扩展型泰勒规则与人民币汇率变动：理论机理与实证检验

本文将汇率预期、国际资本流动与风险溢价纳入到扩展的开放型泰勒规则汇率模型之中,从理论上阐释了人民币汇率动态变化的成因,并结合非线性自回归分布滞后模型（NARDL）展开实证分析。研究发现,扩展型泰勒规则对人民币汇率的影响具有显著的非对称性,且在长期与短期内相异。具体而言,中美息差、产出缺口差、物价差及汇率预期（特别是在贬值预期的环境下）能够对人民币即期汇率产生较强的“叠加”影响。在扩展的泰勒规则汇率模型中,国际资本流动影响作用较大,其次是汇率预期,投资者的逆向选择使得风险溢价波动对人民币汇率的影响具有非对称性特征。因此,货币当局应建立可预期的货币政策框架以及有效监管资本流动和降低风险溢价,避免公众形成对人民币汇率的贬值预期并以此维持人民币汇率在合意、均衡的区间内波动。     

Comment: Uses of Administrative Records: A Social Security Point of View

Recent evidence indicates that using multiple forward rates sharply predicts future excess returns on U.S. Treasury Bonds, with the R²'s being around 30%. The projection coefficients in these regressions exhibit a distinct pattern that relates to the maturity of the forward rate. These dimensions of the data, in conjunction with the transition dynamics of bond yields, offer a serious challenge to term structure models. In this article we show that a regime-shifting term structure model can empirically account for these challenging data features. Alternative models, such as affine specification, fail to account for these important features. We find that regimes in the model are intimately related to bond risk premia and real business cycles.     

Restrictions on Risk Prices in Dynamic Term Structure Models

Restrictions on the risk-pricing in dynamic term structure models (DTSMs) tighten the link between cross-sectional and time-series variation of interest rates, and make absence of arbitrage useful for inference about expectations. This article presents a new econometric framework for estimation of affine Gaussian DTSMs under restrictions on risk prices, which addresses the issues of a large model space and of model uncertainty using a Bayesian approach. A simulation study demonstrates the good performance of the proposed method. Data for U.S. Treasury yields calls for tight restrictions on risk pricing: only level risk is priced, and only changes in the slope affect term premia. Incorporating the restrictions changes the model-implied short-rate expectations and term premia. Interest rate persistence is higher than in a maximally flexible model, hence expectations of future short rates are more variable—restrictions on risk prices help resolve the puzzle of implausibly stable short-rate expectations in this literature. Consistent with survey evidence and conventional macro wisdom, restricted models attribute a large share of the secular decline in long-term interest rates to expectations of future nominal short rates. Supplementary materials for this article are available online.     

On the Role of Risk Premia in Volatility Forecasting

I explain why at-the-money implied volatility is a biased and inefficient forecast of future realized volatility using the insights from the empirical option-pricing literature. First, I explain how the risk premia, which manifest themselves through disparity between objective and risk-neutral probability measures, lead to the disparity between realized and implied volatilities. Second, I show that this disparity is a function of the latent spot volatility, which I estimate using the historical volatility and high–low range. An empirical exercise that is based on at-the-money implied volatility series of foreign currencies and stock market indexes, is supportive of my risk premia-based explanation of the bias.     

Estimating the Parameters of Stochastic Volatility Models Using Option Price Data

This article describes a maximum likelihood method for estimating the parameters of the standard square-root stochastic volatility model and a variant of the model that includes jumps in equity prices. The model is fitted to data on the S&P 500 Index and the prices of vanilla options written on the index, for the period 1990 to 2011. The method is able to estimate both the parameters of the physical measure (associated with the index) and the parameters of the risk-neutral measure (associated with the options), including the volatility and jump risk premia. The estimation is implemented using a particle filter whose efficacy is demonstrated under simulation. The computational load of this estimation method, which previously has been prohibitive, is managed by the effective use of parallel computing using graphics processing units (GPUs). The empirical results indicate that the parameters of the models are reliably estimated and consistent with values reported in previous work. In particular, both the volatility risk premium and the jump risk premium are found to be significant.     

Correcting Estimation Bias in Dynamic Term Structure Models

The affine dynamic term structure model (DTSM) is the canonical empirical finance representation of the yield curve. However, the possibility that DTSM estimates may be distorted by small-sample bias has been largely ignored. We show that conventional estimates of DTSM coefficients are indeed severely biased, and this bias results in misleading estimates of expected future short-term interest rates and of long-maturity term premia. We provide a variety of bias-corrected estimates of affine DTSMs, for both maximally flexible and overidentified specifications. Our estimates imply interest rate expectations and term premia that are more plausible from a macrofinance perspective. This article has supplementary material online.     

Risk-minimizing pricing and hedging foreign currency options under regime-switching jump-diffusion models

This article mainly investigates risk-minimizing European currency option pricing and hedging strategy when the spot foreign exchange rate is driven by a Markov-modulated jump-diffusion model. We suppose the domestic and foreign money market floating interest rates, the drift, and the volatility of the exchange rate dynamics all depend on the state of the economy, which is modeled by a continuous-time hidden Markov chain. The model considered in this article will provide market practitioners with flexibility in characterizing the dynamics of the spot foreign exchange rate. Using the minimal martingale measure, we obtain a system of coupled partial-differential-integral equations satisfied by the currency option price and find the corresponding hedging strategies and the residual risk. According to simulation of currency option prices in the special case of double exponential jump-diffusion regime-switching model, we further discuss and show the effects of the parameters on the prices.     

Macroeconomic Factors Strike Back: A Bayesian Change-Point Model of Time-Varying Risk Exposures and Premia in the U.S. Cross-Section

This article proposes a Bayesian estimation framework for a typical multi-factor model with time-varying risk exposures to macroeconomic risk factors and corresponding premia to price U.S. publicly traded assets. The model assumes that risk exposures and idiosyncratic volatility follow a break-point latent process, allowing for changes at any point on time but not restricting them to change at all points. The empirical application to 40 years of U.S. data and 23 portfolios shows that the approach yields sensible results compared to previous two-step methods based on naive recursive estimation schemes, as well as a set of alternative model restrictions. A variance decomposition test shows that although most of the predictable variation comes from the market risk premium, a number of additional macroeconomic risks, including real output and inflation shocks, are significantly priced in the cross-section. A Bayes factor analysis massively favors the proposed change-point model. Supplementary materials for this article are available online.     

Bootstrap-based ARMA order selection

Modelling the underlying stochastic process is one of the main goals in the study of many dynamic phenomena, such as signal processing, system identification and time series. The issue is often addressed within the framework of ARMA (Autoregressive Moving Average) paradigm, so that the related task of identification of the ‘true’ order is crucial. As it is well known, the effectiveness of such an approach may be seriously compromised by misspecification errors since they may affect model capabilities in capturing dynamic structures of the process. As a result, inference and empirical outcomes may be heavily misleading. Despite the big number of available approaches aimed at determining the order of an ARMA model, the issue is still open. In this paper, we bring the problem in the framework of bootstrap theory in conjunction with the information-based criterion of Akaike (AIC), and a new method for ARMA model selection will be presented. A theoretical justification for the proposed approach as well as an evaluation of its small sample performances, via simulation study, are given.     

INDEX TO VOLUME 4 (1986)

This article provides an empirical investigation of the risk-neutral variance process and the market price of variance risk implied in the foreign-currency options market. There are three principal contributions. First, the parameters of Heston's mean-reverting square-root stochastic volatility model are estimated using dollar/mark option prices from 1987 to 1992. Second, it is shown that these implied parameters can be combined with historical moments of the dollar/mark exchange rate to deduce an estimate of the market price of variance risk. These estimates are found to be nonzero, time varying, and of sufficient magnitude to imply that the compensation for variance risk is a significant component of the risk premia in the currency market. Finally, the out-of-sample test suggests that the historical variance and the Hull and White implied variance contain no more information than that imbedded in the Heston implied variance.     

Estimating linear representations of nonlinear processes

This paper considers the problem of estimating an autoregressive-moving average (ARMA) model when only ergodic and mixing assumptions can be made. The estimation procedure is based on the minimization of a sum of squared deviations about linear conditional expectations. It is shown that the estimator is strongly consistent and asymptotically normal. The results can be used to estimate weak linear representations of some nonlinear processes. Several examples of such linear representations are provided. Other potential areas of applications are inference for noncausal ARMA, aggregation and marginalization of linear processes. A numerical study is also presented. It appears that standard identification routines based on strong hypothesis on the innovation of ARMA models can be seriously misleading when these assumptions do not hold.     

Risk premiums in asset prices and returns

We review recent research on time-varying risk premiums, including attempts to explain rejections by baillie and others of the unbiasedness hypothesis. Using spot and forward foreign exchange rates we discuss the evidence for time-varying risk premiums, relate it to general equilibrium theories of asset pricing, and describe the artificial economy methodology.     

ARCH and Bilinear Time Series Models: Comparison and Combination

Two extensions to the ARMA model, bilinearity and ARCH errors are compared, and their combination is considered. Starting with the ARMA model, tests for each extension are discussed, along with various least squares and maximum likelihood estimates of the parameters and tests of the estimated models based on these. The effects each may have on the identification, estimation, and testing of the other are given, and it is seen that to distinguish between the two properly, it is necessary to combine them into a bilinear model with ARCH errors. Some consequences of the misspecification caused by considering only the ARMA model are noted, and the methods are applied to two real time series.     

Risk premiums in asset prices and returns

We review recent research on time-varying risk premiums, including attempts to explain rejections by baillie and others of the unbiasedness hypothesis. Using spot and forward foreign exchange rates we discuss the evidence for time-varying risk premiums, relate it to general equilibrium theories of asset pricing, and describe the artificial economy methodology.     

基于无限次重复博弈模型的人民币升值节奏问题研究

从游资与预期的角度,利用无限次重复博弈模型研究人民币汇率升值幅度的纳什均衡问题。研究结论包括：纳什均衡汇率与游资不撤离的损失补偿正相关,与中国维持GDP高增长的概率正相关,与贴现系数负相关;在游资大量流进的条件下,人民币汇率升值幅度不仅要包括游资持有者所获得的稳定外汇收益以及损失补偿,还要求汇率调整出现＂超调＂;游资流入对经济的推动扣除风险损失后的剩余,不能小于游资持有者的机会成本加上一定比例升水。     

中美利差对人民币汇率变动的影响分析

以利差对汇率传导机制和路径的理论分析为基础，通过建立汇率行为路径分析模型，检验有关名义利差与名义汇率关系的理论和经验方面的问题。中美实证研究结果表明，名义利差变动对名义汇率变动的影响支持利率平价理论，但利差对汇率变动的传导路径较利率平价理论更为复杂，且传导方式和效应大小在2005年7月汇改前后有显著差别。     

Effects of a single outlier on arma identification

Fox (1972), Box and Tiao (1975), and Abraham and Box (1979) have proposed methods for detecting outliers in time series whose ARMA form is known (or identified). We show that the existence of a single aberrant observation, innovation, or intervention causes an ARMA model to be misidentified using unadjusted autocorrelation (acf) and partial autocorrelation estimates. The magnitude, location, type of outlier, and in some cases the ARMA's parameters, affect the identification outcome. We use variance inflation, signal-to-noise ratios, and acf critical values to determine an ARMA model's susceptibility to misidentifi-cation. Numerical and simulation examples suggest how to iteratively use the outlier detection methods in practice.     

基于Boosting-ARMA的碳价预测

自回归滑动平均(ARMA)模型是最流行的预测模型之一,而模型选择却是使用ARMA进行预测的难点,尤其是当真实模型的阶数较高时,因此提出Boosting-ARMA预测算法,利用Boosting算法进行最优子集ARMA寻找,自动且高效地完成ARMA模型的识别。模拟实验显示,Boosting-ARMA优于其他方法,用新算法预测碳价实证分析发现,Boosting-ARMA算法可以获得较高的碳价预测准确性并且方便快捷。     

On the pade tablé and its relationship to the r and sarrays and arm a modeling

The Box-Jenkins method is a popular and important technique for modeling and forecasting of time series. Unfortunately the problem of determining the appropriate ARMA forecasting model (or indeed if an ARMA model holds) is a major drawback to the use of the Box-Jenkins methodology. Gray et al. (1978) and Woodward and Gray (1979) have proposed methods of estimating p and qin ARMA modeling based on the R and Sarrays that circumvent some of these modeling difficulties.

In this paper we generalize the R and S arrays by showing a relationship to Padé approximunts and then show that these arrays have a much wider application than in just determining model order. Particular non-ARMA models can be identified as well. This includes certain processes that consist of deterministic functions plus ARMA noise, indeed we believe that the combined R and S arrays are the best overall tool so fur developed for the identification of general 2nd order (not just stationary) time scries models.     

The finite-time ruin probability in two non-standard renewal risk models with constant interest rate and dependent subexponential claims

This paper considers an ordinary renewal risk model and a compound renewal risk model with constant interest rate, subexponential claims and a general premium process. We derive some asymptotic results on the finite-time ruin probabilities.