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利用核平滑方法估计半参数 ARCH(∞) 模型AI 解读

We investigate a class of semiparametric ARCH(∞) models that includes as a special case the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible function form with regard to the “news impact” function. We show that the functional part of the model satisfies a type II linear integral equation and give simple conditions under which there is a unique solution. We propose an estimation method that is based…

未知形式条件异方差下时间序列条件均值模型的广义谱检验AI 解读

Economic theories in time series contexts usually have implications on and only on the conditional mean dynamics of underlying economic variables. We propose a new class of specification tests for time series conditional mean models, where the dimension of the conditioning information set may be infinite. Both linear and nonlinear conditional mean specifications are covered. The tests can detect a wide range of model misspecifications in mean while being robust to…

财富—消费比与消费—习惯比AI 解读

Recent research finds that the ratio of aggregate wealth to consumption emerges as a strong forecaster of stock returns. This article reports that the wealth-consumption ratio remains as a strong predictor of stock returns at the presence of the consumption-habit ratio. Most of the variability of the wealth-consumption ratio is not related to the movements of the lagged, contemporaneous, or leading consumption-habit ratios. The results reject the hypothesis that the…

股权溢价的结构突变AI 解读

This article uses Bayesian marginal likelihood analysis to compare univariate models of the stock return behavior and test for structural breaks in the equity premium. The analysis favors a model that relates the equity premium to Markov-switching changes in the level of market volatility and accommodates volatility feedback. For this model, there is evidence of a one-time structural break in the equity premium in the 1940s, with no evidence of additional breaks in the…

金融交易价格与交易时间的离散状态连续时间模型AI 解读

Financial transaction prices typically lie on a discrete grid of values and arrive at random times. This paper proposes an econometric model with this structure. The distribution of each price change is a multinomial, conditional on past information and the time interval between the transactions. The proposed autoregressive conditional multinomial (ACM) model is not restricted to be Markov or symmetric in response to shocks; however, such restrictions can be imposed. The…

面向天气衍生品的天气预测AI 解读

We take a simple time series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time series modeling reveals conditional mean dynamics and, crucially, strong conditional variance dynamics in daily average temperature, and it reveals sharp differences between the…

用马尔可夫转换模型与工具变量检验远期汇率无偏假说AI 解读

Abstract This paper develops a model for the forward and spot exchange rate which allows for the presence of a Markov switching risk premium in the forward market and considers the issue of testing the unbiased forward exchange rate (UFER) hypothesis. Using US/UK data, it is shown that the UFER hypothesis cannot be rejected, provided that instrumental variables are used to account for within‐regime correlation between explanatory variables and disturbances in the Markov…

部分重叠时间序列：商品期货波动动态的新模型AI 解读

Abstract In commodity futures markets, contracts with various delivery dates trade simultaneously. Applied researchers typically discard the majority of the data and form a single time series by choosing only one price observation per day. This strategy precludes a full understanding of these markets and can induce complicated nonlinear dynamics in the data. In this paper, I introduce the partially overlapping time series (POTS) model to model jointly all traded contracts.…

估值比率与长期股票价格可预测性AI 解读

Abstract Using annual data for 1872–1997, this paper re‐examines the predictability of real stock prices based on price–dividend and price–earnings ratios. In line with the extant literature, we find significant evidence of increased long‐horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at short horizons but can be rejected at longer horizons based on bootstrapped…

股票收益建模与预测：利用期货信息、区制转换和国际溢出AI 解读

Abstract This paper proposes a vector equilibrium correction model of stock returns that exploits the information in the futures market, while allowing for both regime‐switching behaviour and international spillovers across stock market indices. Using data for three major stock market indices since 1989, we find that: (i) in sample, our model outperforms several alternative models on the basis of standard statistical criteria; (ii) in out‐of‐sample forecasting, our model…

金融波动率建模中的自动推断与学习AI 解读

This paper uses the specific-to-general methodological approach that is widely used in science, in which problems with existing theories are resolved as the need arises, to illustrate a number of important developments in the modeling of univariate and multivariate financial volatility. Some of the difficulties in analyzing time-varying univariate and multivariate conditional volatility and stochastic volatility include the number of parameters to be estimated and the…

纠正误差：利用高频数据与已实现波动率评估波动率预测AI 解读

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing…

GARCH过程中的影响诊断AI 解读

Influence diagnostics have become important tools for statistical analysis since the seminal work by Cook. In this article we present a curvature-based directional diagnostic, set up based on the slope-based diagnostic to assess the local influence of minor perturbations on influence graph in a regression model. Using both slope- and curvature-based diagnostics, we examine local influence in the generalized autoregressive conditional heteroscedasticity (GARCH) model under…

条件密度估计的交叉验证方法AI 解读

We extend the idea of crossvalidation to choose the smoothing parameters of the ‘double-kernel’ local linear regression for estimating a conditional density. Our selection rule optimises the estimated conditional density function by minimising the integrated squared error. We also discuss three other bandwidth selection rules, an ad hoc method used by Fan et al. (1996), a bootstrap method of Hall et al. (1999) for bandwidth selection in the estimation of conditional…

Cointegration is studied for a non‐linear autoregressive process characterized by discontinuous and regime‐dependent equilibrium or error correction. Here the disequilibrium, as measured by the norm of linear ‘stable’ or cointegrating relations, determines the regime and hence the equilibrium correction of the process. Importantly, switching between regimes is thereby allowed to be caused endogenously. The transition function may be either observable as in, e.g. threshold…

摘要暂缺

平滑样条非线性非参数回归模型AI 解读

Almost all of the current nonparametric regression methods, such as smoothing splines, generalized additive models, and varyingcoefficients models, assume a linear relationship when nonparametric functions are regarded as parameters. In this article we propose a general class of smoothing spline nonlinear nonparametric models that allow nonparametric functions to act nonlinearly. They arise in many fields as either theoretical or empirical models. Our new estimation methods…

受约束的间接估计AI 解读

We develop generalized indirect estimation procedures that handle equality and inequality constraints on the auxiliary model parameters by extracting information from the relevant multipliers, and compare their asymptotic efficiency to maximum likelihood. We also show that, regardless of the validity of the restrictions, the asymptotic efficiency of such estimators can never decrease by explicitly considering the multipliers associated with additional equality constraints.…

非平稳 GARCH 模型的渐近推断AI 解读

Consistency and asymptotic normality are established for the highly applied quasi-maximum likelihood estimator in the GARCH(1,1) model. Contrary to existing literature we allow the parameters to be in the region where no stationary version of the process exists. This has the important implication that the likelihood-based estimator for the GARCH parameters is consistent and asymptotically normal in the entire parameter region including both stationary and explosive behavior.…

广义可加波动模型的 LIVE 方法AI 解读

We investigate a new separable nonparametric model for time series, which includes many autoregressive conditional heteroskedastic (ARCH) models and autoregressive (AR) models already discussed in the literature. We also propose a new estimation procedure called LIVE, or local instrumental variable estimation, that is based on a localization of the classical instrumental variable method. Our method has considerable computational advantages over the competing marginal…

GARCH(p,q) 模型的序贯变点检测AI 解读

We suggest a sequential monitoring scheme to detect changes in the parameters of a GARCH(p,q) sequence. The procedure is based on quasi-likelihood scores and does not use model residuals. Unlike for linear regression models, the squared residuals of nonlinear time series models such as generalized autoregressive conditional heteroskedasticity (GARCH) do not satisfy a functional central limit theorem with a Wiener process as a limit, so its boundary crossing probabilities…

摘要暂缺

随机时间间隔采样下连续时间马尔可夫过程的估计AI 解读

We introduce a family of generalized-method-of-moments estimators of the parameters of a continuous-time Markov process observed at random time intervals. The results include strong consistency, asymptotic normality, and a characterization of standard errors. Sampling is at an arrival intensity that is allowed to depend on the underlying Markov process and on the parameter vector to be estimated. We focus on financial applications, including tick-based sampling, allowing for…

离散时间即期利率模型的样本外表现AI 解读

We provide a comprehensive analysis of the out-of-sample performance of a wide variety of spot rate models in forecasting the probability density of future interest rates. Although the most parsimonious models perform best in forecasting the conditional mean of many financial time series, we find that the spot rate models that incorporate conditional heteroscedasticity and excess kurtosis or heavy tails have better density forecasts. Generalized autoregressive conditional…