Decomposition of Japanese Yen Interest Rate Data Through Local Regression

Seven different Japanese Yen interest rates recorded on a daily basisfor the period from 1986 to 1992 are simultaneously analyzed. Byintroducing a new concept of short term trend, we decomposeeach interest rate series into three components, long termtrend, short term trend and irregular. It is obtained by atwo step lowess smoothing technique. After that, amultivariate autoregressive model (MAR) is fitted to the vectorvalued time series obtained by combining those seven irregularcomponents. The decomposition and MAR model fitting were quitesatisfactory. It enables us to understand well various aspects ofinterest rate series from the trends, the MAR (2) coefficientsand its residuals. The result is compared with the decompositionthrough sabl and the advantages of our procedure will bedemonstrated in relations to other parametric model fitting likeARCH or GARCH. Based on the decomposition we can have betterdaily prediction and more stable long term forecasting.     

Multiscaling and rough volatility: An empirical investigation

Pricing derivatives goes back to the acclaimed Black and Scholes model. However, such a modelling approach is known not to be able to reproduce some of the financial stylised facts, including the dynamics of volatility. In the mathematical finance community, it has therefore emerged a new paradigm, named rough volatility modelling, that represents the volatility dynamics of financial assets as a fractional Brownian motion with Hurst exponent very small, which indeed produces rough paths. At the same time, prices’ time series have been shown to be multiscaling, characterised by different Hurst scaling exponents. This paper assesses the interplay, if present, between price multiscaling and volatility roughness, defined as the (low) Hurst exponent of the volatility process. In particular, we perform extensive simulation experiments by using one of the leading rough volatility models present in the literature, the rough Bergomi model. A real data analysis is also conducted to test if the rough volatility model reproduces the same relationship. We find that the model can reproduce multiscaling features of the prices’ time series when a low value of the Hurst exponent is used, but it fails to reproduce what the real data says. Indeed, we find that the dependency between prices’ multiscaling and the Hurst exponent of the volatility process is diametrically opposite to what we find in real data, namely a negative interplay between the two.     

On time-heterogeneous credibility estimation

Abstract

Let χ_i be the total claim amount of an insurance policy in calendar year i. We assume that the χ_i's are conditionally independent given an unknown random parameter ø, and that for all i. In the present paper it is under these assumptions shown how to calculate the credibility estimator of m(ø) by recursive updating. We also give estimators for the unknown parameters α_i, β_i, and ?_i based on portfolio data. Finally we mention some related models.     

资本结构与市场择机:中国上市公司横截面数据的证明

企业的资本结构选择与企业市场价值相关,市场择机理论系统地阐述了二者之间的关系。与以往的研究不同,本文专门针对中国上市公司的横截面数据对市场择机理论的有效性进行了检验。实证研究结果表明,市值-账面比近期的水平横截面差异对资本结构的影响显著,但是市值-账面比的历史变化累计结果对资本结构几乎没有解释力。这说明中国上市公司的融资行为具有短期性倾向。     

风险投资退出时机研究

信息不对称是风险投资运作过程中的重要特征,而退出环节的信息不对称的表现形式不仅与其它环节相似,而且还有其特殊性.不同类型的信息不对称从不同的角度影响风险投资的退出时机.风险投资家在退出过程中必须以企业当前的状态为基础,结合风险资金市场供求和已确定的退出方式,准确把握风险投资运作过程中各种信息不对称的状况,选择能最大化其退出收益的最优退出时机.     

中国优势产业组合的动态演化机制研究——基于TERGM的实证分析

基于产品空间理论,利用1962―2015年128个国家和地区783个产业出口贸易数据,借助最新发展的时间指数随机图模型（TERGM）,深度剖析中国优势产业组合的动态演化机制。研究发现,中国优势产业组合存在显著的中心―边缘宏观网络结构特征,且由星型和三角形两种结构依赖所驱动,分别意味着轴式发展路径和链式发展路径。进一步地,动态ERGM研究结果表明,我国实施的渐进式发展战略存在时间依赖,且呈现出较强的“自稳定性”。结论表明,TERGM能够有效解决产品空间中的结构依赖和时间依赖问题,是系统描述和检验网络演化的有力分析工具,并且透过微观网络构局揭示中国优势产业组合的动态演化机制,为更好地分析产业结构的网络互赖性,实现产业转型升级提供指导。     

Reserve modelling and the aggregation of risks using time varying copula models

This paper is concerned with the appropriate claim reserving modelling and aggregation of risks in the insurance sector. In fact, literature review provided some methods to evaluate the total amount of reserves and solvency capital of different lines of business. However, these models were derived under the independent losses assumption. Thus, the total amount of reserves and capital may be inaccurate when losses are dependent, as it is the case in practice. In this paper, a novel model is proposed aiming to handle temporal dependence, both between a line of business claim's amounts and between the two lines of business claims. Generalized Autoregressive Conditional Sinistrality model is used to analyze the evolution in time of dependence and time varying copula functions are proposed to aggregate risks. To achieve such purpose, a simulation study, highlighting the impact on reserves and Solvency Capital Requirement, is performed. Results revealed that a diversification effect could be gained on the Solvency Capital when considering time varying dependence structures.     

构筑全球化语境中的城市与建筑观

全球化时代充满着现代性的冲突与矛盾,对中国当代城市与建筑产生了重大的影响.论文聚焦于社会发展的现代性特征以及文化交流和城市、建筑的关系,分析中国城市与建筑生存发展的时空特征,认为全球化发展的趋势和结果不应是文化的单极化,而是多元化,是地域文化的共存,进而澄清城市发展与评判的思想观念,建构基于地域时空维度的城市发展观.     

Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

Optimal prediction under LINLIN loss: Empirical evidence

I compare the forecasts of returns from the mean predictor (optimal under MSE), with the pseudo-optimal and optimal predictor for an asymmetric loss function under the assumption that agents have an asymmetric LINLIN loss function. The results strongly suggest not using the conditional mean predictor under conditions of asymmetry. In general, forecasts can be improved by the use of optimal predictor rather than the pseudo-optimal predictor, suggesting that the loss reduction from using the optimal predictor can actually be important for practitioners as well.