Change-point detection for expected shortfall in time series

Expected shortfall (ES) is a popular risk measure and plays an important role in risk and portfolio management. Recently, change-point detection of risk measures has been attracting much attention in finance. Based on the self-normalized CUSUM statistic in Fan, Glynn and Pelger (2018) and the Wild Binary Segmentation (WBS) algorithm in Fryzlewicz (2014), this paper proposes a variant WBS procedure to detect and estimate change points of ES in time series. The strengthened Schwarz information criterion is also introduced to determine the number of change points. Monte Carlo simulation studies are conducted to assess the finite-sample performance of our variant WBS procedure about ES in time series. An empirical application is given to illustrate the usefulness of our procedure.     

Unbiased estimation of expected return using CAPM

Estimation of expected return is required for many financial decisions. For example, an estimate for cost of capital is required for capital budgeting and cost of equity estimates are needed for performance evaluation based on measures such as EVA. Estimates for expected return are often based on the Capital Asset Pricing Model (CAPM), which states that expected excess return (expected return minus the risk-free rate) is equal to the asset's sensitivity to the world market portfolio (β) times the risk premium on the “world market portfolio” (the market risk premium). Since the world market portfolio, by definition, contains all assets in the world, it is not observable. As a result, an estimate for expected return is commonly obtained by taking an estimate for β based on some index (as a proxy for the world market portfolio) and an estimate for the market risk premium based on a potentially different index and multiplying them together. In this paper, it is shown that this results in a biased estimate for expected return. This is undesirable since biased estimates lead to misallocation of funds and biased performance measures. It is also shown in this paper that the straightforward procedure suggested by Fama and MacBeth [J. Financ. Econ. 1 (1974) 43] results in an unbiased estimate for expected return. Further from the analysis done, it follows that, for an unbiased estimate, it does not matter what proxy is used, as long as it is used correctly an unbiased estimate for expected return results.     

Are Fundamentals Priced in the Bond Market?*

To date, the discussion of the Lev and Thiagarajan 1993 fundamentals in the prior literature has been exclusively in the context of the stock market. Our study is the first to examine the value‐relevance of these fundamentals for default risk. By focusing on the market for new bond issues, we examine the value‐relevance of the fundamental score using expected rather than realized returns. Also, by focusing on the bond market we provide a different perspective than that brought by prior studies relying solely on stock prices. We find the fundamentals to be priced in the market for new bond issues as indicators of expected future earnings and to be value‐relevant in enabling the market to discern differences in bond credit quality over and above the published bond ratings.     

When to accept a sequence of gambles

When is a sequence of gambles, which is initially rejected eventually accepted? The eventual acceptance is defined as a pair property between the utility function and the sequences of gambles. A sequence of gambles is accepted when the gambles follow a large deviation principle and the utility function is non-satiated and bounded from below in a certain way. The number of gambles required for acceptance is computed.     

Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite-dimensional parameter spaces.     

货币政策地区效应——基于地区总供给曲线的理论与实证分析

本文着重检验了我国区域经济中的基于产出缺口的地区短期总供给曲线的存在性,进而分析地区短期总供给曲线对货币政策地区效应的影响。文章首先对地区短期总供给曲线存在的可能性进行了理论解释,应用卡尔曼滤波方法估算了我国各地区的产出缺口,然后运用ARMA模型说明预期通货膨胀率形成机制,最后以通货膨胀率与其预期之差、外部需求冲击等作为解释变量对产出缺口进行回归,结果发现,我国东部地区的短期总供给曲线较中西部地区的短期总供给曲线更平坦,因而东部地区货币政策效应要强于中西部地区。     

A Bayesian approach for more reliable tail risk forecasts

This paper demonstrates that existing quantile regression models used for jointly forecasting Value-at-Risk (VaR) and expected shortfall (ES) are sensitive to initial conditions. Given the importance of these measures in financial systems, this sensitivity is a critical issue. A new Bayesian quantile regression approach is proposed for estimating joint VaR and ES models. By treating the initial values as unknown parameters, sensitivity issues can be dealt with. Furthermore, new additive-type models are developed for the ES component that are more robust to initial conditions. A novel approach using the open-faced sandwich (OFS) method is proposed which improves uncertainty quantification in risk forecasts. Simulation and empirical results highlight the improvements in risk forecasts ensuing from the proposed methods.     

The impact of opacity on bank valuation during the global financial crisis: A channel analysis

Prior literature suggests that opacity in the banking industry is mainly caused by a lack of informativeness in the assessment of the quality of bank assets. Examining a sample of bank holding companies in the United States, we find that there is a negative relationship between opacity and bank valuation during the 2007–2009 global financial crisis. We further attempt to identify two potential channels through which opacity negatively affects bank valuation during the financial crisis: a cash flow channel and an expected return channel. We show that one channel flows from bank profitability, measured by return on equity and return on assets, confirming a cash flow channel, whereas an expected return channel, proxied by the implied cost of capital, only works for small banks. Overall, this study sheds light on the relationship between in-transparency and bank value discount during a global recession.     

REAL OPTIONS AND THE CROSS‐SECTION OF EXPECTED STOCK RETURNS

This paper surveys the theoretical literature investigating the effect of firms’ investment flexibility on the cross‐section of expected stock returns. Real options analysis derives firms’ value‐maximizing investment policies as functions of exogenous fundamental drivers of profitability and calculates firms’ market values as functions of the same variables. These functions yield the relationship between expected stock returns and firm fundamentals. Several plausible explanations for the value premium – the high average stock returns earned by firms with high book‐to‐market ratios – emerge from this literature.     

The expected real yield and inflation components of the nominal yield curve

The term structure of real yields and expected inflation are two unobserved components of the nominal yield curve. The primary objectives of this study are to decompose nominal yields into their expected real yield and inflation components and to examine their behaviour using state-space and regime-switching frameworks. The dynamic yield-curve models capture three well-known latent factors – level, slope, and curvature – that accurately aggregate the information for the nominal yields and the expected real and inflation components for all maturities. The nominal yield curve is found to increase slightly with a slope of about 120 basis points, while the real yield curve slopes upward by about 20 basis points, and the expected inflation curve is virtually flat at slightly above 2 per cent. The regime-switching estimations reveal that the nominal yield, real yield and expected inflation curves have shifted down significantly since 1999.