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时变跳跃尾部AI 解读

We develop new methods for the estimation of time-varying risk-neutral jump tails in asset returns. In contrast to existing procedures based on tightly parameterized models, our approach imposes much fewer structural assumptions, relying on extreme-value theory approximations together with short-maturity options. The new estimation approach explicitly allows the parameters characterizing the shape of the right and the left tails to differ, and importantly for the tail shape…

VIX、方差溢价与股票市场波动率AI 解读

We decompose the squared VIX index, derived from US S&P500 options prices, into the conditional variance of stock returns and the equity variance premium. We evaluate a plethora of state-of-the-art volatility forecasting models to produce an accurate measure of the conditional variance. We then examine the predictive power of the VIX and its two components for stock market returns, economic activity and financial instability. The variance premium predicts stock returns while…

经济约束下的股票收益预测AI 解读

We propose a new approach to imposing economic constraints on time series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates time-varying volatility in the predictive…

This paper shows that jumps in financial asset prices are often erroneously identified and are, in fact, rare events accounting for a very small proportion of the total price variation. We apply new econometric techniques to a comprehensive set of ultra high-frequency equity and foreign exchange tick data recorded at millisecond precision, allowing us to examine the price evolution at the individual order level. We show that in both theory and practice, traditional measures…

投资者情绪对异象收益的预测是否源于伪回归？AI 解读

Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment׳s observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu, and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor…

固定效应动态面板模型的非参数估计AI 解读

This paper considers nonparametric estimation of autoregressive panel data models with fixed effects. A within-group type series estimator is developed and its convergence rate and asymptotic normality are derived. It is found that the series estimator is asymptotically biased and the bias could reduce the mean-square convergence rate compared with the cross-section cases. A bias corrected nonparametric estimator is developed.

退化 ARCH 模型参数推断的一族对比准则AI 解读

In this paper, we introduce a family of contrasts for parametric inference in ARCH models the volatility of which exhibits some degeneracy. We focus specifically on ARCH processes with a linear volatility (called LARCH processes), for which the Gaussian quasi-likelihood estimator may be inconsistent. Our approach generalizes that of Beran and Schützner (2009) and gives an interesting alternative to the WLSE used by Francq and Zakoïan (2010) for an autoregressive process with…

检测气候极端事件变化的非参数熵方法AI 解读

Summary The paper focuses primarily on temperature extremes measured at 24 European stations with at least 90 years of data. Here, the term extremes refers to rare excesses of daily maxima and minima. As mean temperatures in this region have been warming over the last century, it is automatic that this positive shift can be detected also in extremes. After removing this warming trend, we focus on the question of determining whether other changes are still detectable in such…

ARCH过程的多变点检测AI 解读

Summary The emergence of the recent financial crisis, during which markets frequently underwent changes in their statistical structure over a short period of time, illustrates the importance of non-stationary modelling in financial time series. Motivated by this observation, we propose a fast, well performing and theoretically tractable method for detecting multiple change points in the structure of an auto-regressive conditional heteroscedastic model for financial returns…

利用商品价格指数预测通货膨胀AI 解读

This paper shows that for five small commodity-exporting countries that have adopted inflation targeting monetary policies, world commodity price aggregates have predictive power for their CPI and PPI inflation, particularly once possible structural breaks are taken into account. This conclusion is robust to using either disaggregated or aggregated commodity price indexes (although the former perform better), the currency denomination of the commodity prices, and to using…

马尔可夫转换结构VAR：结合传统与统计冲击识别AI 解读

In structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. Unfortunately, these shocks may not have a meaningful structural economic interpretation. It is discussed how statistical and conventional identifying information can be combined. The discussion is based on a VAR model for the US containing oil prices, output, consumer prices and a…

意见分歧与资产价格AI 解读

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns,…

使用未滤波数据的扩展新凯恩斯菲利普斯曲线：美国通胀的后验预测证据AI 解读

SUMMARY Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low‐frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time‐varying patterns…

缺失冲击的DSGE模型政策分析工具AI 解读

SUMMARY In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors that feed into the structural shocks of the DSGE model to create correlated disturbances. We estimate the factor structure by either matching impulse responses of the augmented DSGE model to those generated by an auxiliary model or by…

销售、库存与实际利率：一个世纪的典型事实AI 解读

SUMMARY We use Bayesian time‐varying parameter structural vector autoregressions with stochastic volatility to investigate changes in reduced‐form and structural correlations between inventories and either sales growth or the real interest rate in the USA during both the inter‐war and post‐World War II periods. We identify four structural shocks by combining a single long‐run restriction to identify a permanent output shock with three sign restrictions to identify demand‐…

SUMMARY The likelihood of the parameters in structural macroeconomic models typically has non‐identification regions over which it is constant. When sufficiently diffuse priors are used, the posterior piles up in such non‐identification regions. Use of informative priors can lead to the opposite, so both can generate spurious inference. We propose priors/posteriors on the structural parameters that are implied by priors/posteriors on the parameters of an embedding…

SUMMARY We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using two empirical illustrations consisting of the Smets and Wouters model and a larger news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely used random…

财政极限估计：希腊案例AI 解读

SUMMARY This paper uses Bayesian methods to estimate a real business cycle model that allows for interactions among fiscal policy instruments, the stochastic ‘fiscal limit’ and sovereign default. Using the particle filter to perform likelihood‐based inference, we estimate the full nonlinear model with post‐EMU data until 2010:Q4. We find that (i) the probability of default on Greek debt was in the range of 5–10% in 2010:Q4 and (ii) the 2011 surge in the Greek real interest…

罕见冲击与大衰退AI 解读

SUMMARY We estimate a DSGE (dynamic stochastic general equilibrium) model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student's t ‐distribution. Results from the Smets and Wouters ( American Economic Review 2007; 97 : 586–606) model estimated on the usual set of macroeconomic time series over the 1964–2011 period indicate that (i) the Student's t specification is strongly favored by the data even when we allow for…

SUMMARY The mismatch between the timescale of DSGE (dynamic stochastic general equilibrium) models and the data used in their estimation translates into identification problems, estimation bias, and distortions in policy analysis. We propose an estimation strategy based on mixed‐frequency data to alleviate these shortcomings. The virtues of our approach are explored for two monetary policy models. Copyright © 2014 John Wiley & Sons, Ltd.

家庭是否通过住房所有权自我保险？美国城市间的证据AI 解读

Are households more likely to be homeowners when “housing risk” is higher? We show that home-ownership rates and loan-to-value (LTV) ratios at the city level are strongly negatively correlated with local house price volatility. However, causal inference is confounded by house price levels, which are systematically correlated with housing risk in an intuitive way: in cities where the land value is larger relative to the local cost of structures, house prices are higher and…

劳动收入过程的持久—暂时成分表示AI 解读

We consider the decomposition of shocks to a dynamic process into a persistent and a transitory component. Without additional assumptions (such as zero correlation) the decomposition of shocks into a persistent and transitory component is indeterminate. The assumption that is conventional in the earnings literature is that there is no correlation. The Beveridge–Nelson decomposition that is widely used in time series analysis assumes a perfect correlation. Without…

不规则空间观测数据的概率密度非参数估计AI 解读

Nonparametric estimation of probability density functions, both marginal and joint densities, is a very useful tool in statistics. The kernel method is popular and applicable to dependent data, including time series and spatial data. But at least for the joint density, one has had to assume that data are observed at regular time intervals or on a regular grid in space. Though this is not very restrictive in the time series case, it often is in the spatial case. In fact, to a…

全天候高频波动率的贝叶斯建模与预测AI 解读

This article estimates models of high-frequency index futures returns using “around-the-clock” 5-min returns that incorporate the following key features: multiple persistent stochastic volatility factors, jumps in prices and volatilities, seasonal components capturing time of the day patterns, correlations between return and volatility shocks, and announcement effects. We develop an integrated MCMC approach to estimate interday and intraday parameters and states using…