低温低密度水泥复合减轻材料研究

深水固井一直存在低环境温度、低破裂压力梯度、潜在浅层流、材料成本较高等技术难题。针对这些问题开发出了一种适用于低温（10 ℃）深水固井的低密度水泥复合减轻材料体系KBS1053,该水泥体系的密度为1.55 g/cm3,具有低温下凝结速度较快、低温强度发展较好、浆体流动性较好等特点,其早期抗压强度高于同等密度空心玻璃微珠低密度水泥体系,且原材料来源广泛,具有良好的工程性能和经济效益。     

工程物探技术在地下库可研阶段勘察中的应用

根据某大型地下水封石油油库可研阶段勘察实施过程,介绍大型地下水封石油油库可研阶段工程勘察的要求和方法、工程物探种类、高密度电法和地震反射法的原理、方法、实施过程中的应用情况、勘察成果整理分析。阐述了物探与钻探、地质测绘相互验证对建库条件的评价。     

Long-run equilibrium, short-term adjustment, and spillover effects across Chinese segmented stock markets and the Hong Kong stock market

This paper adopts a novel FIVECM-BEKK GARCH approach to examine the bilateral relationships among the A-share and B-share stock markets in China and the Hong Kong stock market. The evidence shows that these stock markets are fractionally cointegrated. Analyses of the spillover effects across these markets indicate that the A-share markets are most influential. The relaxation of government restrictions on the purchase of B shares by domestic residents accelerates the market integration process of A-share markets with the B-share and Hong Kong markets. The effects of the Asian crisis on the stock-return dynamic correlations vary across these markets.     

大秦线增加资本投入提高运输密度的分析

资本投入对产出数量起决定性作用。通过对大秦线的资本投入对运量增加的影响情况进行分析,说明加强影响列车质量和密度相关设施的资本投入,对提高产出起到重要作用。同时指出对投向大秦线的资本,应向发展重载技术方面倾斜,能对大秦线运输能力的提高起持续性影响作用。     

Analysing Tax–Benefit Reforms Using Non‐Parametric Methods*

Static tax–benefit microsimulation models (MSMs) are widely used and well‐regarded tools for public policy analysis, but it is essential to use them very carefully. This paper focuses on the analysis of MSM output, suggesting the use of non‐parametric methods as a useful, informative and relatively straightforward complement to detect effects not always captured by measures often used to present MSM results. Non‐parametric methods are used here to analyse the output of an MSM applied to the 1998 Italian personal income tax reform, the main change in which concerned the tax schedule: the first tax rate was increased from 10 per cent to 18.5 per cent and the top one was reduced by 4.5 percentage points. Non‐parametric methods highlight that the effects of this reform were very different for different types of households, with low‐income pensioner households among the main losers. Results are checked for robustness by standard statistical methods and compared with empirical results obtainable using quintile histograms.     

The shape of small sample biases in pricing kernel estimations

Numerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio of two separate density estimations. In the first step, we analyse theoretically the functional dependence for the ratio of a density to its estimated density; this cautions the reader regarding potential computational issues coupled with statistical techniques. In the second step, we study this quantitatively; we show that small sample biases shape the estimated pricing kernel, and that estimated pricing kernels typically violate the commonly believed monotonicity at the centre even when the true pricing kernel fulfils these. This contributes to an alternative, statistical explanation for the puzzling shape in pricing kernel estimations.     

Building multivariate Sato models with linear dependence

The increased trading in multi-name financial products has required the development of state-of-the-art multivariate models. These models should be computationally tractable and, at the same time, flexible enough to explain the stylized facts of asset log-returns and of their dependence structure. The popular class of multivariate Lévy models provides a variety of tractable models, but suffers from one major shortcoming: Lévy models can replicate single-name derivative prices for a given time-to-maturity, but not for the whole range of quoted strikes and maturities, especially during periods of market turmoil. Moreover, there is a significant discrepancy between the moment term structure of Lévy models and the one observed in the market. Sato processes on the other hand exhibit a moment term structure that is more in line with empirical evidence and allow for a better replication of single-name option price surfaces. In this paper, we propose a general framework for multivariate models characterized by independent and time-inhomogeneous increments, where the asset log-return processes at unit time are modeled as linear combinations of independent self-decomposable random variables, where at least one self-decomposable random variable is shared by all the assets. As examples, we consider two general subclasses within this new framework, where we assume a normal variance-mean mixture with a one-sided tempered stable mixing density or a difference of one-sided tempered stable laws for the distribution of the risk factors. Particular attention is given to the models' ability to explain the asset dependence structure. A numerical study reveals the advantages of these new types of models.     

Dependence calibration and portfolio fit with factor-based subordinators

The paper explores the properties of a class of multivariate Lévy processes used for asset returns. We focus on describing both linear and non-linear dependence in an economic sensible and empirically appropriate way. The processes are subordinated Brownian motions. The subordinator has a common and an idiosyncratic component, to reflect the properties of trade, which it represents. A calibration to a portfolio of 10 US stock indices returns over the period 2009–2013 shows that the hyperbolic specification has a very good fit to marginal distributions, to the overall correlation matrix and to the return distribution of both long-only and long-short random portfolios, which also incorporate non-linear dependence. Their tail behaviour is also well captured by the variance gamma specification. The main message is not only the goodness of fit, but also the flexibility in capturing dependence and the ease of calibration on large sets of returns.     

Recovering the real-world density and liquidity premia from option data

In this paper, we develop a methodology for simultaneous recovery of the real-world probability density and liquidity premia from observed S&P 500 index option prices. Assuming the existence of a numéraire portfolio for the US equity market, fair prices of derivatives under the benchmark approach can be obtained directly under the real-world measure. Under this modelling framework, there exists a direct link between observed call option prices on the index and the real-world density for the underlying index. We use a novel method for the estimation of option-implied volatility surfaces of high quality, which enables the subsequent analysis. We show that the real-world density that we recover is consistent with the observed realized dynamics of the underlying index. This admits the identification of liquidity premia embedded in option price data. We identify and estimate two separate liquidity premia embedded in S&P 500 index options that are consistent with previous findings in the literature.     

The multivariate Variance Gamma model: basket option pricing and calibration

In this paper, we propose a methodology for pricing basket options in the multivariate Variance Gamma model introduced in Luciano and Schoutens [Quant. Finance 6(5), 385–402]. The stock prices composing the basket are modelled by time-changed geometric Brownian motions with a common Gamma subordinator. Using the additivity property of comonotonic stop-loss premiums together with Gauss-Laguerre polynomials, we express the basket option price as a linear combination of Black & Scholes prices. Furthermore, our new basket option pricing formula enables us to calibrate the multivariate VG model in a fast way. As an illustration, we show that even in the constrained situation where the pairwise correlations between the Brownian motions are assumed to be equal, the multivariate VG model can closely match the observed Dow Jones index options.