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.     

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.     

Risk shocks and housing supply: A quantitative analysis

This paper analyzes the role of stochastic uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production and provide estimates of the time-series properties of risk shocks by using firm level productivity data. The analysis demonstrates that risk shocks to the housing production sector are a quantitatively important impulse mechanism for understanding housing price movements. Specifically, the model can match the volatility of housing prices observed in the data. It is also demonstrated that adjustment costs are important in replicating the contemporaneous correlation of housing prices with GDP and residential investment. Critically, bankruptcy costs act as an endogenous markup factor in housing prices and are an important determinant of house price volatility. However, in comparison to housing demand shocks, risk shocks have low explanatory power for real quantities.     

Distortions,efficiency and the size distribution of firms

We develop a model of firms’ growth in which the tax and credit environments act as selection mechanisms. Such a model, parametrized and validated using a variety of data restrictions, can rationalize observations about input choices and size patterns typical of many developing countries. Using counterfactual experiments, we show that firms’ optimal responses to the tax environment are effective in reducing efficiency losses. As a consequence, tax distortions only account for 13% of the gap in output per worker between an undistorted economy and the benchmark. Credit constraints account for 44% of this gap. However, the interaction between the cost of capital and credit constraints appears to be the most important source of misallocation and can explain up to 85% of the difference in output per worker between the benchmark and first-best.     

Formal education and public knowledge

In this paper, I examine the transitional dynamics of an economy populated by individuals who split their time between acquiring a formal education, producing final goods, and innovating.The paper has two objectives: (i) uncovering the macroeconomic circumstances that favored the rise of formal education; (ii) to reconcile the remarkable growth of the education sector with the constancy of other key macroeconomic variables, such as the interest rate, the consumption-output ratio, and the growth rate of per capita income (Kaldor facts).The transitional dynamics of human capital growth models, such as Lucas (1998), would attribute the arrival of education to the diminishing marginal productivity of physical capital. Conversely, the model proposed here suggests that it is the rate of learning that catches up with the rate of return on physical capital. As technical knowledge expands, the rate of return on education increases, inducing individuals to stay longer in school. The model's transitional paths are matched with long run U.S. educational and economic data.     

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.     

Combining Data from Surveys and Administrative Record Systems. The Norwegian Experience

At Statistics Norway administrative data have been extensively used in order to improve the quality of survey data. Various techniques have been used to reduce sampling variance and/or to reduce the effects of non-response. In the present article some of the most commonly used methods are being presented, and based on empirical rather than theoretical evaluations, we give our conclusions concerning their potentials and limitations.     

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.