ARE LONG-HORIZON STOCK RETURNS PREDICTABLE? A BOOTSTRAP ANALYSIS

This paper examines empirical evidence of predictability of long-horizon real and excess stock returns in the UK using univariate as well as multivariate Variance Ratio tests. In order to estimate the sampling distribution of the test statistics, artificial histories ofstock returns are generated from their empirical distribution using the bootstrap method. This allows the construction of significance levels of the test statistic which are free from distributional assumptions. The empirical results indicate that there is no evidence of mean reversion in stock prices even if a wider information set to forecast stock returns is used and that the significance of historical Variance Ratio statistics has been overstated by previous studies.     

Optimal investments in volatility

Volatility has evolved as an attractive new asset class of its own. The most common instruments for trading volatility are variance swaps. Mean returns of DAX and ESX variance swaps over the time period of 1995 to 2004 are strongly negative, and only part of the negative premium can be explained by the negative correlation of variance swap returns with stock market indices. We analyze the implications of this observation for optimal portfolio composition. Mean-variance efficient portfolios are characterized by sizable short positions in variance swaps. Typically, the stock index is also sold short to achieve a better portfolio diversification. To capture heterogeneous preferences for higher moments, we use a variant of the polynomial goal programming method. We assume that investors strive for a high Sharpe ratio, high skewness, and low kurtosis. Our analysis reveals that it is often not possible to achieve a balanced tradeoff between Sharpe ratio and skewness. Investors are advised to hold the extreme portfolios (Sharpe ratio driven, skewness driven, or kurtosis driven) and avoid the middle ground. This “all-or-nothing” characteristic is reflected in jumps of asset weights when certain thresholds of preference parameters are crossed. These empirical findings can explain why many investors are so reluctant to implement option-based short-selling strategies.

A note on decomposing the Malmquist productivity index by means of subvector homotheticity

Summary. This paper introduces a decomposition of the Malmquist productivity index into component indexes. The motivation is to derive an analogue of the decomposition of the T?rnqvist index into productivity and quality change provided by Fixler and Zieschang (1992) to the Malmquist index. Since we employ no second order approximations, this decomposition requires additional structure, namely a generalized version of Shephard's (1970) inverse homotheticity, which we dub subvector homotheticity. We show that subvector homotheticity is necessary and sufficient for our decomposition. Received: July 10, 1998; revised version: August 11, 1999     

PISA: What makes the difference?

The large difference in the level and variance of student performance in the 2000 PISA study between Finland and Germany motivates this paper. It analyses why Finnish students showed a significantly higher performance by estimating educational production functions for both countries, using a unique micro-level dataset with imputed data and added school type information. The difference in reading proficiency scores is assigned to different effects, using Oaxaca–Blinder and Juhn–Murphy–Pierce decomposition methods. The analysis shows that German students and schools have on average more favorable characteristics except for the lowest deciles, but experience much lower returns to these characteristics in terms of test scores than Finnish students. The role of school types remains ambiguous. Overall, the observable characteristics explain more of the variation in test scores in Germany than in Finland.      

Measuring connectedness of euro area sovereign risk

We introduce a method for measuring the default risk connectedness of euro zone sovereign states using credit default swap (CDS) and bond data. The connectedness measure is based on an out-of-sample variance decomposition of model forecast errors. Due to its predictive nature, it can respond to crisis occurrences more quickly than common in-sample techniques. We determine the sovereign default risk connectedness using both CDS and bond data in order to obtain a more comprehensive picture of the system. We find evidence that there are several observable factors that drive the difference between CDS and bonds, but both data sources still contain specific information for connectedness spill-overs. In general, we can identify countries that impose risk on the system and the respective spill-over channels. Our empirical analysis covers the years 2009–2014, such that the recovery paths of countries exiting EU and IMF financial assistance schemes and the responses to the ECB’s unconventional policy measures can be analyzed.     

Measuring the increasing connectedness of Chinese assets with global assets: using a variance decompositions method

This study uses the network topology of variance decompositions to investigate the connectedness of four assets (stocks, bonds, foreign exchange and commodities) across five countries (US, EU, UK, Japan and China). We find that connectedness to and from the Chinese asset markets increased significantly from 2013 to 2018, which reveals that Chinese assets have gradually become integrated into the global economy. We also investigate the volatility connectedness in economically fragile periods and find that the Chinese market acted as a transmitter of volatility in the 2015 Chinese stock crash. This finding is potentially essential to modern risk measurement and management.     

Inferring volatility dynamics and risk premia from the S&P 500 and VIX markets

We estimate a flexible affine model using an unbalanced panel containing S&P 500 and VIX index returns and option prices and analyze the contribution of VIX options to the model’s in- and out-of-sample performance. We find that they contain valuable information on the risk-neutral conditional distributions of volatility at different time horizons, which is not spanned by the S&P 500 market. This information allows enhanced estimation of the variance risk premium. We gain new insights on the term structure of the variance risk premium, present a trading strategy exploiting these insights, and show how to improve S&P 500 return forecasts.     

Decomposition of tourism greenhouse gas emissions: Revealing the dynamics between tourism economic growth,technological efficiency,and carbon emissions

This study proposes an analytical framework for decomposing the national tourism carbon footprint and carbon efficiency to identify the dynamics between economic growth, technological efficiency, and environmental externality. Using the environmentally extended input–output model and decomposition methods, tourism carbon changes are decomposed into the economic factors of total consumption and purchasing patterns, and the production factors of industry input structure and technological improvement. This macro-level approach provides a basis for 1) assessing whether total tourism emissions increase in direct proportion to tourism consumption over time, 2) tracing the underlying determinants and their effects on tourism emissions expansion and eco-efficiency performance, and 3) comparing the carbon performance of the tourism industry against the national average. Based on the example of Taiwan, the results demonstrate that we are a long way from the goal of using the technological efficiencies of production to offset tourism-based carbon emissions.     

英语分级教学对高等数学教学影响的方差分析

对高等数学课程考试的成绩进行了方差分析，分析的结果是英语分级教学对高等数学成绩无显著影响，而学生入学时的高考数学成绩对高等数学教学的影响值得重视。     

Agricultural land-use dynamics: Assessing the relative importance of socioeconomic and biophysical drivers for more targeted policy

A detailed understanding of multiple human and environmental factors influencing land allocations among agricultural uses can facilitate more efficient and targeted land policy. To show this, we used a comprehensive dataset of socioeconomic, physiographic, and climatic indicators to investigate potential determinants of land-use in Australia’s intensive agricultural region during the period 1992–2010. We applied a seemingly unrelated regressions land-use shares spatial error model with random effects coupled with variance decomposition analysis to identify the statistical significance, direction and magnitude of observed associations between land-use and its drivers.Population: density, rainfall, equity ratio, and access to markets were the most influential policy-relevant land-use factors. Land allocations to cereals and livestock production were significantly influenced by spatiotemporal rainfall and temperature variability. Improved pastures, cereals, annual and perennial crops plantations were larger in regions with better access to markets. Increases in equity ratio (i.e., better financial position) were associated with larger land allocations to improved pastures and annual crops and smaller extensive grazing area. Marginal associations were detected between land-use and output prices, and higher population density was associated with lower shares for all high value agricultural land-uses. The results suggest that improved transportation infrastructure, zoning regulations, and mechanisms to reduce farm debt exposure and risks from climate variability could have significant impact on the configuration of the Australian agricultural landscape.