亚洲地区股票指数收益率的波动性研究——基于GARCH族模型

为防范股票市场上的不确定性和风险,有效地度量股票指数收益率的波动性显得尤为重要。本文运用GARCH族模型,拟合了股票指数收益率的波动性方程,并实证研究了亚洲地区四个最具代表性国家:日本、中国、印度和韩国的股票指数收益率的波动性。结果表明:亚洲地区股票指数收益率的波动呈现出聚集性和持续性,股票市场存在着冲击的非对称性;中国和印度的股票市场抗风险能力比日本和韩国弱,股票指数收益率的波动性带来的负面影响更大。     

基于经济资本的我国保险公司投资风险限额配置研究

投资风险限额管理是保险公司对投资风险进行管理的重要手段,是保险公司建立有效风险管理体系不可或缺的组成部分。保险公司投资风险限额管理主要包括投资风险限额配置、风险限额监控和风险限额动态调整三个环节,其中风险限额配置是整个风险限额管理流程的基础。运用GARCH模型和GJR模型,并结合Copula理论,探讨了投资风险限额配置的方法,通过实证分析证实投资组合之间存在分散化效应,各投资风险限额之和大于总风险限额,并得出投资风险限额优化配置模型调整资产配置,可以显著提高保险公司投资绩效。     

融资融券试点对我国股票市场波动性的影响实证研究

融资融券交易正式启动对我国股票市场将产生什么样的波动性影响,是学术界和理论界共同关注的焦点。本文在前人研究基础上,从我国实际情况出发,以标的证券指数——上证50指数与深证成指指数作为影响我国股票市场的代表展开实证,运用GARCH族模型,引入虚拟变量D,其中D用来刻画融资融券推出前后对我国股票市场的影响。通过建模,得出融资融券试点一年多时间以来有利于减小我国股票市场波动性的结论。     

Range-based multivariate volatility model with double smooth transition in conditional correlation

This paper proposes a multivariate model named Double Smooth Transition Conditional Correlation Conditional Autoregressive Range (DSTCC-CARR for short). Determined by two transition variables, the correlations smoothly transit from one state to another. Together with the DSTCC-GARCH model, the model is employed to investigate the interdependence between Hong Kong's and international stock markets. It is proved by the empirical analysis that the DSTCC-CARR model is more credible and efficient than the DSTCC-GARCH model. Linkages among Hong Kong's and other world's markets captured by these two models are testified to be consistent with history, and have meaningful interpretations.     

Modeling time series information into option prices: An empirical evaluation of statistical projection and GARCH option pricing model

This paper compares the empirical performances of statistical projection models with those of the Black–Scholes (adapted to account for skew) and the GARCH option pricing models. Empirical analysis on S&P500 index options shows that the out-of-sample pricing and projected trading performances of the semi-parametric and nonparametric projection models are substantially better than more traditional models. Results further indicate that econometric models based on nonlinear projections of observable inputs perform better than models based on OLS projections, consistent with the notion that the true unobservable option pricing model is inherently a nonlinear function of its inputs. The econometric option models presented in this paper should prove useful and complement mainstream mathematical modeling methods in both research and practice.     

Futures Trading Activity and Commodity Cash Price Volatility

Abstract:   This paper examines the lead‐lag relationship between futures trading activity (volume and open interest) and cash price volatility for major agricultural commodities. Granger causality tests and generalized forecast error variance decompositions show that an unexpected increase in futures trading volume unidirectionally causes an increase in cash price volatility for most commodities. Likewise, there is a weak causal feedback between open interest and cash price volatility. These findings are generally consistent with the destabilizing effect of futures trading on agricultural commodity markets.     

Exchange Rate Risk and Interest Rate: A Case Study for Turkey

This paper examines the effect of exchange rate risk on interest rates within the uncovered interest rate parity condition for Turkey. When the interest rate is measured with the Treasury auction interest rate and the exchange rate risk is measured with the conditional variance of the exchange rate, then we found that there is a positive relation between the exchange rate risk and interest rate with the data from December 1986 to January 2001.     

The response of term rates to monetary policy uncertainty

This paper shows that greater uncertainty about monetary policy can lead to a decline in nominal interest rates. In the context of a limited participation model, monetary policy uncertainty is modeled as a mean preserving spread in the distribution for the money growth process. This increase in uncertainty lowers the yield on short-term maturity bonds because the household sector responds by increasing liquidity in the banking sector. Long-term maturity bonds also have lower yields but this decrease is a result of the effect that greater uncertainty has on the nominal intertemporal rate of substitution—which is a convex function of money growth. We examine the nature of these relations empirically by introducing the GARCH-SVAR model—a multivariate generalization of the GARCH-M model. The predictions of the model are broadly supported by the data: higher uncertainty in the federal funds rate can lower the yields of the three- and six-month treasury bill rates.     

Modeling conditional return autocorrelation

Empirical estimates of conditional return autocorrelation are generated over the period 1973 to 2000 for S&P500 index data, as well as for a small selection of individual U.S. stocks. We find that conditional autocorrelation is highly variable, and these dynamics are consistent with changes in point autocorrelation estimates generated in various subperiods. The conditional autocorrelation estimates for some stocks exhibited a pattern of mean reversion, while for others, evidence of long-term trends and structural breaks was found. While we were unable to uncover what characteristics drive the nature of these autocorrelation patterns, our analysis ruled out industry, investor type or degree of internationalisation as explanations.     

Value-at-Risk Prediction: A Comparison of Alternative Strategies

Given the growing need for managing financial risk, risk predictionplays an increasing role in banking and finance. In this studywe compare the out-of-sample performance of existing methodsand some new models for predicting value-at-risk (VaR) in aunivariate context. Using more than 30 years of the daily returndata on the NASDAQ Composite Index, we find that most approachesperform inadequately, although several models are acceptableunder current regulatory assessment rules for model adequacy.A hybrid method, combining a heavy-tailed generalized autoregressiveconditionally heteroskedastic (GARCH) filter with an extremevalue theory-based approach, performs best overall, closelyfollowed by a variant on a filtered historical simulation, anda new model based on heteroskedastic mixture distributions.Conditional autoregressive VaR (CAViaR) models perform inadequately,though an extension to a particular CAViaR model is shown tooutperform the others.