Exploring longitudinal risk‐return relationships

We study a longitudinal fit model of adaptation and its association with the longitudinal risk‐return relationship. The model allows the firm to adjust its position in response to partial learning about a changing environment characterized by two path‐dependent processes—a random walk and a stochastic trend. Computational simulations at low levels of learning in both environmental contexts are consistent with empirical data. However, the results are also consistent when firm behavior appears to be mindless in the form of a random walk. Hence, both imperfect learning and a mindless random walk can lead to the inverse longitudinal risk‐return relationships observed empirically. We discuss this apparent paradox and the possible resolution between mindless and conscious behavior as plausible causes of the longitudinal Bowman Paradox. Copyright © 2014 John Wiley & Sons, Ltd.     

Capital and resolution policies: The US interbank market

We develop an empirically based simulation study to test two types of policies designed to control systemic risk: preventive policies targeting capital requirements and mitigation policies targeting default resolution. We find that capital buffers reduce both the number of defaults and the resulting losses. The loss reduction benefit increases as the magnitude of adverse shocks becomes higher. We find that a simple branch-breakup resolution strategy reduces the loss borne by the Federal Deposit Insurance Corporation (FDIC). The mitigation effect becomes higher as the fraction of assets resolved through auctions and auction competitiveness increase.     

Model Comparison for Temperature-Based Weather Derivatives in Mainland China

Abstract:

In this paper, we provide a comparison of two models of temperature-based weather derivatives. The Alaton et al. model (2002) and the continuous-time autoregressive (CAR) model of Benth et al. (2007) are applied to temperature data from twelve cities in China. The objective is to determine which is the better model for temperature derivative modeling in Chinese cities. We found the CAR model to be more accurate in terms of normality of residuals and smaller relative errors. However, the shortcomings of both the Alaton et al. model and the CAR model are revealed in this study as well.     

A robust VaR model under different time periods and weighting schemes

This paper analyses several volatility models by examining their ability to forecast Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx indices and is modeled for long and short trading positions by using non parametric, semi parametric and parametric methods. In order to choose one model among the various forecasting methods, a two-stage backtesting procedure is implemented. In the first stage the unconditional coverage test is used to examine the statistical accuracy of the models. In the second stage a loss function is applied to investigate whether the differences between the models, that calculated accurately the VaR, are statistically significant. Under this framework, the combination of a parametric model with the historical simulation produced robust results across the sample periods, market capitalization schemes, trading positions and confidence levels and therefore there is a risk measure that is reliable.

基于声誉效应的工程监理激励机制模型研究

为了提高工程监理工作积极性,基于Holmstrom-Milgrom模型,引入声誉效应,建立声誉效应和显性激励机制相结合的动态委托代理激励模型.分析声誉效应发挥激励机制作用条件,并与未考虑声誉效应的委托代理模型进行比较.结果表明:声誉效应在满足一定的条件下,引入声誉激励机制能够增加业主的期望收益,并且能提高监理的努力水平...     

1 bit量化数字DBPSK解调器

通过对 1 bit量化数字 DBPSK解调算法的系统仿真确定了其解调性能达到实用要求 ,阐述了同相正交积分型数字锁相环的实现方法 ,提出了 1 bit量化数字 DBPSK解调器的系统结构 ,并取得了良好的实际性能。     

Assessing the Least Squares Monte-Carlo Approach to American Option Valuation

A detailed analysis of the Least Squares Monte-Carlo (LSM) approach to American option valuation suggested in Longstaff and Schwartz (2001) is performed. We compare the specification of the cross-sectional regressions with Laguerre polynomials used in Longstaff and Schwartz (2001) with alternative specifications and show that some of these have numerically better properties. Furthermore, each of these specifications leads to a trade-off between the time used to calculate a price and the precision of that price. Comparing the method-specific trade-offs reveals that a modified specification using ordinary monomials is preferred over the specification based on Laguerre polynomials. Next, we generalize the pricing problem by considering options on multiple assets and we show that the LSM method can be implemented easily for dimensions as high as ten or more. Furthermore, we show that the LSM method is computationally more efficient than existing numerical methods. In particular, when the number of assets is high, say five, Finite Difference methods are infeasible, and we show that our modified LSM method is superior to the Binomial Model.     

悬索桥空缆状态数值分析

本文主要介绍悬索桥空缆状态数值分析方法。主要包括:基于无应力状态分析法和悬链线理论求解悬索桥空缆线形的数值迭代方法;等高、不等高悬链线求解方法及利用matlab编制相应程序实现空缆线形数据的求解。     

液压支架立柱动态性能仿真研究

通过对液压支架立柱的动态特性进行仿真,研究了立柱活柱腔的压力变化特征,为液压支架立柱防冲击系统研究提供必要的理论基础。     

A STATE-SPACE PARTITIONING METHOD FOR PRICING HIGH-DIMENSIONAL AMERICAN-STYLE OPTIONS

The pricing of American-style options by simulation-based methods is an important but difficult task primarily due to the feature of early exercise, particularly for high-dimensional derivatives. In this paper, a bundling method based on quasi-Monte Carlo sequences is proposed to price high-dimensional American-style options. The proposed method substantially extends Tilley's bundling algorithm to higher-dimensional situations. By using low-discrepancy points, this approach partitions the state space and forms bundles. A dynamic programming algorithm is then applied to the bundles to estimate the continuation value of an American-style option. A convergence proof of the algorithm is provided. A variety of examples with up to 15 dimensions are investigated numerically and the algorithm is able to produce computationally efficient results with good accuracy.