Risk preferences estimation of exporting firms under exchange rate uncertainty

This note empirically analyses how exchange rate fluctuations affects firms’ optimal production and exporting decisions. A firm’s elasticity of risk aversion determines the direction of the impact of exchange rate risk on exports. Based on a flexible utility function that incorporates all possible risk preferences, a unique structurally estimable equation is derived. Quantile regression method is used to estimate this equation and compute the risk aversion elasticities for a panel of Indian firms. This approach allows us to demonstrate how characteristics of exporters at the intensive margin varies with the level of elasticities across the conditional exchange rate distribution.     

经济增长风险的冲击传导和经济周期波动的“溢出效应”

在非确定性的经济环境当中 ,我们利用经济增长率的绝对离差、条件标准差和在险增长水平等三种方法度量了经济增长风险和条件波动性 ,然后利用冲击反应函数度量了经济增长水平对于经济增长风险的动态反应 ,并检验了增长水平与波动性之间的影响关系。检验结果表明 ,经济风险性和波动性与经济增长水平之间存在显著正相关关系 ,由此可以推断经济周期波动性对于经济增长水平存在“溢出效应” ,较高的经济波动性带来了经济增长水平的“风险奖励” ;同时 ,从经济风险的传导过程中可以判断 ,非确定性因素和突发事件尚未对我国经济增长的趋势水平形成显著干预 ,我国经济增长过程抵御外部冲击的能力已经得到显著提高。     

Liquidity-adjusted conditional capital asset pricing model

This paper derives a liquidity-adjusted conditional two-moment capital asset pricing model (CAPM) and a liquidity-adjusted conditional three-moment CAPM respectively based on theory of stochastic discount factor. The liquidity-adjusted conditional two-moment CAPM shows that a security's conditional expected excess return consists of three parts: its conditional expected liquidity cost, the systemic risk premium and the liquidity risk premium. The liquidity-adjusted conditional three-moment CAPM shows that a security's conditional expected excess return depends on its conditional expected liquidity cost, the conditional covariance between its return and the market return, the conditional covariance between its liquidity cost and the market liquidity cost, and the conditional coskewness of its return and the market return.     

The effects of inflation uncertainty on interest rates: a nonlinear approach

In this article, we investigate the effects of inflation variability on short-term interest rates within a nonlinear smooth transition regression framework. The test results suggest that only the conditional mean of the inflation is a nonlinear process whereas the conditional variance is time variant but linear. Using the square root of conditional variance as a proxy for inflation risk, we estimate Fisher equation augmented with inflation risk. Although the estimated Fisher equations suggest that inflation risk reduces short-term interest rates, we find that the effects of inflation risk on interest rates are regime-dependent. Particularly, we find that the negative effects of inflation variability on nominal rates are greater in low-inflationary regimes when compared to high-inflationary regimes. On the other hand, it is found that both inflation and inflation uncertainty raise the expected inflation effect.     

A two-regime threshold model with conditional skewed Student t distributions for stock returns

This paper proposes a two-regime threshold model for the conditional distribution of stock returns in which returns follow a distinct skewed Student t distribution within each regime: the model allows capturing time variation in the conditional distribution of returns, as well as higher order moments. An application of the model to daily U.S. stock returns illustrates the advantages of the proposed model in comparison to alternative specifications: the model performs well in terms of in-sample fit; it more accurately estimates the conditional volatility; and it produces useful risk assessment as measured by the term structure of value at risk.     

Volatility spill-overs in commodity spot prices: New empirical results

The classical rational expectations model of commodity markets implies that expected spot price risk is an explanatory variable in spot price regressions; and also that inventory carryover, which is reduced by a larger price variance, creates autoregressive conditional heteroscedastic processes in spot prices. In order to falsify/verify this theory, it has typically been assumed that the square root of the conditional variance of spot prices, a proxy for spot price risk, enters the conditional mean function of spot prices. Based on this simple representation, a typical but counter intuitive outcome has been that spot price risk has an insignificant impact on spot prices, see, e.g., Beck (Beck, S., 1993. A Rational Expectations Model of Time Varying Risk Premia in Commodities Futures Markets: Theory and Evidence. International Economic Review 34, 149–168, Beck, S., 2001. Autoregressive Conditional Heteroskedasticity in Commodity Spot Prices. Journal of Applied Econometrics 16, 115–132). In this paper, we propose an alternative functional relationship (from GARCH(1,1) to GARCH(1,1)-AR(m)) between spot price risk and spot prices that is fully supported by the classical rational expectations model, and based on this new representation we are able to provide stronger empirical support for Muth's rational expectation theory.     

Pricing and hedging options with GARCH-stable proxy volatilities

This article considers modelling nonnormality in return with stable Paretian (SP) innovations in generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH) and Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH) volatility dynamics. The forecasted volatilities from these dynamics have been used as a proxy to the volatility parameter of the Black–Scholes (BS) model. The performance of these proxy-BS models has been compared with the performance of the BS model of constant volatility. Using a cross section of S&P500 options data, we find that EGARCH volatility forecast with SP innovations is an excellent proxy to BS constant volatility in terms of pricing. We find improved performance of hedging for an illustrative option portfolio. We also find better performance of spectral risk measure (SRM) than value-at-risk (VaR) and expected shortfall (ES) in estimating option portfolio risk in case of the proxy-BS models under SP innovations.

Abbreviation: generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH) and Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH)     

The Impact of Inflation Uncertainty on Interest Rates in the UK

This paper assesses the effect of expected inflation and inflation risk on interest rates within the Fisher hypothesis framework. Autoregressive Conditional Heteroscedastic models are used to estimate the conditional variability of inflation as a proxy for risk. With the UK quarterly data from 1958:4 to 1994:4, we found that both the expected inflation and the conditional variability of inflation positively affect the UK three‐month Treasury‐bill rate.     

Equity market information and credit risk signaling: A quantile cointegrating regression approach

Investigating linkages between credit and equity markets, we consider daily aggregate U.S. CDS spreads as well as well-chosen equity market and implied volatility indexes over ten years. We describe such robust (to spurious correlation) relationship with the quantile cointegrating regression approach. Such approach handles extreme quantiles/CDS values and their behavior with respect to the equity market's influence. Heteroskedastic patterns such as time-varying variance, but also autocorrelation, skewness and leptokurtosis are captured. Thus, the sensitivity of aggregate CDS spreads to equity market price and volatility channels is accurately measured across quantiles and spreads. Such quantile-dependent sensitivity exhibits asymmetric responses to equity market shocks. A sub-period analysis investigates potential regime shifts in estimated quantile cointegrating regressions. Quantile cointegrating coefficients vary over time and quantiles, and exhibit different magnitudes across sub-periods and spreads. Therefore, the relationship is unstable over time. We also propose a scenario analysis and risk signaling application for credit risk management prospects. Under specific risk levels, credit risky situations are described conditional on the equity market's information over time, and related expected aggregate CDS spreads are computed. Estimated conditional quantiles/CDS spreads act as credit alert triggers.     

Valuing the Incremental Benefits of Groundwater Protection when Exposure Levels are Known

Both economic theory and psychological research indicate that benefit functions for reductions in health risk exposures may be conditional on current exposures. Using nitrates found in household wells, it is demonstrated that perceptions of health risks across exposure levels are affected by the individual's current exposure level, thus providing support for a conditional benefits function approach. Functions of conditional incremental benefits are estimated from a contingent valuation study of households that had been informed of their water test results. Incremental benefits reach a peak at an intermediate level of nitrates and then decline. Possible explanations for this non-convexity are provided.     

Revisiting growth empirics based on IV panel quantile regression

We analyse the well-known issue of economic growth convergence using quantile regression. Most previous studies have used a least squares (LS) method or variation, which focuses on the issue only at the mean of the growth rate. Therefore, such results cannot provide a satisfactory answer to what can happen if the growth rate is far from the conditional mean level. For example, we consider the following question: do we still have economic growth convergence or is the convergence speed changed in a low growth period such as the ‘Great Recession,’ that started in 2008? We propose using instrumental variable panel quantile regression to answer this question. Our empirical findings demonstrate that economic growth convergence occurs at all quantiles over the entire conditional distribution, but that the convergence speed does depend on quantiles; the convergence speed is much higher when the GDP growth rate is at either high or low quantiles.     

Estimating loss functions of experts

We propose a new and simple methodology to estimate the loss function associated with experts’ forecasts. Under the assumption of conditional normality of the data and the forecast distribution, the asymmetry parameter of the lin–lin and linex loss function can easily be estimated using a linear regression. This regression also provides an estimate for potential systematic bias in the forecasts of the experts. The residuals of the regression are the input for a test for the validity of the normality assumption. We apply our approach to a large data set of SKU-level sales forecasts made by experts, and we compare the outcomes with those for statistical model-based forecasts of the same sales data. We find substantial evidence for asymmetry in the loss functions of the experts, with underprediction penalized more than overprediction.     

Portfolio style: Return-based attribution using quantile regression

Return-based classification identifies a portfolio's style signature in the time series of its returns. Detection is based on a regression of portfolio returns on returns of factor mimicking indices. The method is easy to apply and does not require information about portfolio composition. Classification using least squares means that style is determined by the way factor exposure influences expected returns. We introduce regression quantiles as a complement to the standard analysis. The regression quantiles extract additional information from the time series of returns by identifying the way style affects returns at places other than the expected value. This allows discrimination among portfolios that would be otherwise judged equivalent based on conditional expectations. It also provides direct information about the impact of style on the tails of the conditional return distribution. Simple examples are presented to illustrate regression quantile classification.     

Score-driven Markov-switching EGARCH models: an application to systematic risk analysis

We introduce new Markov-switching (MS) dynamic conditional score (DCS) exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models, to be used by practitioners for forecasting value-at-risk (VaR) and expected shortfall (ES) in systematic risk analysis. We use daily log-return data from the Standard & Poor’s 500 (S&P 500) index for the period 1950–2016. The analysis of the S&P 500 is useful, for example, for investors of (i) well-diversified US equity portfolios; (ii) S&P 500 futures and options traded at Chicago Mercantile Exchange Globex; (iii) exchange traded funds (ETFs) related to the S&P 500. The new MS DCS-EGARCH models are alternatives to of the recent MS Beta-t-EGARCH model that uses the symmetric Student’s t distribution for the error term. For the new models, we use more flexible asymmetric probability distributions for the error term: Skew-Gen-t (skewed generalized t), EGB2 (exponential generalized beta of the second kind) and NIG (normal-inverse Gaussian) distributions. For all MS DCS-EGARCH models, we identify high- and low-volatility periods for the S&P 500. We find that the statistical performance of the new MS DCS-EGARCH models is superior to that of the MS Beta-t-EGARCH model. As a practical application, we perform systematic risk analysis by forecasting VaR and ES.

Abbreviation Single regime (SR); Markov-switching (MS); dynamic conditional score (DCS); exponential generalized autoregressive conditional heteroscedasticity (EGARCH); value-at-risk (VaR); expected shortfall (ES); Standard & Poor's 500 (S&P 500); exchange traded funds (ETFs); Skew-Gen-t (skewed generalized t); EGB2 (exponential generalized beta of the second kind); NIG (normal-inverse Gaussian); log-likelihood (LL); standard deviation (SD); partial autocorrelation function (PACF); likelihood-ratio (LR); ordinary least squares (OLS); heteroscedasticity and autocorrelation consistent (HAC); Akaike information criterion (AIC); Bayesian information criterion (BIC); Hannan-Quinn criterion (HQC).     

Integrated Risk Management with a Filtered Bootstrap Approach

We present a multi-period risk model to measure portfolio risk that integrates market risk, credit risk and, in a simplified way, liquidity risk. Thus, it overcomes the major limitation currently shared by many risk models that are unable to give a complete picture of all portfolio risks according to a single, coherent framework. The model is based on the Filtered Bootstrap approach; hence, it captures conditional heteroskedasticity, serial correlation and non-normality in the risk factors, that is, most of the features of observed financial time series. Being a simulation risk model, it copes in a natural way with derivatives as it allows the full valuation of the probability density function of the contracts. In addition, it is a suitable and flexible way to generate future scenarios on medium‐term horizons, so this model is particularly appropriate for asset management companies.     

Does past performance affect mutual fund tracking error in Taiwan?

This study examines the relationship between fund past performance and manager choice of portfolio risk in Taiwan. Employing the exponential generalized autoregressive conditional heteroscedasticity and linear regression models, the results demonstrate that historically poor average performance does not increase mutual fund tracking error (TE) or portfolio risk. Additionally, yearly tournament behaviour, namely mid-year losers increasing their last-half year TEs, only appears in funds with higher management fees. This implies that managers of high management fee funds actively increase TE in response to poor historical performance, to enable them to beat the market during future months or the second half of the year.     

我国区域产业结构优化及其影响因素分析——基于分位数回归方法

测算了2000—2008年我国30个省(自治区、直辖市)的产业结构优化指标值,构建了分位数回归模型,对2000—2008年我国区域产业结构优化程度及其影响因素进行了实证分析。结果表明:金融发展、实物投资、对外贸易、技术创新对区域产业结构优化具有一定的促进作用,但在条件分布的不同位置,这种促进作用存在明显差异;人口流动对区域产业结构优化具有负面影响,在低分位数处该影响最大,在高分位数处该影响相对较小;汇率制度的变迁导致各因素对产业结构优化的影响发生明显改变。     

Under Performers and Over Achievers: A Quantile Regression Analysis of Growth

Numerous papers have searched for empirical linkages between long run economic growth and a myriad of economic, socio-political and environmental factors. Most of these studies use ordinary least-squares regression or panel regression analysis on a sample of countries and therefore consider the behaviour of growth around the mean of the conditional distribution. We extend the literature by using quantile regression to analyse long-term growth at a variety of points in the conditional distribution. By using this approach, we identify the determinants of growth for under performing countries relative to those for over achieving countries.     

Estimation of an endogenous switching regression model with discrete dependent variables: Monte-Carlo analysis and empirical application of three estimators

The performances of alternative two-stage estimators for the endogenous switching regression model with discrete dependent variables are compared, with regard to their usefulness as starting values for maximum likelihood estimation. This is especially important in the presence of large correlation coefficients, in which case maximum likelihood procedures have difficulties to converge. Monte-Carlo simulations indicate that an estimator that corrects for conditional heteroskedasticity of the residuals is superior in almost all instances, and especially when maximum likelihood is problematic. This result is also obtained in an empirical example in which off-farm work participation equations of farm women are conditional on farm work participation status. First version received: July 1995/final version received: March 1998     

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non–linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)–in–mean models are employed. The conditional errors are assumed to follow the normal and Student– t distributions. The non–linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non–linearity. Under the Student density, the extent of non–linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH–in–mean regression generated the worse out–of–sample forecasts.