Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

欧美股票市场对我国股票市场的传染效应研究——基于次贷与欧债危机背景

近年来,金融危机频频爆发且易表现出传染性,这使得金融传染引起国内外学者的高度关注.本文选取合适的动态条件相关模型研究欧美市场与A股、港股市场的条件相关性,结合内生多重结构突变模型与T检验方法划分危机传染期与平稳期,选用考虑外部影响的CCK模型研究A股、港股市场的羊群行为,随后,引入收益率分散度指标,研究两次危机的羊群行为传染渠道.研究结果表明:港股市场受两次危机传染的速度均快于A股市场,受传染的持续时间均长于A股市场,但受传染的程度均弱于A股市场:次贷危机传染程度强于欧债危机的传染程度,但传染的持续时间短于欧债危机:羊群行为传染渠道是两次危机对A股、港股市场的传染渠道之一.     

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

水平评估对高职院校图书馆的影响

阐述了水平评估的概况及图书馆在评估中的重要地位,分析了评估给高职院校图书馆带来的发展机遇和负面影响,并提出促进图书馆良性发展的机制。     

国家财政支农的效益性与路径选择

通过对国家财政支农宏观、中观、微观效益低下的实证分析,从国家预算、财政意识、政府博弈、配套政策诸方面探讨了影响财政支农效益的深层原因,进而指出国家财政支农的路径选择。     

金融波动的平方根随机自回归波动模型

讨论了SR-SARV模型的时间聚合性和同期聚合性,比较了波动模型之间的关系,指出了SR-SARV模型研究的意义,并给出其参数估计方法,对上证指数进行了实证研究。     

试论高校内部会计控制的目标及方法

内部会计控制是高校财务管理的重要组成部分。在阐释内部控制内涵的基础上,探讨和论述了高校内部会计控制的目标、方法,以适应高校防范财务风险、确保资产安全、提高会计信息质量与促进教育事业发展的需要。     

从国际货币体系改革趋势看中国金融发展战略

经过金融危机的冲击后,如何改革现有的国际货币体系成为国际金融界关注的重要课题,不同的经济体从各自的利益出发,提出了不同的改革意见和建议。从国际货币体系所主要包含的储备货币体系、国际收支平衡调节和国际资本流动等视角看,这些改革的设想从不同角度分析了现有的国际货币体系改革的潜在趋势。中国应当根据这些新的趋势,设定新的国际环境下的金融发展战略。