基于RCMRS的套期保值时变模型及实证研究

采用随机系数马尔科夫体制转换(RCMRS)模型对中国铜期货市场套期保值比进行估计.RCMRS模型跳出GARCH类模型基于新息描述的研究框架,视最优套期保值比为随机系数,直接估计出依赖于市场体制状态的时变套期保值比.市场体制状态在模型中被视为潜在变量,和其它参数一起通过最大化似然函数估计出来.由于考虑了不同市场体制状态对套期保值比的影响,RCMRS模型估计的最小方差套期保值比波动范围要小于GARCH类模型估计结果的波动范围.均值—方差效用函数不仅反映了风险,还同时反映了收益率及风险厌恶程度.在采用方差下降百分比测度套期保值效率的同时,另外采用均值—方差效用最大化原则对RCMRS模型与GARCH、VECM、VAR及OLS模型的套期保值表现进行了样本内和样本外比较.样本内比较支持RCMRS模型,而样本外比较则不利于RCMRS模型.     

基于风险约束的套期保值模型及沪深300股指实证研究

提出利用风险价值VaR建立套期保值资产组合的风险约束.以套期保值资产组合收益最大为目标,以控制套期保值资产组合风险为约束,建立了基于风险约束的套期保值模型.该模型在有效控制风险的基础上,可以大幅提高套期保值资产组合的收益.对沪深300股指现货和期货的数据进行了实证分析,对比了现有研究的最小二乘((OLS)、向量自回归(VAR)、向量误差修正(VEC)三种模型以及本文建立的基于风险约束的期货套期保值模型.样本内检验结果表明,本模型比现有研究模型的收益有大幅提高,平均增加81.6%.同时并没有失去对风险的控制,与现有研究模型只有5.32%的差别.对于样本外检验,模型在控制风险和提高收益两个方面都要优于现有研究模型.模型比现有研究模型平均可提高收益21.4%,平均降低风险3.61%.     

我国股指期货的套期保值比率研究

本文主要运用OLS、VAR、ECM、diagonal-BEKK、full-BEKK、scalar-BEKK对沪深300股指期货仿真交易的套期保值比率进行研究,比较了静态套期模型和动态套期保值模型的效果,并研究不同参数化形式对动态套期保值模型的影响。结果表明尽管动态套期保值在样本内要优于静态套期保值模型,但样本外效果却不是很好;另外,动态模型的不同的参数化形式对结果的影响也比较大。     

基于动态条件相关系数模型的石油市场套期保值比估计

以WTI和Brent两地的原油现货市场和期货市场为研究对象,选择对角化的动态条件相关(DCC)模型估计了市场间的动态条件相关系数,求解了WTI市场、Brent市场及跨市的动态套期保值比,评价了各种市场组合的套期保值效果.得到如下几点结论:第一,WTI市场的一体化程度高于Brent市场;第二,两个月期货的套期保值比高于1个月期货的套期保值比,WTI相应市场组合的套期保值比要高于Brent市场;第三,采取Brent期货对WTI现货进行对冲时,其套期保值比要高于用WTI期货对Brent现货对冲时的情形,也高于Brent市场的套期保值比;第四,套期保值比越高,套期保值效果越好.     

基于EWMA模型的铜期货动态套期保值效果研究

利用我国铜期货市场的真实交易数据以及铜现货市场的日结算价为研究对象,以投资组合收益率方差最小化为目标,建立了OLS,ECM,VECM,B-VAR 4种静态套期保值模型,针对金融市场收益率尖峰厚尾和波动率聚集的特征,构建了基于最优衰减因子的时变方差的EWMA模型的动态套期保值方案,并且对静态与动态模型的套期保值效果进行分析比较,不但考虑了所用实证数据的实际特点,而且考虑了套期保值比率预测的准确性和经济性,实证结果表明,该动态模型优于传统的静态套期保值模型.     

基于Copula-TGARCH模型的股指期货最佳套期保值比研究

本文基于一种新的Copula-TGARCH模型估计股指期货的最佳套期保值比,根据现货和期货收益率序列不同的尾部相依性,用不同的Copula函数形式(Gumbel,Clayton,Gaussian)拟合两者的相关性,并与其它的动态套期保值模型(ECM-CCC-GARCH和ECM-DVEC-GARCH)比较其套期保值的有效性。通过对香港恒生指数现货和期货的实证分析发现:无论样本期内、外,Copula-TGARCH模型的套期保值效果均优于其它模型,而基于非对称Gumbel Copula的套期保值比最佳。     

基于债券久期的国债期货套期保值模型分析

使用久期的方法在中国国债期货市场上进行套期保值是否有效?使用久期的方法研究国债期货套期保值的效率问题在国外已经很多,然而这种方法是否适合于目前中国的国债市场,相关研究还不多见,还有待进一步的证实.为此借鉴国外相关理论,采用比较研究的方法,以国债期货上市后2013年9月到2014年5月初,国债现货和国债期货的数据为样本,以基于久期的最优套期保值比率模型为主,其他模型为辅,比较出最优套期保值效率.研究结果表明,基于久期的套期保值方法在目前中国的国债市场效果一般.     

带有风险价值的最优期货套期保值策略

本文研究了带有风险价值约束的期货套期保值优化问题.用最优化方法获得了套期保值策略的存在性、求解模型的增广拉格朗日算法及其收敛性.文中的结果推广了期货收益率服从正态分布的单变量套期保值策略的研究,表现为用服从椭圆分布的随机变量刻画市场风险因子的厚尾特征、用风险价值控制套期保值的风险、构建了均值-VaR组合套期保值理论模型并给出了求解算法.     

中国股票市场最优套期保值比率研究——基于高阶矩HAR模型

金融资产收益率高阶矩风险和跳跃行为是套期保值策略的重要影响因素.文章将已实现高阶矩测度和跳跃风险测度引入HAR族波动率模型,构建高阶矩HAR族波动模型,并将Copula.函数与最优高阶矩波动率模型相结合,建立含高阶矩的Copula-HAR-RV-CJSJV-D-SK套期保值模型.以沪深300指数和中证500指数以及对应的股指期货构建套期保值策略.实证表明,从方差减少比率和超额收益率两方面来看,基于新模型的套期保值效果在样本内和样本外均优于传统静态套期保值模型、时变二元GARCH族套期保值模型和Copula-GARCH族套期保值模型.     

基于最小模糊方差的最优交叉套期保值模型

采用隶属度消除期货和现货收益率的异常波动对套期保值的影响,用非线性风险叠加原理描述多种期货对一种现货的组合风险,在最小方差套期保值模型的基础上,建立了基于最小模糊方差的最优交叉套期保值模型。本模型的创新与特色一是通过多种期货对一种现货的交叉套期保值提高了套期保值的有效性。这解决了仅用一种期货对一种现货进行交叉套期保值而导致风险较大的问题。二是用隶属函数对期货和现货收益率赋权消除离散程度大的收益率对最优套期比的影响。在采用隶属函数赋权的情况下,离散程度大的期货和现货收益率会被自动地赋予较小的权重,有效地减小了异常数据对最优套期比的影响。三是用非线性风险对冲原理叠加多种期货对一种现货的组合风险。通过期货与现货收益率的模糊协方差矩阵计算组合风险,反映了风险的非线性叠加和非线性对冲。四是现有研究的多种期货对一种现货的最小方差交叉套期保值模型仅仅是本模型在模糊隶属函数取1时的一个特例。当本模型的隶属函数为1时,本模型就是多种期货对一种现货的最小方差交叉套期保值模型;当本模型的隶属函数为1时、且研究对象为一种期货对一种现货套期保值时,本模型就是一种期货对一种现货的最小方差交叉套期保值模型。通过实证研究和与现有研究的对比分析,证明本研究所建立的模型可以有效的减小套期保值的风险并提高套期保值的有效性。     

Sudden changes in variance and time varying hedge ratios

This paper analyzes the influence of sudden changes in the unconditional volatility on the estimation and forecast of volatility and its impact on futures hedging strategies. We employ several multivariate GARCH models to estimate the optimal hedge ratios for the Spanish stock market including in each one some well-known patterns that may affect volatility forecasts (asymmetry and sudden changes). The main empirical results show that more complex models including sudden changes in volatility outperform the simpler models in hedging effectiveness both with in-sample and out-of-sample analysis. However, the evidence is stronger when the loss distribution tail is used as a measure for the effectiveness (Value at Risk (VaR) and Expected Shortfall (ES)) suggesting that traditional measures based on the variance of the hedged portfolio should be used with caution.     

Data-driven hedging of stock index options via deep learning

We develop deep learning models to learn the hedge ratio for S&P500 index options from options data. We compare different combinations of features and show that with sufficient training data, a feedforward neural network model with time to maturity, the Black-Scholes delta and market sentiment as inputs performs the best in the out-of-sample test under daily hedging. This model significantly outperforms delta hedging and a data-driven hedging model. Our results also demonstrate the importance of market sentiment for hedging.     

A dynamic stochastic programming model for international portfolio management

We develop a multi-stage stochastic programming model for international portfolio management in a dynamic setting. We model uncertainty in asset prices and exchange rates in terms of scenario trees that reflect the empirical distributions implied by market data. The model takes a holistic view of the problem. It considers portfolio rebalancing decisions over multiple periods in accordance with the contingencies of the scenario tree. The solution jointly determines capital allocations to international markets, the selection of assets within each market, and appropriate currency hedging levels. We investigate the performance of alternative hedging strategies through extensive numerical tests with real market data. We show that appropriate selection of currency forward contracts materially reduces risk in international portfolios. We further find that multi-stage models consistently outperform single-stage models. Our results demonstrate that the stochastic programming framework provides a flexible and effective decision support tool for international portfolio management.     

Better than pre-committed optimal mean-variance policy in a jump diffusion market

Dynamic mean-variance investment model can not be solved by dynamic programming directly due to the nonseparable structure of variance minimization problem. Instead of adopting embedding scheme, Lagrangian duality approach or mean-variance hedging approach, we transfer the model into mean field mean-variance formulation and derive the explicit pre-committed optimal mean-variance policy in a jump diffusion market. Similar to multi-period setting, the pre-committed optimal mean-variance policy is not time consistent in efficiency. When the wealth level of the investor exceeds some pre-given level, following pre-committed optimal mean-variance policy leads to irrational investment behaviors. Thus, we propose a semi-self-financing revised policy, in which the investor is allowed to withdraw partial of his wealth out of the market. And show the revised policy has a better investment performance in the sense of achieving the same mean-variance pair as pre-committed policy and receiving a nonnegative free cash flow stream.     

需求风险下套期保值与市场进入分析

需求风险是企业面临的主要风险之一,对企业的生产经营和管理决策具有重要影响。本文考虑由多个风险厌恶企业构成的产品竞争市场,分析了需求风险下企业参与套期保值和市场进入的决策问题。文章首先通过Cournot博弈分析了套期保值对于规避需求风险的作用和意义;然后,探讨了企业参与套期保值和市场进入的决策过程,并给出了三种情形下的市场均衡结构;最后,通过数值实验对结论进行了验证。研究表明:套期保值提高了企业应对需求风险的能力,使企业获得更高的产量和收益;参与套期保值企业数量随着进入市场企业数量的增加而减少;当市场竞争程度或市场费用增加时,将会有更多的企业选择参与套期保值,而选择进入市场的企业会减少。     

Longevity risk management for life and variable annuities: The effectiveness of static hedging using longevity bonds and derivatives

For many years, the longevity risk of individuals has been underestimated, as survival probabilities have improved across the developed world. The uncertainty and volatility of future longevity has posed significant risk issues for both individuals and product providers of annuities and pensions. This paper investigates the effectiveness of static hedging strategies for longevity risk management using longevity bonds and derivatives (q-forwards) for the retail products: life annuity, deferred life annuity, indexed life annuity, and variable annuity with guaranteed lifetime benefits. Improved market and mortality models are developed for the underlying risks in annuities. The market model is a regime-switching vector error correction model for GDP, inflation, interest rates, and share prices. The mortality model is a discrete-time logit model for mortality rates with age dependence. Models were estimated using Australian data. The basis risk between annuitant portfolios and population mortality was based on UK experience. Results show that static hedging using q-forwards or longevity bonds reduces the longevity risk substantially for life annuities, but significantly less for deferred annuities. For inflation-indexed annuities, static hedging of longevity is less effective because of the inflation risk. Variable annuities provide limited longevity protection compared to life annuities and indexed annuities, and as a result longevity risk hedging adds little value for these products.     

不同价格模式下考虑需求波动性和差异化服务的网约车平台定价决策

针对单个平台两种品牌网约车的最优定价问题,考虑平台服务质量的差异化和市场需求波动性,分别建立动态价格、差异化价格和静态价格模式下的网约车动态服务模型,运用多元函数和泛函的条件极值求得两种品牌网约车的最优定价策略。研究发现,平台最优动态价格和差异化价格均随需求波动时长单调变化,而最优静态价格并非单调。此外,平台提高差异化服务时,两种品牌网约车的最优价格均提高,但高服务质量的网约车会有更高的提价幅度;固定佣金报酬率增大时,平台最优价格均提高,但边际损失成本较大的网约车会有更高的提价幅度。最后,通过数值仿真对不同价格模式下的平台利润进行比较和灵敏度分析,并发现平台利润在市场需求稳定时差异不大。