基于GARCH模型的VaR方法在上证市场中的应用

VaR(Value at Risk,在险价值)作为市场风险的度量方法之一,能够有效地度量金融市场的风险。本文在介绍了VaR的概念、特点以及计算方法的基础之上,利用GARCH模型估计、预测股市的VaR值,并对上证指数进行风险度量的实证研究。分析结果表明基于GARCH模型的VaR方法能够较好地反映出股市的风险,适合在上证市场进行风险管理。     

极值理论POT模型与阈值选取研究

极端值模型是准确估计"厚尾"分布金融资产回报市场风险的有力工具,本文研究了基于GPD分布的极值理论中的POT模型,并通过比较分析各种方法选取的阈值,得出最优的阈值u,最后通过POT模型计算VaR和CVaR值。     

我国商业银行利率风险模型及实证分析

银行间同业拆借利率是商业银行利率风险测度的重要指标。我国银行间同业拆借利率中的隔夜拆借利率具有明显的尖峰、厚尾特征,可以利用ARCH族模型估计得到的条件标准差来度量其VaR值。我们对2007年我国商业银行数据进行了实证分析,结果表明,只有ARCH(1)和EARCH(1,1)模型能较好的拟合隔夜拆借利率,且从VAR值可以看出我国商业银行利率的日风险巨大。     

基于动态半参数法的我国证券市场风险研究

本文用动态半参数法估计了我国证券市场的风险值VaR。由于金融市场存在利好与利空信息所带来的非对称性,因此本文利用所选数据,建立EGARCH波动模型,同时用参数法、历史模拟法计算了VaR,并用Kupiec(1995)的Back-test对每种方法的有效性进行了评价。突出了动态半参数法对数据拟合的优越性。     

极值理论在VaR中的应用及对沪深股市的实证分析

通过利用极值理论(EVT)计算风险价值(VaR)的方法与正态分布和实际分布的VaR结果进行比较，并应用沪深指数日收益率进行的实证研究表明，在极端条件下，用极值方法估计的VaR值有更高的准确性。     

不同类型商业银行利率风险的实证研究——基于银行间同业拆借利率视角

本文通过建立基于三种不同分布下的GARCH族模型,计算银行间同业拆借头寸VaR值,定量分析了我国商业银行利率风险状况,并对不同类型商业银行利率风险的差异性进行了考察。实证结果表明,EGARCH-GED—VaR模型能够较好地估计银行间同业拆借利率风险。估计结果显示,我国银行间同业拆借利率风险较高且市场利率波动存在杠杆效应(TARCH);不同类型商业银行利率风险存在显著差异,国有商业银行和股份制商业银行利率风险最高,城商行次之,外资银行、农村商业银行和合作银行风险较低。     

货币错配风险的特征与组合评估

提出一种基于VaR模型失败率的VaR风险综合值的权数构造方法,并利用它测度货币错配VaR风险上下限值和分析风险变动特征。同时AR(m)—ARCH类模型VaR组合风险测度表明:组合模型的VaR值比单一模型更合理和可靠;具有债权型特征的我国货币错配风险主要来自于外币资产增长快于外币负债的增长;货币错配风险区间的波动宽度与外币资产与外币负债增长之比值呈正相关关系;在本币升值和贬值之间权衡得失时,本币升值比贬值更有利于债权型货币错配风险的弱化。     

基于VAR历史模拟模型的融资融券市场风险衡量研究

市场风险是融资融券交易中的主要风险。本文把VaR历史模拟法引入融资融券市场风险的衡量中去,以宏源证券等五只融资融券标的股票为例,具体衡量了市场风险VaR值的大小,并通过事后检验来考察模型的可信性。在事后检验的基础上,比较一段时期内的VaR值得出动态保证金比例。     

我国银行间短期债券回购利率风险的度量研究——基于内部模型法(VaR)分析

本文用基于正态分布和t分布的GARCH类模型计算的VaR度量了我国银行间7天国债回购的短期利率风险,并与EWMA法、Delta-正态法和历史法对样本外动态VaR值计量的准确性进行了比较,得出以下几点结论:(1)EWMA和IGARCH(1,1)-N模型能够准确度量7天回购的利率风险;(2)EWMA方法的风险度量精度最高;(3)基于Delta-正态法、历史法以及基于t分布的GARCH类模型会严重高估风险;(4)基于正态分布的GARCH类模型的VaR并不会低估风险。     

外汇市场的VaR风险度量

目前,金融资产市场风险的通用度量工具为VaR(Value at Risk)模型("风险估值"模型),在几个巴塞尔协议形成后,用VaR度量金融风险更是受到普遍关注。文章试以外汇市场的风险度量为研究对象,收集近三年来的外汇交易收盘价,使用VaR模型,论证利用VaR技术计算外汇风险的可行性。     

Effects of Corporate Diversification: Evidence From the Property–Liability Insurance Industry

Using a sample of property–liability insurers over the period 1995–2004, we develop and test a model that explains performance as a function of line‐of‐business diversification and other correlates. Our results indicate that undiversified insurers consistently outperform diversified insurers. In terms of accounting performance, we find a diversification penalty of at least 1 percent of return on assets or 2 percent of return on equity. These findings are robust to corrections for potential endogeneity bias, alternative risk measures, alternative diversification measures, and an alternative estimation technique. Using a market‐based performance measure (Tobin's Q) we find that the market applies a significant discount to diversified insurers. The existence of a diversification penalty (and diversification discount) provides strong support for the strategic focus hypothesis. We also find that insurance groups underperform unaffiliated insurers and that stock insurers outperform mutuals.     

Designing feasible and effective health plan payments in countries with data availability constraints

Risk equalization schemes, which transfer money to/from insurers that have above/below average risks, are a fundamental tool in regulated health insurance markets in many countries. Risk sharing (the transfer of some responsibility for costs from a plan to the regulator or the overall insurance market), are an additional method of insulating insurers who attract higher-than-average risks. This paper proposes, implements and quantifies incorporating risk sharing within a risk equalization scheme that can be applied in a data-poor context. Using Chile's private health insurance market as case study, we show that modest amount of risk sharing greatly improves fit even in simple demographic-based risk equalization. Expanding the model's formula to include morbidity-based adjustors and risk sharing redirects compensations at insurer level and reduces opportunity to engage in profitable risk selection at the group level. Our emphasis on feasibility may make alternatives proposed attractive to countries facing data-availability constraints.     

How Does the Use of Credit Default Swaps Affect Firm Risk and Value? Evidence from US Life and Property/Casualty Insurance Companies

This study uses a unique credit default swap (CDS) transaction data set of insurers to examine the effects of CDS usage on the risk profile and firm value of US insurance companies for the period 2001‐2009. Applying a Heckman two‐stage model to adjust for the potential endogeneity of CDS usage with respect to firm risk and firm value, we find consistent evidence that the utilization of CDS for income generation purposes is associated with greater market risk, deterioration of financial performance, and lower firm value, for both Life and Property/Casualty insurers.     

Combining fair pricing and capital requirements for non-life insurance companies

The aim of this article is to identify fair equity-premium combinations for non-life insurers that satisfy solvency capital requirements imposed by regulatory authorities. In particular, we compare target capital derived using the value at risk concept as planned for Solvency II in the European Union with the tail value at risk concept as required by the Swiss Solvency Test. The model framework uses Merton’s jump-diffusion process for the market value of liabilities and a geometric Brownian motion for the asset process; fair valuation is conducted using option pricing theory. We show that even if regulatory requirements are satisfied under different risk measures and parameterizations, the associated costs of insolvency – measured with the insurer’s default put option value – can differ substantially.     

Estimating the Cost of Equity for Property-Liability Insurance Companies

Due to the highly skewed and heavy‐tailed distributions associated with the insurance claims process, we evaluate the Rubinstein‐Leland (RL) model for its ability to improve the cost of equity estimates of insurance companies because of its distribution‐free feature. Our analyses show that there is as large as a 94‐basis‐point difference in the estimated cost of insurance equity between the RL model and the capital asset pricing model (CAPM) for the sample of property‐liability insurers with more severe departures from normality. In addition, consistent with our hypotheses, significant differences in the market risk estimates are found for insurers with return distributions that are asymmetrically distributed, and for small insurers. Third, we find significant performance improvements from using the RL model by showing smaller values of excess return of the expected return of the portfolio to the model return for a portfolio of insurers with returns that are more skewed and for a portfolio of small insurers. Finally, our panel data analysis shows the differences in the market risk estimates are significantly influenced by firm size, degree of leverage, and degree of asymmetry. The implication is that insurers should use the RL model rather than the CAPM to estimate its cost of capital if the insurer is small (assets size is less than $2,291 million), and/or its returns are not symmetrical (the value of skewness is greater than 0.509 or less than ?0.509).     

基于产品组合视角我国寿险公司综合免疫策略研究

针对我国人寿保险公司的经营面临着日益加大的市场风险与死亡率风险．提出了一种同时规避死亡率风险与利率风险的综合免疫策略。假设寿险公司采取资产主导的资产负债管理模式，通过对寿险公司两类主要产品（死亡给付产品和生存给付产品）的组合比例调整，实现对死亡率风险和利率风险的双重免疫。为此首先建立寿险公司死亡率自然对冲模型，在此基础上将利率风险引入模型，进而构建起同时规避死亡率和利率风险的寿险公司综合免疫产品组合策略。     

Haar wavelets-based approach for quantifying credit portfolio losses

This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is particularly suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. WA is an accurate, robust and fast method, allowing the estimation of the VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability.     

Capital-based regulation,portfolio risk and capital determination: Empirical evidence from the US property–liability insurers

This paper examines the impact of capital-based regulation on the insurer’s risk and capital adjustments in the US property–liability insurance industry. We conduct the three-stage least squares (3SLS) procedure to estimate a simultaneous equations model. The key finding is that undercapitalized insurers increase capital to avoid regulatory costs and take more risks to generate higher returns. We also investigate firm characteristics that determine the insurer’s capital structure. The results indicate that insurers appear to rely heavily on retained earnings to make up their capital shortage and insurers with greater growth opportunity may hold high levels of capital to control for agency problems. Robustness tests with an alternative risk measure and subsamples present consistent results.     

Cream Skimming: Innovations in Insurance Risk Classification and Adverse Selection

We demonstrate how innovations in insurance risk classification can lead to adverse selection, or cream skimming, against insurers that are slow to adopt such pricing innovations. Using a model in which insurers with insufficient pricing data cannot differentiate between low‐ and high‐risk policyholders and therefore charge both the same premium, we show how innovative insurers develop new risk classification data to identify overcharged low‐risk policyholders and attract them from rival insurers with reduced prices. Less innovative insurers thus insure a growing percentage of high‐risk customers, resulting in adverse selection attributable to their informational disadvantage. Next, we examine two cases in which “Big Data” innovations in risk classification led to concerns about cream skimming among U.S. auto insurers. First, we track the rapid adoption of credit‐based insurance scores as pricing variables in personal auto insurance markets. Second, we examine the growing popularity of usage‐based insurance programs like telematics, plans in which insurers use data on policyholders’ actual driving behavior to set prices that attract low‐risk customers. Issues associated with the execution of such pricing strategies are discussed. In both cases, we document how rival insurers quickly adopt successful innovations to reduce their exposure to adverse selection.     

Organizational structure,risk-based capital requirements,and the sales of downgraded bonds

Using bond downgrades as external shocks to life insurers’ asset risk, we document several findings of the impact of organizational structure and risk factors on investment risk taking. First, we find that mutual insurers and widely-held stock insurers are more likely to sell downgraded bonds than are closely-held stock insurers. Second, we find evidence that insurers are less likely to sell downgraded bonds that remain in the same rating class than bonds downgraded to a lower rating class. The result implies that insurers sell downgraded bonds mainly because of additional capital charge is imposed, not because of downgrade itself. In other words, risk factors in risk-based capital regulation do matter on life insurers’ investment risk taking. Finally, we find that life insurers might be reluctant to sell downgraded bonds at fire-sale prices during the 2008–2009 financial crisis.