in the pursuit of long‐term goals, past goal behavior influences present goal choices. Instead, the present work focuses on how anticipating future goal behavior, specifically future goal‐inconsistent behavior, influences present goal choices. For example, how anticipating overspending on an upcoming vacation influences current spending behavior. The authors propose that the effect of anticipated goal‐inconsistent behavior on present goal choice is moderated by the perceived changeability of the future behavior. When future goal‐inconsistent behavior is perceived as changeable, consumers tend to imagine it away, and it has no systematic effect on present goal choices. However, when future goal‐inconsistent behavior is perceived as unchangeable, consumers accept it as a matter of fact, and systematic effects occur. Specifically, some consumers not only fail to buffer against future goal‐inconsistent behavior's negative consequences, but tend to exacerbate those consequences by increasing their goal‐inconsistent behavior in the present. Four studies examine this surprising behavior, using an individual difference (the response‐to‐failure scale) to identify when and for whom it occurs. The studies demonstrate the role of perceived changeability using various manipulations across multiple critical goal domains such as spending, eating, and academics.     

Cover's universal portfolio,stochastic portfolio theory,and the numéraire portfolio

Cover's celebrated theorem states that the long‐run yield of a properly chosen “universal” portfolio is almost as good as that of the best retrospectively chosen constant rebalanced portfolio. The “universality” refers to the fact that this result is model‐free, that is, not dependent on an underlying stochastic process. We extend Cover's theorem to the setting of stochastic portfolio theory: the market portfolio is taken as the numéraire, and the rebalancing rule need not be constant anymore but may depend on the current state of the stock market. By fixing a stochastic model of the stock market this model‐free result is complemented by a comparison with the numéraire portfolio. Roughly speaking, under appropriate assumptions the asymptotic growth rate coincides for the three approaches mentioned in the title of this paper. We present results in both discrete and continuous time.     

Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration

Given that the United States is an engine of global stock market while China is the largest emerging market with a cornucopia of anomalies in particular, it is vital to investigate the risk-return relationship in the two markets. This paper brings new insights not only into risk-return tradeoff, but also to the leverage effect, with the application of the fractionally co-integrated vector auto-regression (FCVAR) model capturing the fractional cointegrated relationship and long memory property. Results show that China stock markets own the property of double long memory but the US markets don’t. Most of all, in the US market, a positive risk-return tradeoff exists for the whole sample while after the crisis, even we find the negative relation, it’s not a volatility feedback effect but low risk and high returns. However, there is only a volatility feedback effect in China stock markets. Besides, there is a leverage effect in the US market, while Chinese market exhibits a reverse one, another anomaly, indicating significant difference in the two markets again.     

Volatility of volatility is (also) rough

Using high-frequency data for major volatility indexes, we compute the volatility of volatility and show that its logarithm follows a fractional Brownian motion with Hurst parameter smaller than 1/2 thereby extending to the volatility asset class the recent findings obtained for the equity index markets. The results confirm that the volatility of volatility is a rough process and it possesses the long memory property. We also show that the correlation between the volatility and the volatility of volatility is positive, consistent with observations in the volatility option market. Lastly, a robustness check using volatility futures confirms the findings.     

浅谈长螺旋钻孔灌注桩技术在水利工程中的应用

长螺旋钻孔灌注桩施工是工业民用建筑基础处理中经常采用的一种施工技术。文章通过对其在围堤湖分洪闸闸室段基础处理工程中成功应用的阐析,提出它在某些水利工程的基础处理施工中同样可以占据一席之地,并有其独特的优势。     

Exploring the predictability of range‐based volatility estimators using recurrent neural networks

We investigate the predictability of several range‐based stock volatility estimates and compare them with the standard close‐to‐close estimate, which is most commonly acknowledged as the volatility. The patterns of volatility changes are analysed using long short‐term memory recurrent neural networks, which are a state‐of‐the‐art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index and report averaged evaluation results. We find that the direction of changes in the values of range‐based estimates are more predictable than that of the estimate from daily closing values only.