论文检索

是否存在最优的预测组合？AI 解读

We consider several geometric approaches for combining forecasts in large samples—a simple eigenvector approach, a mean corrected eigenvector and trimmed eigenvector approach. We give conditions where geometric approach yields identical result as the regression approach. We also consider a mean and scale corrected simple average of all predictive models for finite sample and give conditions where simple average is an optimal combination. Monte Carlos are conducted to compare…

基于高频数据的似然扩散模型选择：渐近分析AI 解读

We provide a new asymptotic analysis of model selection procedure that compares likelihoods of two candidate diffusion models. Our asymptotic analysis relies on two dimensional asymptotic expansions with shrinking sampling interval Δ and increasing sampling span T, and clarifies the different roles of drift and diffusion functions in the selection of diffusion models. In particular, we show that the model with superior diffusion function specification is always preferred to…

仿射期限结构模型的可检验含义AI 解读

Affine term structure models have been used to address a wide range of questions in macroeconomics and finance. This paper investigates a number of their testable implications which have not previously been explored. We show that the assumption that certain specified yields are priced without error is testable, and find that the implied measurement or specification error exhibits serial correlation in all of the possible formulations investigated here. We further find that…

用狄利克雷过程混合估计半参数非对称随机波动率模型AI 解读

We extend the asymmetric, stochastic, volatility model by modeling the return-volatility distribution nonparametrically. The novelty is modeling this distribution with an infinite mixture of Normals, where the mixture unknowns have a Dirichlet process prior. Cumulative Bayes factors show our semiparametric model accurately forecasting market returns. During tranquil markets, expected volatility rises (declines, then rises as the shock increases) when the market shock is…

马尔可夫转换与变点 GARCH 模型的边际似然AI 解读

GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood, which is essential for determining the number of regimes or change-points. We solve the problem by using particle MCMC, a…

波动率活动度：模型设定与估计AI 解读

The paper examines volatility activity and its asymmetry and undertakes further specification analysis of volatility models based on it. We develop new nonparametric statistics using high-frequency option-based VIX data to test for asymmetry in volatility jumps. We also develop methods for estimating and evaluating, using price data alone, a general encompassing model for volatility dynamics where volatility activity is unrestricted. The nonparametric application to VIX…

时间变换Lévy LIBOR市场模型：利率上限曲面与互换期权立方的联合定价估计AI 解读

We propose a novel time-changed Lévy LIBOR (London Interbank Offered Rate) market model for jointly pricing of caps and swaptions. The time changes are split into three components. The first component allows matching the volatility term structure, the second generates stochastic volatility, and the third accommodates for stochastic skew. The parsimonious model is flexible enough to accommodate the behavior of both caps and swaptions. For the joint estimation we use a…

收益率曲线中的信息：宏观金融方法AI 解读

SUMMARY We use a macro‐finance model, incorporating macroeconomic and financial factors, to study the term premium in the US bond market. Estimating the model using Bayesian techniques, we find that a single factor explains most of the variation in bond risk premiums. Furthermore, the model‐implied risk premiums account for up to 40% of the variability of one‐ and two‐year excess returns. Using the model to decompose yield spreads into an expectations and a term premium…

非参数与半参数模型的局部识别AI 解读

In parametric, nonlinear structural models, a classical sufficient condition for local identification, like Fisher (1966) and Rothenberg (1971), is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We derive an analogous result for the nonparametric, nonlinear structural models, establishing conditions under which an infinite dimensional analog of the full rank condition is sufficient for local identification.…

利用小波与阈值法估计高维波动率矩阵AI 解读

We propose a locally stationary linear model for the evolution of high-dimensional financial returns, where the time-varying volatility matrix is modelled as a piecewise-constant function of time. We introduce a new wavelet-based technique for estimating the volatility matrix, which combines four ingredients: a Haar wavelet decomposition, variance stabilization of the Haar coefficients via the Fisz transform prior to thresholding, a bias correction, and extra time-domain…

多元动态模型中分布形状参数的序贯估计AI 解读

Sequential maximum likelihood and GMM estimators of distributional parameters obtained from the standardised innovations of multivariate conditionally heteroskedastic dynamic regression models evaluated at Gaussian PML estimators preserve the consistency of mean and variance parameters while allowing for realistic distributions. We assess their efficiency, and obtain moment conditions leading to sequential estimators as efficient as their joint ML counterparts. We also…

不同持久性程度下的预测回归与稳健长跨度回归AI 解读

The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables…

马尔可夫转换多重分形交易间隔模型及其美国股票应用AI 解读

We propose and illustrate a Markov-switching multifractal duration (MSMD) model for analysis of inter-trade durations in financial markets. We establish several of its key properties with emphasis on high persistence and long memory. Empirical exploration suggests MSMD’s superiority relative to leading competitors.

完全子集回归AI 解读

This paper proposes a new method for combining forecasts based on complete subset regressions. For a given set of potential predictor variables we combine forecasts from all possible linear regression models that keep the number of predictors fixed. We explore how the choice of model complexity, as measured by the number of included predictor variables, can be used to trade off the bias and variance of the forecast errors, generating a setup akin to the efficient frontier…

利用非线性滤波进行预测密度的时变组合AI 解读

We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be…

存在结构突变的长记忆过程预测AI 解读

We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC or Mallows’ Cp) to choose the order of the approximate AR model. Our method avoids…

期权隐含通胀概率密度的经济含义AI 解读

Recently a market in options based on consumer price index inflation (inflation caps and floors) has emerged in the US. This paper uses quotes on these derivatives to construct probability densities for inflation. We study how these probability density functions respond to news announcements and find that the implied odds of deflation are sensitive to certain macroeconomic news releases. We also estimate empirical pricing kernels using these option prices along with time…

定价模型表现与两步横截面回归方法AI 解读

ABSTRACT Over the years, many asset pricing studies have employed the sample cross‐sectional regression (CSR) R 2 as a measure of model performance. We derive the asymptotic distribution of this statistic and develop associated model comparison tests, taking into account the impact of model misspecification on the variability of the CSR estimates. We encounter several examples of large R 2 differences that are not statistically significant. A version of the intertemporal…

存在水平变化与确定性趋势时的记忆参数估计AI 解读

We propose estimators of the memory parameter of a time series that are robust to a wide variety of random level shift processes, deterministic level shifts, and deterministic time trends. The estimators are simple trimmed versions of the popular log-periodogram regression estimator that employ certain sample-size-dependent and, in some cases, data-dependent trimmings that discard low-frequency components. We also show that a previously developed trimmed local Whittle…

无需正性约束的 GARCH 模型：指数 GARCH 还是对数 GARCH？AI 解读

This paper provides a probabilistic and statistical comparison of the log-GARCH and EGARCH models, which both rely on multiplicative volatility dynamics without positivity constraints. We compare the main probabilistic properties (strict stationarity, existence of moments, tails) of the EGARCH model, which are already known, with those of an asymmetric version of the log-GARCH. The quasi-maximum likelihood estimation of the log-GARCH parameters is shown to be strongly…

跳跃是否影响股票风险溢价的动态变化？AI 解读

This paper investigates whether risks associated with time-varying arrival of jumps and their effect on the dynamics of higher moments of returns are priced in the conditional mean of daily market excess returns. We find that jumps and jump dynamics are significantly related to the market equity premium. The results from our time-series approach reinforce the importance of the skewness premium found in cross-sectional studies using lower-frequency data; and offer a potential…

半参数向量乘法误差模型AI 解读

SUMMARY Financial time series are often non‐negative‐valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector multiplicative error model (vMEM; the element‐by‐element product of a vector of conditionally autoregressive scale factors and a multivariate i.i.d. innovation process) is a suitable strategy. Its parameters can be estimated by generalized method of moments, bypassing the problem of…

时变调整后知情交易概率与对称订单流冲击概率的估计AI 解读

SUMMARY Recently, Duarte and Young (2009) studied the probability of informed trading (PIN) proposed by Easley et al . (2002) and decomposed it into two parts: the adjusted PIN (APIN) as a measure of asymmetric information and the probability of symmetric order‐flow shock (PSOS) as a measure of illiquidity. They provide some cross‐section estimates of these measures using daily data over annual periods. In this paper we propose a method to estimate daily APIN and PSOS by…

包含偏度与杠杆效应的条件异方差因子模型AI 解读

SUMMARY Conditional heteroskedasticity, skewness and leverage effects are well‐known features of financial returns. The literature on factor models has often made assumptions that preclude the three effects to occur simultaneously. In this paper I propose a conditionally heteroskedastic factor model that takes into account the presence of both the conditional skewness and leverage effects. This model is specified in terms of conditional moment restrictions and unconditional…