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GARCH(p,q) 过程的四阶矩结构AI 解读

In this paper, a necessary and sufficient condition for the existence of the unconditional fourth moment of the GARCH( p , q ) process is given and also an expression for the moment itself. Furthermore, the autocorrelation function of the centered and squared observations of this process is derived. The statistical theory is further illustrated by a few special cases such as the GARCH(2,2) process and the ARCH( q ) process.

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GARCH 模型中基于最大似然的估计量效率比较AI 解读

In this paper, we investigate the loss of asymptotic efficiency of semiparametric and quasi-maximum likelihood estimators relative to maximum likelihood estimators in models with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). For a general time-varying location-scale model, the factors that contribute to differences in efficiency among the estimators can be divided in two categories. One pertains to the parametric specifications of the conditional mean…

利用日内极差校准波动率扩散并提取远期积分方差AI 解读

A common model for security price dynamics is the continuous-time stochastic volatility model. For this model, Hull and White (1987) show that the price of a derivative claim is the conditional expectation of the Black-Scholes price with the forward integrated variance replacing the Black-Scholes variance. Implementing the Hull and White characterization requires both estimates of the price dynamics and the conditional distribution of the forward integrated variance given…

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随机波动率模型中的贝叶斯单位根检验AI 解读

This article uses a Bayesian unit-root test in stochastic volatility models. The time series of interest is the volatility that is unobservable. The unit-root testing is based on the posterior odds ratio, which is approximated by Markov-chain Monte Carlo methods. Simulations show that the testing procedure is efficient for moderate sample size. The unit-root hypothesis is rejected in seven market indexes, and some evidence of nonstationarity is observed in the TWSI of Taiwan.

利用金融与经济变量预测股票收益的非线性方法AI 解读

Inspired by the linear predictability and nonlinearity found in the finance literature, this article examines the nonlinear predictability of the excess returns. The relationship between the excess returns and the predicting variables is recursively modeled by a neural-network model, which is capable of performing flexible nonlinear functional approximation. The nonlinear neural-network model is found to have better in-sample fit and out-of-sample forecasts compared to its…

将涨跌停视为均衡期货价格删失观测的 GARCH 估计AI 解读

We develop an algorithm for estimating generalized autoregressive conditional heteroscedasticity models for time series containing some censored observations. Motivation for the algorithm comes from those futures markets and some equity markets that have limits constraining the maximum allowable movement in price in a day. When a limit is reached, trading stops and the equilibrium price is not observed. We maximize the likelihood function by replacing the unobservable…

定性响应时间序列的条件异方差：银行最优惠贷款利率的吉布斯抽样分析AI 解读

Previous time series applications of qualitative response models have ignored features of the data, such as conditional heteroscedasticity, that are routinely addressed in time series econometrics of financial data. This article addresses this issue by adding Markov-switching heteroscedasticity to a dynamic ordered probit model of discrete changes in the bank prime lending rate and estimating via the Gibbs sampler. The dynamic ordered probit model of Eichengreen, Watson, and…

一类 GARCH 过程的矩性质AI 解读

This paper considers the moments of a family of first-order GARCH processes. First, a general condition of the existence of any integer moment of the absolute values of the observations is given. Second, a general expression for this moment as a function of lower-order moments is derived. Third, the kurtosis and the autocorrelation function of the squared and absolute-valued observations are derived. The results apply to a host of different GARCH parameterizations. Finally,…

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贝叶斯套利阈值分析AI 解读

A Bayesian estimation procedure is developed for estimating multiple-regime (multiple-threshold) error-correction models appropriate for deviations from financial arbitrage relationships. This approach has clear advantages over classical stepwise threshold autoregressive analysis. Unlike many other applications of threshold models, the knowledge of some costs involved in setting up arbitrage positions allows us to specify an informative prior. To illustrate the Bayesian…

广义矩估计模型中的结构稳定性检验AI 解读

This article proposes a new methodology for testing structural stability in models estimated via generalized method of moments. Like most previous studies of this general problem, attention is focused on the case in which some aspect of the model potentially changes at a single point in the sample, known as the “breakpoint.” Unlike this earlier work, however, our approach is based on a decomposition of the null hypothesis into two components involving parameter constancy and…

重尾实用指南：统计技术与应用AI 解读

Part 1 Applications: heavy tailed probability distributions in the World Wide Web, M.E. Crovella et al self-similarity and heavy tails - structural modelling of network traffic, W. Willinger et al heavy tails in high-frequency financial data, U.A. Muller et al stable paretian modelling in finance - some empirical and theoretical aspects, S. Mittnik et al risk management and quantile estimation, F. Bassi et al. Part 2 Time series: analyzing stable time series, R.J. Adler et…

水平序列记忆参数估计中的长短记忆条件异方差AI 解读

Semiparametric estimates of long memory seem useful in the analysis of long financial time series because they are consistent under much broader conditions than parametric estimates. However, recent large sample theory for semiparametric estimates forbids conditional heteroskedasticity. We show that a leading semiparametric estimate, the Gaussian or local Whittle one, can be consistent and have the same limiting distribution under conditional heteroskedasticity as under the…

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资产收益波动率的单位根检验AI 解读

It is now well established that the volatility of asset returns is time varying and highly persistent. One leading model that is used to represent these features of the data is the stochastic volatility model. The researcher may test for non‐stationarity of the volatility process by testing for a unit root in the log‐squared time series. This strategy for inference has many advantages, but is not followed in practice because these unit root tests are known to have very poor…

资产收益波动率的单位根检验AI 解读

It is now well established that the volatility of asset returns is time varying and highly persistent. One leading model that is used to represent these features of the data is the stochastic volatility model. The researcher may test for non-stationarity of the volatility process by testing for a unit root in the log-squared time series. This strategy for inference has many advantages, but is not followed in practice because these unit root tests are known to have very poor…

We present an econometrically tractable life cycle labor supply model for panel data including intertemporally progressive taxes on uncertai wage and nonwge incomes. Our two‐stage fixedeffects generalized method‐of‐moments approach first extimates intretemporal and then intertemporal preferences. Specification testing domonstrates the value of incorporating joint progressive taxation of labor and nonlabor incomes, Results for prime‐age men emphasize the roles played by…

均值—方差有效投资组合权重估计的抽样误差AI 解读

This paper presents an exact finite‐sample statistical procedure for testing hypotheses about the weights of mean‐variance efficient portfolios. The estimation and inference procedures on efficient portfolio weights are performed in the same way as for the coefficients in an OLS regression. OLS t ‐ and F ‐statistics can be used for tests on efficient weights, and when returns are multivariate normal, these statistics have exact t and F distributions in a finite sample. Using…

随机波动率模型的非线性滤波方法及日度股票收益应用AI 解读

This paper develops a new method for the analysis of stochastic volatility (SV) models. Since volatility is a latent variable in SV models, it is difficult to evaluate the exact likelihood. In this paper, a non-linear filter which yields the exact likelihood of SV models is employed. Solving a series of integrals in this filter by piecewise linear approximations with randomly chosen nodes produces the likelihood, which is maximized to obtain estimates of the SV parameters. A…

随机波动率模型的非线性滤波方法及日股票收益应用AI 解读

This paper develops a new method for the analysis of stochastic volatility (SV) models. Since volatility is a latent variable in SV models, it is difficult to evaluate the exact likelihood. In this paper, a non-linear filter which yields the exact likelihood of SV models is employed. Solving a series of integrals in this filter by piecewise linear approximations with randomly chosen nodes produces the likelihood, which is maximized to obtain estimates of the SV parameters. A…