Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach

This paper suggests a term structure model which parsimoniously exploits a broad macroeconomic information set. The model uses the short rate and the common components of a large number of macroeconomic variables as factors. Precisely, the dynamics of the short rate are modeled with a Factor-Augmented Vector Autoregression and the term structure is derived using parameter restrictions implied by no-arbitrage. The model has economic appeal and provides better out-of-sample yield forecasts at intermediate and long horizons than a number of previously suggested approaches. The forecast improvement is highly significant and particularly pronounced for short and medium-term maturities.     

Estimating Long-term Trends in Tropospheric Ozone Levels

This paper develops Bayesian methodology for estimating long-term trends in the daily maxima of tropospheric ozone. The methods are then applied to study long-term trends in ozone at six monitoring sites in the state of Texas. The methodology controls for the effects of meteorological variables because it is known that variables such as temperature, wind speed and humidity substantially affect the formation of tropospheric ozone. A semiparametric regression model is estimated in which a nonparametric trivariate surface is used to model the relationship between ozone and these meteorological variables because, while it is known that the relatinship is a complex nonlinear one, its functional form is unknown. The model also allows for the effects of wind direction and seasonality. The errors are modeled as an autoregression, which is methodologically challenging because the observations are unequally spaced over time. Each function in the model is represented as a linear combination of basis functions located at all of the design points. We also estimate an appropriate data transformation simulataneously with the functions. The functions are estimated nonparametrically by a Bayesian hierarchical model that uses indicator variables to allow a non-zero probability that the coefficient of each basis term is zero. The entire model, including the nonparametric surfaces, data transformation and autoregression for the unequally spaced errors, is estimated using a Markov chain Monte Carlo sampling scheme with a computationally efficient transition kernel for generating the indicator variables. The empirical results indicate that key meteorological variables explain most of the variation in daily ozone maxima through a nonlinear interaction and that their effects are consistent across the six sites. However, the estimated trends vary considerably from site to site, even within the same city.     

Nonlinear dynamical systems and inventory management

Surprisingly, deterministic time series can generate highly irregular, random-appearing trajectories. These deterministic time series result from nonlinear dynamical systems of differential or difference equations. The random appearance displayed by these systems is called nonlinear dynamical complexity. Properties of nonlinear complex systems include aperiodic random appearance, sensitive dependence on initial conditions and model parameters, and nonstationarity. Experiments involving the operation of simulated business environments and theoretical nonlinear dynamical models for inventory are reviewed to explore motivating factors that can give rise to demand with nonlinear complexities. The experimental and theoretical evidence reviewed indicates that nonlinear complexities in demand have significant implications for inventory management. Thus, researchers and practitioners in inventory management need to consider these properties when choosing inventory management methods. Characteristics of nonlinear dynamical systems and their implications for inventory management are presented in this paper. The use of the Brock, Dechert, and Scheinkman (1987) (BDS) test for nonlinear dependence is demonstrated on actual demand data.     

A No‐Arbitrage Structural Vector Autoregressive Model of the UK Yield Curve*

This article combines a Structural Vector Autoregression with a no‐arbitrage approach to build a multifactor Affine Term Structure Model (ATSM). The resulting No‐Arbitrage Structural Vector Autoregressive (NASVAR) model implies that expected excess returns are driven by structural macroeconomic shocks. This is in contrast with a standard ATSM, in which agents are concerned with non‐structural risks. As a simple application, we study the effects of supply, demand and monetary policy shocks on the UK yield curve. We show that all structural shocks affect the slope of the yield curve, with demand and supply shocks accounting for a large part of the time variation in bond yields.     

Short-term inflation projections: A Bayesian vector autoregressive approach

In this paper we construct a large Bayesian Vector Autoregressive model (BVAR) for the Euro area that captures the complex dynamic inter-relationships between the main components of the Harmonized Index of Consumer Prices (HICP) and their determinants. The model generates accurate conditional and unconditional forecasts in real-time. We find a significant pass-through effect of oil-price shocks on core inflation and a strong Phillips curve during the Great Recession.     

The exchange rate system and macroeconomic fluctuations in Sub-Saharan Africa

In this paper, we investigate the sources of macroeconomic fluctuations in Sub-Saharan African (SSA) countries with particular attention to the exchange rate system. We use a structural Vector Autoregression (VAR) model with limited capital mobility and long run restrictions to identify the shocks. Supply and terms of trade shocks tend to dominate output movements in the CFA and non-CFA countries alike. However, terms of trade shocks tend to influence the CFA zone to a greater extent and there seems to be a higher influence of demand shocks on output and the real exchange rates in the non-CFA countries.     

R&D and aggregate fluctuations

Empirical observations raise interesting questions regarding the sources of the excessive volatility in the R&D sector as well as the nature of the relation between the sector and aggregate fluctuations. Using US data for the period 1959–2007, we identify sectoral technology and capital investment-specific shocks by employing a Vector Autoregression. The identifying assumptions are motivated by a two-sector dynamic general equilibrium model. Controlling for real and nominal factors, we find that capital investment-specific shocks explain 70 percent of fluctuations of R&D investment, while R&D technology shocks explain 30 percent of the variation of aggregate output, net of R&D investment. Technology shocks jointly explain almost all the variation of output in the R&D sector and 78 percent of the variation of output in the rest of the economy. They also constitute the main factor of the procyclicality of R&D investment.     

Comparisons of Bayesian estimation procedures for two-way contingency tables without interaction

Bayesian and empirical Bayesian estimation methods are reviewed and proposed for the row and column parameters in two-way Contingency tables without interaction. Rasch's multiplicative Poisson model for misreadings is discussed in an example. The case is treated where assumptions of exchangeability are reasonable a priori for the unknown parameters. Two different types of prior distributions are compared, It appears that gamma priors yield more tractable results than lognormal priors.     

The transmission of foreign shocks to South Eastern European economies: A Bayesian VAR approach

The paper analyses the macroeconomic effects of foreign shocks in three South-East European (SEE) economies: Croatia, Macedonia and Bulgaria. In this regard, we investigate the transmission of several eurozone shocks (output gap, money market rates and inflation) on various macroeconomic variables in the aforementioned countries (output, inflation, money market rates and budget deficits). We trace the effects of foreign shocks on the basis of impulse response functions obtained from the Bayesian Vector Auto Regressions (VARs) separately for each country. The main findings from our study are: first, economic expansion in the eurozone has strong output and inflation effects on SEE economies, implying some degree of synchronization of business cycles; second, eurozone inflation is instantly and to a great extent transmitted to domestic inflation, suggesting that inflation in the SEE economies is mostly driven by foreign inflation; third, domestic money market rates are not closely linked with eurozone money markets; fourth, monetary policy in the SEE countries does not seem to be responsive to eurozone inflation shocks; and fifth, the fiscal authorities attempt to offset the spillover effects from both economic expansion and monetary tightening in the eurozone.     

CAPM as a strategic planning tool

In this paper, the use of the capital asset pricing model (CAPM) as a strategic planning tool is reviewed. The links between the CAPM and other approaches to strategic planning are noted and discussed. We conclude that the CAPM complements other types of analysis very effectively and offers management an extremely powerful analytical tool for strategic planning.     

Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of “green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019.     

A UNIFYING VIEW ON MULTI‐STEP FORECASTING USING AN AUTOREGRESSION

Abstract This paper unifies two methodologies for multi‐step forecasting from autoregressive time series models. The first is covered in most of the traditional time series literature and it uses short‐horizon forecasts to compute longer‐horizon forecasts, while the estimation method minimizes one‐step‐ahead forecast errors. The second methodology considers direct multi‐step estimation and forecasting. In this paper, we show that both approaches are special (boundary) cases of a technique called partial least squares (PLS) when this technique is applied to an autoregression. We outline this methodology and show how it unifies the other two. We also illustrate the practical relevance of the resultant PLS autoregression for 17 quarterly, seasonally adjusted, industrial production series. Our main findings are that both boundary models can be improved by including factors indicated from the PLS technique.     

Large time‐varying parameter VARs: A nonparametric approach

In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time‐varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs. When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and compares well with large (parametric) Bayesian VARs with time‐varying parameters. The tool can also be used for structural analysis. As an example, we study the time‐varying effects of oil price shocks on sectoral U.S. industrial output. According to our results, the increased role of global demand in shaping oil price fluctuations largely explains the diminished recessionary effects of global energy price increases.     

Nonstationary nonlinear heteroskedasticity

In this paper, we consider time series with the conditional heteroskedasticities that are given by nonlinear functions of integrated processes. Such time series are said to have nonlinear nonstationary heteroskedasticity (NNH), and the functions generating conditional heterogeneity are called heterogeneity generating functions (HGF's). Various statistical properties of time series with NNH are investigated for a wide class of HGF's. For NNH models with a variety of HGF's, volatility clustering and leptokurtosis, which are common features of ARCH type models, are manifest. In particular, it is shown that the sample autocorrelations of their squared processes vanish only very slowly, or do not even vanish at all, in the limit. Volatility clustering is therefore well expected. The NNH models with certain types of HGF's indeed have sample characteristics that are very similar to those of ARCH type models. Moreover, the sample kurtosis of the NNH model either diverges or has a stable limiting distribution with support truncated on the left by the kurtosis of the innovations. This would well explain the presence of leptokurtosis in many observed time series data. To illustrate the empirical relevancy of our model, we analyze the spreads between the forward and spot rates of USD/DM exchange rates. It is found that the conditional variances of the spreads can be well modelled as a nonlinear function of the levels of the spot rates.     

基于流动性调整CAViaR模型的风险度量方法

本文提出了流动性风险度量的一个新的方法,流动性调整的CAViaR模型。该模型能够直接反映资产流动性的变动对未来风险的影响,并在此基础上计算资产未来经过流动性调整的风险VaR,从而使投资者能够更好地管理风险,尤其是流动性风险。实证研究表明,该模型能够较好地刻画中国股市流动性风险的动态变化特征;并且发现股票流动性的大幅下降通常导致未来风险明显加大,且正向流动性下降所带来的风险往往较负向流动性要更大,因此更值得投资者关注。     

存在自相关时自回归模型的估计和检验

讨论存在自相关情况下自回归模型中随机解释变量的内生性,指出目前的计量经济理论所存在的问题,证明了随机误差存在自相关情况下一阶自回归模型和高阶自回归模型的随机解释变量与随机误差都不相关,同时改进了自回归模型的估计和检验方法。     

The nonlinear effect of oil price shocks on financial stress: Evidence from China

This paper examines the nonlinear effects of different types of oil price shocks on China’s financial stress index (FSI). For this purpose, we use newly proposed framework by Ready (2018) to decompose oil prices into supply, demand and risk shocks. Then, we use a Markov regime-switching (MRS) model to investigate the nonlinear effects of these oil price shocks on China’s FSI. The empirical results show that the effects of three oil price shocks are nonlinear under different regimes. In particular, oil supply shocks mainly have a significantly positive effect on China’s FSI in the low-volatility state; demand shocks have negative effects on China’s FSI in different regimes, but this effect is larger in the low-volatility state; the effect of risk shocks on China’s FSI is the opposite, and it is positive in the high-volatility state but negative in the low-volatility state.     

纯分时定价与分时阶梯定价对政策目标实现的对比分析

基于双层需求模型,对分时阶梯定价与纯分时定价下的居民电力需求行为进行比较。利用相对需求方程检验两类定价下的相对需求是否具有非位似偏好性质,运用绝对需求方程分析居民在峰谷时的不同弹性特征。研究结论表明,分时阶梯与纯分时定价下存在不同的收入和电费位似偏好性质;居民的价格与需求弹性显著不同;两类定价下可以实现的政策目标也存在明显差异。本文为系统比较非线性定价与线性定价下的需求奠定了基础。     

International spillovers in global asset markets

The paper empirically estimates the financial transmission within and across bond and equity markets in the four largest global financial markets – the United States, the Euro area, Japan, and the United Kingdom. We argue that international bond and equity markets are highly interconnected both within and across asset classes in a globalized world, where the complex transmission process across various financial assets is not restricted to just the domestic market. This paper employs identification through generalized forecast error variance decompositions to estimate spillovers across four systemic markets in a Vector Autoregression (VAR) framework. We find that asset prices react most strongly to international shocks within the same asset class, but that there are also substantial international spillovers across asset classes. Rolling estimations analysis provides evidence that global asset markets have become more integrated and that the bilateral relationships change over time. Our results are robust to specifications that take into account the monetary policy stance and include foreign exchange markets.     

杜芬振子检测模型非线性研究

杜芬振子可被用于微弱信号检测,杜芬振子检测模型决定了杜芬振子的非线性,进而决定了杜芬振子的检测性能,基于非线性系统分析方法对杜芬振子检测模型的结构和特性系统地进行了研究,得出了非线性项的最简形式。