重工业与经济发展:计划经济时代再考察

改革开放初期,学术界和政策界对重工业优先发展战略几乎彻底否定。本文的出发点是,由于投入要素的迂回生产提高轻工业的效率,重工业的发展具有正的外部性,因此必要的补贴有利于整个经济的发展。在一个动态一般均衡模型的基础上,本文研究了重工业优先发展的最优补贴期限和补贴率。本文对中国的数据进行了校准,然后做了两个政策实验,一是中国实行平衡发展战略,二是中国实行适当重工业优先发展的赶超战略,并将其和实际结果进行比较。我们发现,实际结果下居民的效用贴现和低于平衡战略下的效用贴现和;但是,如果我们实行最优赶超战略,那么,实行补贴的时间短于实际发生的情形,而且补贴率低于实际水平,同时,居民的效用贴现和比平衡战略下的还要高。     

中国国际储备适度规模测度1994～2009——基于效用最大化分析框架的数值模拟

截至2010年9月,中国以美元计价的储备规模已高达2.64万亿美元。储备高速增长增强了中国国际清偿力和抵御国际金融风险的能力,但也会增加国内通货膨胀压力,弱化货币政策的独立性和有效性,成为影响内外均衡的重要问题。本文基于效用最大化分析框架和平滑代表性经济人消费方法,采用1994~2009年度数据分析中国储备规模的变化和适度规模的确定。我们认为,中国储备适度规模/GDP比率的理想变动区间是25%~30%,从2004年开始,实际储备规模超出适度规模平均为10%左右,基本处在合意变动区间内。但从近期储备存量走势来看,适度储备规模缺口存在加速增大的趋势。在储备管理方面,中央银行在运用国际储备进行资金运营和投资时要注意加强对潜在金融风险的监管。     

Riding on the smiles

Using a data set of vanilla options on the major indexes we investigate the calibration properties of several multi-factor stochastic volatility models by adopting the fast Fourier transform as the pricing methodology. We study the impact of the penalizing function on the calibration performance and how it affects the calibrated parameters. We consider single-asset as well as multiple-asset models, with particular emphasis on the single-asset Wishart Multidimensional Stochastic Volatility model and the Wishart Affine Stochastic Correlation model, which provides a natural framework for pricing basket options while keeping the stylized smile–skew effects on single-name vanillas. For all models we give some option price approximations that are very useful for speeding up the pricing process. In addition, these approximations allow us to compare different models by conveniently aggregating the parameters, and they highlight the ability of the Wishart-based models to control separately the smile and the skew effects. This is extremely important from a risk-management perspective of a book of derivatives that includes exotic as well as basket options.     

Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions

Verifying probabilistic forecasts for extreme events is a highly active research area because popular media and public opinions are naturally focused on extreme events, and biased conclusions are readily made. In this context, classical verification methods tailored for extreme events, such as thresholded and weighted scoring rules, have undesirable properties that cannot be mitigated, and the well-known continuous ranked probability score (CRPS) is no exception.In this paper, we define a formal framework for assessing the behavior of forecast evaluation procedures with respect to extreme events, which we use to demonstrate that assessment based on the expectation of a proper score is not suitable for extremes. Alternatively, we propose studying the properties of the CRPS as a random variable by using extreme value theory to address extreme event verification. An index is introduced to compare calibrated forecasts, which summarizes the ability of probabilistic forecasts for predicting extremes. The strengths and limitations of this method are discussed using both theoretical arguments and simulations.     

A strategic predictive distribution for tests of probabilistic calibration

Forecasts of probability distributions are needed to support decision making in many applications. The accuracy of predictive distributions should be evaluated by maximising sharpness subject to calibration. Sharpness relates to the concentration of the predictive distributions, while calibration concerns their statistical consistency with the data. This paper focuses on calibration testing. It is important that a calibration test cannot be gamed by forecasts that have been strategically designed to pass the test. The widely used tests of probabilistic calibration for predictive distributions are based on the probability integral transform. Drawing on previous results for quantile prediction, we show that strategic distributional forecasting is a concern for these tests. To address this, we provide a simple extension of one of the tests. We illustrate ideas using simulated data.     

Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness

This study assesses systemic risk inherent in credit default swap (CDS) indices using empirical and statistical analyses. We define systemic risk in two perspectives: the possibilities of simultaneous and contagious defaults, and then quantify them separately across benchmark models. To do so, we employ a Marshall-Olkin copula model to measure simultaneous default risk, and an interacting intensity-based model to capture contagious default risk. For an empirical test, we collect daily data for the iTraxx Europe CDS index and its tranche prices in the period from 2005 to 2014, and calibrate model parameters varying across time. In addition, we select forecasting models that have minimal prediction errors for the calibrated time series. Finally, we identify significant changes in each dynamic of systemic risk indicator before and after default and downgrade-related episodes that have occurred in the global financial crisis and European sovereign debt crisis.     

The Japanese saving rate between 1960 and 2000: productivity,policy changes,and demographics

In this paper, we use an overlapping generations model to study the factors generating the saving rate in Japan between 1960–2000. The model economy allows for observed aging of the population, total factor productivity (TFP), and fiscal policy to affect the national saving rate. Our calibrated general equilibrium setup generates saving rates that are reasonably similar to the data during this period. Our counterfactual experiments indicate that observed TFP growth rates are the main reason for both the secular decline and the two humps in the saving rate during 1960–2000.      

Kernel-based calibration diagnostics for recession and inflation probability forecasts

A probabilistic forecast is the estimated probability with which a future event will occur. One interesting feature of such forecasts is their calibration, or the match between the predicted probabilities and the actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. We show here that we can do this without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating the predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on the calibration error in a number of economic applications, including the prediction of recessions and inflation, using both forecasts made and stored in real time and pseudo-forecasts made using the data vintage available at the forecast date. The outcomes are evaluated using both first-release outcome measures and subsequent revised data. We find substantial evidence of incorrect calibration in professional forecasts of recessions and inflation from the SPF, as well as in real-time inflation forecasts from a variety of output gap models.     

Inflation breakeven in the Jarrow and Yildirim model and resulting pricing formulas

The Jarrow and Yildirim model for pricing inflation-indexed derivatives is still the main reference technique adopted in the inflation market. Despite its popularity it has some shortcomings, the most immediate of which is the difficulty of calibrating to market prices of options due to the large number of parameters involved. Since the market trades options on the inflation rate or index, we reformulate their model in terms of the notion of breakeven inflation. The first main advantage is the possibility of describing the prices of the most popular inflation derivatives as functions of just three parameters: breakeven volatility, the volatility of the CPI price index and the correlation between them. Secondly, the resulting Black–Scholes-implied volatilities are very straightforward to implement and the geometric interpretation of the model makes it intuitive to calibrate. Lastly, the model permits us to reproduce a realistic picture of the current state of the art of the derivatives market and, in particular, due to its simplicity, it is able to estimate the risk premium priced by the inflation market.     

The deterministic shift extension and the affine dynamic Nelson–Siegel model

The affine dynamic Nelson–Siegel model links the affine class of models with the Nelson–Siegel interpolation scheme of the yield curve. Its parameters are interpreted as the latent factors of the spot rate process driven by an affine diffusion. Using an appropriate specification of this diffusion, the yields become in form of the Nelson–Siegel model but an adjustment term is introduced. In this paper, the model is extended using a deterministic shift extension so as to perfectly fit the term structure and reduce the correction term. This enhancement allows to simulate the yield curve and the spot rate process consistently with the market data used for the calibration of the model. A numerical example discusses the calibration results of the original model and the proposed extension.