An evolutionary Cournot oligopoly model with imitators and perfect foresight best responders

We consider the competition among quantity setting players in a linear evolutionary environment. To set their outputs, players adopt, alternatively, the best response rule having perfect foresight or an imitative rule. Players are allowed to change their behavior through an evolutionary mechanism according to which the rule with better performance will attract more followers. The relevant stationary state of the model describes a scenario where players produce at the Cournot‐Nash level. Due to the presence of imitative behavior, we find that the number of players and implementation costs, needed to the best response exploitation, have an ambiguous role in determining the stability properties of the equilibrium and double stability thresholds can be observed. Differently, the role of the intensity of choice, representing the evolutionary propensity to switch to the most profitable rule, has a destabilizing role, in line with the common occurrence in evolutionary models. The global analysis of the model reveals that increasing values of the intensity of choice parameter determine increasing dynamic complexities for the internal attractor representing a population where both decision mechanisms coexist.     

Transport infrastructure equities in mixed-asset portfolios: estimating risk with a Garch-Copula CVaR model

Transport infrastructure is an important subsector within infrastructure, but knowledge of its equities in terms of risk-return characteristics and contribution to portfolio performance is still limited. This study assesses the subsector individually and in a multi-asset, index-based portfolio. In doing so, we apply a t-Copula-based Conditional Value-at-Risk model to simulate risk and returns. Our findings reveal that the subsector has a relatively low dependency on other equities, performs like other alternative asset classes such as general real estate, and does not grant significant risk diversification benefits for mainstream institutional investors such as pension funds. Investors aiming for higher target returns may however assign substantial weights to transport infrastructure, supporting our conjecture that it does not share the same asset class characteristics as general infrastructure. By contrasting Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) scores for both the mean-variance framework and the t-Copula simulation, we further document the limitations of traditional VaR approaches. Hence, this study’s results support the use of risk assessment tools that incorporate non-normal distributions to represent multivariate dependence structures.     

A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.     

证券公司风险监管制度有效性的实证研究

按照国际清算银行(BIS)颁布的新资本充足率规范,以49个中国证券公司实际自营投资组合为样本、采用基于VaR的内部模型法,估算各证券公司自营投资的市场风险和应计提资本,并以之检验中国证券公司新老资本监管制度的有效性.实证结果显示:改革后的2006版资本监管制度比老制度有所改善,但对风险的反映不如内部模型法准确,中国监管制度改革的方向应是采用内部模型法.因主要品种风险调整比例设置偏低,新制度平均低估证券公司自营风险29%,建议监管规则调整上海180指数股票、ST类股票、基金、企业债、可转债等品种的风险调整比例.     

风险价值及压力测试在采购策略中的应用

首先分析了供应链采购风险管理的重要性,然后分别介绍了VaR和压力测试这两种风险测度方法,并在此基础上建立了采购风险度量模型,为制定科学的采购策略提供了量化的决策依据。     

Conditional VaR using EVT - Towards a planned margin scheme

This paper constructs a robust Value-at-Risk (VaR) measure for the Indian stock markets by combining two well-known facts about equity return time series — dynamic volatility resulting in the well-recognized phenomenon of volatility clustering, and non-normality giving rise to fat tails of the return distribution. While the phenomenon of volatility dynamics has been extensively studied using GARCH model and its many relatives, the application of Extreme Value Theory (EVT) is relatively recent in tracking extreme losses in the study of risk measurement. There are recent applications of Extreme Value Theory to estimate the unexpected losses due to extreme events and hence modify the current methodology of VaR. Extreme value theory (EVT) has been used to analyze financial data showing clear non-normal behavior. We combine the two methodologies to come up with a robust model with much enhanced predictive abilities. A robust model would obviate the need for imposing special ad hoc margins by the regulator in times of extreme volatility. A rule based margin system would increase efficiency of the price discovery process and also the market integrity with the regulator no longer seen as managing volatility.     

Parameter risk in time-series mortality forecasts

The projection of mortality rates is an essential part of valuing liabilities in life insurance portfolios and pension schemes. An important tool for risk management and solvency purposes is a stochastic projection model. We show that ARIMA models can be better representations of mortality time-series than simple random-walk models. We also consider the issue of parameter risk in time-series models from the point of view of an insurer using them for regulatory risk reporting – formulae are given for decomposing overall risk into undiversifiable trend risk (parameter uncertainty) and diversifiable volatility. Particular attention is given to the contrasts in how academic researchers might view these models and how insurance regulators and practitioners in life offices might use them. Using a bootstrap method we find that, while certain kinds of parameter risk are negligible, others are too material to ignore. We also find that an objective model selection criterion, such as goodness of fit to past data, can result in the selection of a model with unstable parameter values. While this aspect of the model is superficially undesirable, it also leads to slightly higher capital requirements and thus makes the model of keen interest to regulators. Our conclusions have relevance to insurers using value-at-risk capital assessments in the European Union under Solvency II, but also territories using conditional tail expectations such as Australia, Canada and Switzerland.     

基于VaR模型的商业银行体系系统性风险研究

本文以中国2016年之前上市商业银行作为中国银行业的代表,测算银行业系统性 风险VaR。整体来讲,我国银行业系统性风险较低,但VaR在2015年较高。虽如此,我国银行业资本持有量能够抵御银行体系的系统性风险。在系统性风险VaR贡献度方面,本文实证分析表明,在样本期间内,浦发银行、中国银行、农业银行、交通银行贡献度较高。银行体系系统 性风险VaR受GDP增长率和沪深300指数收益率的显著影响。     

金融风险计量研究--基于VaR模型的分析

本文通过分析风险的本质涵义,提出了定量分析金融风险的路径;通过回顾对于金融风险定量研究的一些模型和方法,详细介绍了VaR模型及其估计技术,指出了风险定量技术的广阔应用前景。     

VaR及对证券投资基金的VaR测算

本在对VaR方法的分析的基础上，选择了GAROH模型度量证券投资基金的风险，并计算了样本期间22只基金的VaR值。在此基础上给出了各基金对应的RAROC值，并对结果进行了分析。