Electricity spot price modelling with a view towards extreme spike risk

Sums of Lévy-driven Ornstein–Uhlenbeck processes are appropriate for modelling electricity spot price data. In this paper we present a new estimation method with particular emphasis on capturing the high peaks, which is one of the stylized features of such data. After introducing our method we show it at work for the EEX Phelix Base electricity price index. We also present a small simulation study to demonstrate the performance of our estimation procedure.     

A smooth non-parametric estimation framework for safety-first portfolio optimization

In this paper, we adopt a smooth non-parametric estimation to explore the safety-first portfolio optimization problem. We obtain a non-parametric estimation calculation formula for loss (truncated) probability using the kernel estimator of the portfolio returns’ cumulative distribution function, and embed it into two types of safety-first portfolio selection models. We numerically and empirically test our non-parametric method to demonstrate its accuracy and efficiency. Cross-validation results show that our non-parametric kernel estimation method outperforms the empirical distribution method. As an empirical application, we simulate optimal portfolios and display return-risk characteristics using China National Social Security Fund strategic stocks and Shanghai Stock Exchange 50 Index components.     

基于有效波动率的市场一般性风险测度研究

鲁棒跳跃的波动率估计是波动率研究的新方向。本文首先采用蒙特卡洛模拟技术检验鲁棒跳跃波动率估计量MedRV的有效性以及预测的准确性,结果表明:MedRV能够有效鲁棒跳跃行为,得到有效波动率(EV)的估计量,同时相对于双幂次变差(BV)有更好的预测准确性。然后基于MedRV估计量构造了市场一般性风险测度,并对中国证券市场一般性风险分布特征进行了研究,结果表明:基于MedRV估计量所得到的MedRV-VaR指标可以有效摒除极端市场风险因子,得到市场一般性风险测度。     

Maximum likelihood estimation of stochastic volatility models

We develop and implement a method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by proxies based on the implied volatility of a short-dated at-the-money option. The approximation results in a small loss of accuracy relative to the standard errors due to sampling noise. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine Heston model and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.     

A joint model for temperature and natural gas with an application to the US market

Natural gas spot prices and temperatures have been studied in detail in the literature as separate processes. We propose a simple joint model that, in spite of its parsimony, describes accurately many stylized facts of the two time series: in particular we show the role played by a time-delay parameter in order to take into account the impact of temperature forecast in cross-dependency. We discuss in detail a stepwise procedure in order to calibrate model parameters, describing the elementary estimation techniques involved and the statistical accuracy achieved. In the analysis, we focus on the benchmark market in the USA (Henry Hub) and the temperatures in the Northeast and Midwest regions; we observe a negative, statistically significant, gas-temperature correlation in the cold season.     

A horse race of models and estimation methods for predicting bankruptcy

We use a comprehensive set of performance metrics to analyze the improvement in the classification power and prediction accuracy of various bankruptcy prediction models after adding governance variables and/or varying the estimation method used. In a sample covering bankruptcies of U.S. public firms in the period 2000 to 2015, we find that the addition of governance variables significantly improves the performance of all bankruptcy prediction models. We also find that the additional explanatory power provided by governance measures improves the further the firm is from bankruptcy, which suggests that governance variables may provide earlier and more accurate warning of the firm's bankruptcy potential. Our findings show that the performance of any bankruptcy prediction model is significantly affected by the estimation method used. We find that regardless of the bankruptcy model, hazard analysis provides the best classification and out-of-sample forecast accuracy among the parametric methods. Furthermore, non-parametric methods such as neural networks, data envelopment analysis or classification and regression trees appear to provide comparable and sometimes superior classification accuracy to hazard analysis. Lastly, we use the dynamic panel generalized methods of moments model to address concerns raised in prior studies about the susceptibility of similar studies to endogeneity issues and find that our findings continue to hold.     

Recovering the market risk premium from higher-order moment risks

We propose a consistent approach for the estimation of the market risk premium. As a first step, we define the broadest possible set of ex ante estimators from the viewpoint of a power utility optimiser holding the market portfolio. We then employ an evaluation framework to optimise the parametrisation of the methodology. We show that this theoretical framework can still produce reasonable market risk premium estimates, even when the representative agent is not a power utility optimiser. Our results show that the inclusion of higher-order moment risk premia improves the accuracy of the method.     

Machine learning for quantitative finance: fast derivative pricing,hedging and fitting

In this paper, we show how we can deploy machine learning techniques in the context of traditional quant problems. We illustrate that for many classical problems, we can arrive at speed-ups of several orders of magnitude by deploying machine learning techniques based on Gaussian process regression. The price we have to pay for this extra speed is some loss of accuracy. However, we show that this reduced accuracy is often well within reasonable limits and hence very acceptable from a practical point of view. The concrete examples concern fitting and estimation. In the fitting context, we fit sophisticated Greek profiles and summarize implied volatility surfaces. In the estimation context, we reduce computation times for the calculation of vanilla option values under advanced models, the pricing of American options and the pricing of exotic options under models beyond the Black–Scholes setting.     

On the Efficiency of Conditional Heteroskedasticity Models

This paper discusses how conditional heteroskedasticity models can be estimated efficiently without imposing strong distributional assumptions such as normality. Using the generalized method of moments (GMM) principle, we show that for a class of models with a symmetric conditional distribution, the GMM estimates obtained from the joint estimating equations corresponding to the conditional mean and variance of the model are efficient when the instruments are chosen optimally. A simple ARCH(1) model is used to illustrate the feasibility of the proposed estimation procedure.     

An alternative method to estimate parameters in modelling the behaviour of commodity prices

Measuring the risk of investment projects involving commodities and modelling its price dynamics behaviour is usually implemented with Kalman filtering techniques. However, because the use of these techniques has high implementation requirements, recent literature has employed approximate models. This paper proposes a new and simpler spreadsheet implementation procedure which presents lower implementation requirements than the widely used Kalman filtering estimation procedure. The proposal needs to estimate fewer parameters than usual and does not directly estimate sequences but considers the relationship between the states implicitly when defining the regression matrices. This translates into a significant reduction in processing time. We apply the proposal to estimate the parameters of a 4-factor model for four commercial commodities: crude oil, heating oil, unleaded gasoline and natural gas; we then compare the accuracy with results using the Kalman filter method. Results indicate that error measurements are approximately equal for the actual model and the approximation proposed in this paper, for both the in- and out-of-sample data-sets.     

ESTIMATING THE DEPENDENCE STRUCTURE OF SHARE PRICES: A COMPARATIVE STUDY OF THE UNITED STATES AND JAPAN

In this paper, we show that estimating the correlation structure of domestic share prices via the Overall Mean method cannot be considered universally superior to estimation at the Full Historical level for all countries. Specifically, the Japanese data show that the Full Historical Model outperforms the Overall Mean Model in forecasting accuracy, while the opposite is true with the U.S. data. We derive a Composite Model that analytically explains this contrasting result. The Industry Mean Model, which allows for efficient ex ante portfolio selection via a simple algorithm, is likely to be the best forecasting model applicable to both the U.S. and Japanese stock markets.     

Market-wide illiquidity and the distribution of non-parametric stochastic discount factors

Employing out-of-sample non-parametric estimation techniques, we show that market-wide liquidity risk matters for asset pricing independently of the specific functional form of the stochastic discount factor (SDF) and, therefore, of the asset pricing model specification. Market-wide illiquidity significantly affects the distribution of the SDF. Specifically, it boosts up the volatility of the SDF, causing minor effects on higher moments of its distribution. This finding is robust to the use of different sets of test assets in the estimation of the SDF, including equity and corporate bond portfolios, and the use of a high-dimensional data estimation procedure.     

A semi-analytic method for valuing high-dimensional options on the maximum and minimum of multiple assets

Valuing high-dimensional options has many important applications in finance but when the true distributions are unknown or complex, numerical approximations must be used. Approximation methods based on Monte-Carlo simulation show a steep trade-off between estimation accuracy and computational efficiency. This article presents an alternative semi-analytic approximation method for pricing options on the maximum or minimum of multiple assets with unknown distributions. Computational efficiency is shown to improve significantly without sacrificing estimation accuracy. The method is illustrated with applications to options on underlying assets with mean-reverting prices, time-dependent correlations, and stochastic volatility The authors would like to thank the two anonymous referees, the associate editor, and Dr. Jess H. Chua at the University of Calgary for valuable comments and insights on this research. This research was partly supported by NUS grant R-146-000-059-112     

两因子随机死亡率状态空间模型及长寿风险测度

引入状态空间模型对传统两因子CBD模型拟合阶段和预测阶段进行联合建模,并基于卡尔曼滤波方法对模型参数进行估计。进一步考虑到死亡率数据的小样本特征,结合Bootstrap仿真技术和生存年金组合折现模型对长寿风险进行测度。利用1996~2011年数据展开实证研究,结果表明:结合模型解释能力、参数估计结果和误差项正态分布检验结果,两因子状态空间模型要优于传统CBD模型;年金组合规模的扩大可以消除微观长寿风险,但不能消除宏观长寿风险和参数风险;宏观长寿风险占据着不可分散风险的主导地位。     

Understanding analysts forecasts

The purpose of this paper is to model analysts’ forecasts. The paper differs from the previous research in that we do not focus on how accurate these predictions may be. Accuracy may indeed be an important quality but we argue instead that another equally important aspect of the analysts’ job is to predict and describe the impact of jump events. In effect, the analysts’ role is one of scenario prediction. Using a Bayesian-inspired generalised method of moments estimation procedure, we use this notion of scenario prediction combined with the structure of the Morgan Stanley analysts’ forecasting database to model normal (base), optimistic (bull) and pessimistic (bear) forecast scenarios for a set of reports from Asia (excluding Japan) for 2007–2008. Since the estimation procedure is unique to this paper, a rigorous derivation of the asymptotic properties of the resulting estimator is also provided.     

The moments of the Gompertz distribution and maximum likelihood estimation of its parameters

The Gompertz distribution is widely used to describe the distribution of adult deaths. Previous works concentrated on formulating approximate relationships to characterise it. However, using the generalised integro-exponential function, exact formulas can be derived for its moment-generating function and central moments. Based on the exact central moments, higher accuracy approximations can be defined for them. In demographic or actuarial applications, maximum likelihood estimation is often used to determine the parameters of the Gompertz distribution. By solving the maximum likelihood estimates analytically, the dimension of the optimisation problem can be reduced to one both in the case of discrete and continuous data. Monte Carlo experiments show that by ML estimation, higher accuracy estimates can be acquired than by the method of moments.     

Estimation of slowly decreasing Hawkes kernels: application to high-frequency order book dynamics

We present a modified version of the non parametric Hawkes kernel estimation procedure studied in Bacry and Muzy [arXiv:1401.0903, 2014] that is adapted to slowly decreasing kernels. We show on numerical simulations involving a reasonable number of events that this method allows us to estimate faithfully a power-law decreasing kernel over at least six decades. We then propose a eight-dimensional Hawkes model for all events associated with the first level of some asset order book. Applying our estimation procedure to this model, allows us to uncover the main properties of the coupled dynamics of trade, limit and cancel orders in relationship with the mid-price variations.     

Spectral calibration of exponential Lévy models

We investigate the problem of calibrating an exponential Lévy model based on market prices of vanilla options. We show that this inverse problem is in general severely ill-posed and we derive exact minimax rates of convergence. The estimation procedure we propose is based on the explicit inversion of the option price formula in the spectral domain and a cut-off scheme for high frequencies as regularisation.     

Outsourcing and firm performance nexus: An analysis using the conventional and panel double-bootstrap procedure

Industrial performance is an essential element of economic progress. In this study, we examine the impact of outsourcing on industrial performance using the firm-level data of 191 textile companies in India over the period 2000–2015. First, we follow the conventional non-parametric two-stage procedure and analyse the nexus between outsourcing and firm performance under a single-objective setting. We then test the influence of outsourcing on the performance of multiple-objective firms using reverse directional distance function scores. To address the bias in efficiency estimation and the serial correlation issue in the second-stage regression, we use truncated regression and the double-bootstrap procedure for panel data analysis. Our results show an improvement in industrial performance over the study period. Our analysis following the conventional two-stage procedure shows that the outsourcing of manufacturing activities and professional jobs contributes to industrial performance. The relation between outsourcing and firm performance essentially remains the same in a more reliable analysis using a panel double bootstrap procedure.     

The brain gain of CFOs in China: The case of analyst forecasts

We study the impact of CFOs with foreign experience on analysts' forecast accuracy in emerging markets. Using a unique data set from China, we find that analysts' forecast accuracy increases when firms hire CFOs with foreign experience, confirming the brain gain effect of CFOs. Our results are robust after addressing potential endogeneity by introducing the propensity score matched (PSM) procedure and Heckman two-stage method. Channel analyses show that CFOs with foreign experience are related to decreased earnings management and a greater probability of hiring high-quality auditors, indicating that the improvement in financial reporting quality and information environment brought by returnee CFOs mainly drive our results. Further cross-section tests reveal that compared to firms with more external pressure, the positive effect of returnee CFOs on analysts' forecast accuracy is more pronounced among firms with fewer analyst coverage and belonging to less competitive industries. Returnee CFOs with foreign work experience exert a more significant impact on analysts' forecast accuracy than those with foreign study experience. Overall, we provide the first evidence on the brain gain of CFOs in terms of analyst forecasts.