A new copula for modeling portfolios with skewed,leptokurtic and high-order dependent risk factors

The paper proposes a new copula for modeling higher-order dependencies between pairs of portfolio assets, employing orthogonal polynomials to model symmetric co-kurtoses. Skewness and leptokurtosis of portfolio margins are modeled either with the Gram–Charlier expansion of the Normal distribution or Gram–Charlier-like expansions of leptokurtic laws. Details on the estimation method of this copula are provided, and a simulation study is carried out to assess its potential range of applicability with respect to widely employed alternatives in the copula literature. Empirical evidence of the suitability of this approach to model financial data and compute risk measures is provided.     

Option Pricing under the Double Exponential Jump-Diffusion Model with Stochastic Volatility and Interest Rate

This paper proposes an efficient option pricing model that incorporates stochastic interest rate (SIR), stochastic volatility (SV), and double exponential jump into the jump-diffusion settings. The model comprehensively considers the leptokurtosis and heteroscedasticity of the underlying asset’s returns, rare events, and an SIR. Using the model, we deduce the pricing characteristic function and pricing formula of a European option. Then, we develop the Markov chain Monte Carlo method with latent variable to solve the problem of parameter estimation under the double exponential jump-diffusion model with SIR and SV. For verification purposes, we conduct time efficiency analysis, goodness of fit analysis, and jump/drift term analysis of the proposed model. In addition, we compare the pricing accuracy of the proposed model with those of the Black–Scholes and the Kou (2002) models. The empirical results show that the proposed option pricing model has high time efficiency, and the goodness of fit and pricing accuracy are significantly higher than those of the other two models.     

Term structure of interest rates in the Singapore Asian dollar market

This paper investigates empirically the term structure of interest rates in the Singapore Asian Dollar Market. We consider extended versions of the ARCH-M model of Engle, Lilien, and Robins (1987). The extended models permit autocorrelation, skewness and leptokurtosis in the residuals. The robustness of the empirical tests with respect to alternative specifications of the ARCH process is examined. It turns out that there is significant time-varying term premium, and this conclusion is independent of the hypothesized ARCH model.     

Measuring financial risk and portfolio reversion with time changed tempered stable Lévy processes

Given that underlying assets in financial markets exhibit stylized facts such as leptokurtosis, asymmetry, clustering properties and heteroskedasticity effect, this paper applies the stochastic volatility models driven by tempered stable Lévy processes to construct time changed tempered stable Lévy processes (TSSV) for financial risk measurement and portfolio reversion. The TSSV model framework permits infinite activity jump behaviors of returns dynamics and time varying volatility consistently observed in financial markets by introducing time changing volatility into tempered stable processes which specially refer to normal tempered stable (NTS) distribution as well as classical tempered stable (CTS) distribution, capturing leptokurtosis, fat tailedness and asymmetry features of returns in addition to volatility clustering effect in stochastic volatility. Through employing the analytical characteristic function and fast Fourier transform (FFT) technique, the closed form formulas for probability density function (PDF) of returns, value at risk (VaR) and conditional value at risk (CVaR) can be derived. Finally, in order to forecast extreme events and volatile market, we perform empirical researches on Hangseng index to measure risks and construct portfolio based on risk adjusted reward risk stock selection criteria employing TSSV models, with the stochastic volatility normal tempered stable (NTSSV) model producing superior performances relative to others.     

Kurtosis of GARCH and stochastic volatility models with non-normal innovations

Both volatility clustering and conditional non-normality can induce the leptokurtosis typically observed in financial data. In this paper, the exact representation of kurtosis is derived for both GARCH and stochastic volatility models when innovations may be conditionally non-normal. We find that, for both models, the volatility clustering and non-normality contribute interactively and symmetrically to the overall kurtosis of the series.     

Modelling the conditional volatility of commodity index futures as a regime switching process

Commodity index futures offer a versatile tool for gaining different forms of exposure to commodity markets. Volatility is a critical input in many of these applications. This paper examines issues in modelling the conditional variance of futures returns based on the Goldman Sachs Commodity Index (GSCI). Given that commodity markets tend to be ‘choppy’ (Webb, 1987 ), a general econometric model is proposed that allows for abrupt changes or regime shifts in volatility, transition probabilities which vary explicitly with observable fundamentals such as the basis, GARCH dynamics, seasonal variations and conditional leptokurtosis. The model is applied to daily futures returns on the GSCI over 1992–1997. The results show clear evidence of regime shifts in conditional mean and volatility. Once regime shifts are accounted for, GARCH effects are minimal. Consistent with the theory of storage, returns are more likely to switch to the high‐variance state when the basis is negative than when the basis is positive. The regime switching model also performs well in forecasting the daily volatility compared to standard GARCH models without regime switches. The model should be of interest to sophisticated traders who base their trading strategies on short‐term volatility movements, managed commodity funds interested in hedging an underlying diversified portfolio of commodities and investors of options and other derivatives tied to GSCI futures contracts. Copyright © 2001 John Wiley & Sons, Ltd.     

Global and regional integration of the Middle East and North African (MENA) stock markets

We investigate financial integration of MENA region to facilitate a more in-depth exploration of the structure of interdependence and transmission mechanism of stock returns and volatility between MENA and world stock markets. The EGARCH-M models with a generalized error distribution are employed to consider both leverage effect of negative shocks and leptokurtosis prevalent in the MENA stock markets. The estimation results of multivariate AR-GARCH models indicate that there are large and predominantly positive volatility spillovers and volatility persistence in conditional volatility between MENA and world stock markets. Own-volatility spillovers are generally higher than cross-volatility spillovers for all the markets.     

Nonstationary nonlinear heteroskedasticity

In this paper, we consider time series with the conditional heteroskedasticities that are given by nonlinear functions of integrated processes. Such time series are said to have nonlinear nonstationary heteroskedasticity (NNH), and the functions generating conditional heterogeneity are called heterogeneity generating functions (HGF's). Various statistical properties of time series with NNH are investigated for a wide class of HGF's. For NNH models with a variety of HGF's, volatility clustering and leptokurtosis, which are common features of ARCH type models, are manifest. In particular, it is shown that the sample autocorrelations of their squared processes vanish only very slowly, or do not even vanish at all, in the limit. Volatility clustering is therefore well expected. The NNH models with certain types of HGF's indeed have sample characteristics that are very similar to those of ARCH type models. Moreover, the sample kurtosis of the NNH model either diverges or has a stable limiting distribution with support truncated on the left by the kurtosis of the innovations. This would well explain the presence of leptokurtosis in many observed time series data. To illustrate the empirical relevancy of our model, we analyze the spreads between the forward and spot rates of USD/DM exchange rates. It is found that the conditional variances of the spreads can be well modelled as a nonlinear function of the levels of the spot rates.     

Evaluation of similarity models for expected utility violations

A body of work proposes a decision cost argument to explain expected utility (EU) violations based on pair similarity. These similarity models suggest various measures over the risky pairs that define decision costs and benefits. This paper assesses the empirical modeling success of these similarity measures in explaining risky choice patterns showing EU independence violations. We also compare model fit for these similarity models relative to EU and to a selected generalized EU model. Although the candidate models exhibit some degree of substitutability, our results indicate support for models that use relatively simple measures as instruments for similarity.     

Two new mean–variance enhanced index tracking models based on uncertainty theory

The enhanced index tracking (EIT) problem is concerned with selecting a tracking portfolio that achieves an excess return over a given benchmark with a minimum tracking error. This paper explores the EIT problem by providing two new mean–variance EIT models based on uncertainty theory where stock returns are treated as uncertain variables instead of random variables and stock return distributions are estimated by experts instead of from historical data. First, this paper formulates an uncertain enhanced index tracking (UEIT) model and analyzes the characteristic of the UEIT frontier. Then to reduce the tracking portfolio’s risk, this paper adds a risk index (RI) constraint to the UEIT model and proposes a UEIT-RI model. Next, by comparing the UEIT and UEIT-RI models this paper gives the advantages of the two models. Investors can choose the model according to their preferences. Finally, this paper conducts numerical examples to illustrate the application of the two models and the analysis results.     

Time series properties of ARCH processes with persistent covariates

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.     

Estimation of financial agent-based models with simulated maximum likelihood

This paper proposes a general computational framework for empirical estimation of financial agent-based models, for which criterion functions have unknown analytical form. For this purpose, we adapt a recently developed nonparametric simulated maximum likelihood estimation based on kernel methods. In combination with the model developed by Brock and Hommes (1998), which is one of the most widely analysed heterogeneous agent models in the literature, we extensively test the properties and behaviour of the estimation framework, as well as its ability to recover parameters consistently and efficiently using simulations. Key empirical findings indicate the statistical insignificance of the switching coefficient but markedly significant belief parameters that define heterogeneous trading regimes with a predominance of trend following over contrarian strategies. In addition, we document a slight proportional dominance of fundamentalists over trend-following chartists in major world markets.     

The role of executives in hostile takeover attempts

This paper proposes a two-stage game theoretic model where the discretionary power of executives acts as an implicit defense against hostile takeovers. Following managerial enterprise models, this paper analyzes the effects of discretionary power of target’s executives over R&D and advertising expenditures in defeating hostile takeover attempts. It is shown that in vertically differentiated industries, in equilibrium, target’s executives keep low level of R&D and advertising expenditures to make their firm an unattractive target for hostile takeovers. The model reveals that executives are influenced by their self-interest of monetary and non-monetary benefits and this self-interest makes the industry more differentiated.     

时变需求下耐烦期有限且购买费用滞后支付的库存模型

在时变需求下考虑耐烦期有限的库存模型。同时,将购买费用的滞后支付首次引入到该模型,提出了在三种不同的滞后支付规则下寻求最优订货策略的库存控制模型。     

Forecast combination for U.S. recessions with real-time data

This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index, as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use log-score and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's own forecasting performance.     

Predicting Major Economic Events with Accuracy: A New Framework for Scientific Macroeconomic Models

Despite intense studies over the last several hundred years, the questions about causes, forecasting, and prevention of economic crises remain unsolved. The poor performance of macroeconomic models during the Great Recession of 2008 has forced many economists to reexamine macroeconomic theories and search for credible alternatives to the agent‐based and general‐equilibrium models now currently used by most economists. This article derives a new category of macroeconomic model and applies it conceptually to explain the causes of economic crises. This model, known as the indeterministic balance sheet plus (IBS+) model, is a special breed of accounting models. It takes an indeterministic view of future balance sheets. This article proposes a classification of causes of economic crises using IBS+ models to analyze balance sheets of key economic sectors. Most economic crises are caused by mismanagement of balance sheets by key economic players, not by any fundamental flaw of capitalism. The frequency of economic crises can be minimized by proper risk management practices, but economic crises can never be completely eliminated. Historically, treating mismanagement of balance sheets as the main cause of economic crises is a generalization of Austrian business cycle theory, Fisher's debt deflation theory, and Minsky's financial instability hypothesis.     

Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model

Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatial dependence into the forecasting framework. Through the careful consideration of the temporal modeling component, complemented by support vector regression of the temporal model residuals, this work demonstrates that a DVINE copula model most accurately represents the residual spatial dependence. Additionally, this work proposes a complete set of validation mechanisms for multi-h-step forecasts that, when considered together, comprehensively evaluate accuracy. The model and validation mechanisms are demonstrated in two case studies, totaling ten wind farms in the Texas electric grid. The proposed method outperforms baseline and competitive models, with an average Continuous Ranked Probability Score of less than 0.15 for individual farms, and an average Energy Score of less than 0.35 for multiple farms, over the 24-hour-ahead horizon. Results show the model’s ability to replicate the power output dynamics through calibrated and sharp predictive densities.     

An algorithm for the solution of stochastic optimal control problems for large nonlinear econometric models

This paper considers the problem of solving an optimal control problem for large dynamic economic models which are both nonlinear and stochastic. It proposes a technique which combines conventional deterministic optimal control algorithms with the procedure of stochastic simulation, which calculates a numerical approximation to the distribution of the models endogenous variables. The new technique is computationally feasible for even large nonlinear models and, as an illustration of this, the Bank of England's large quarterly forecasting model is used in an example.     

操纵性应计模型的一个改进——基于动态面板估计的实证分析

使用动态面板对操纵性应计模型进行估计可以克服截面数据模型的内生性估计偏误。采用1999—2011年中国A股市场相关数据,对动态面板模型和其他模型的检测能力进行比较,结果表明：在对第Ⅱ类错误进行检验时,动态面板模型检测能力显著优于其他模型;在对第1类错误、审计师意见检验时,动态面板模型检测能力与其他模型接近。     

Modeling Growth Data Using Multidimensional Scaling Profile Analysis

This paper discusses how Multidimensional Scaling (MDS) can provide an exploratory technique for identifying major growth profiles, which may be indicative of growth associated with subgroups. It briefly overviews the conventional growth models and growth mixture models, examines the assumptions related to these models, and indicates some limitations associated with these models. It then proposes an exploratory growth profile analysis using the MDS model as a complement to more specification-oriented techniques. It describes the Profile Analysis via Multidimensional Scaling model (PAMS) and extends the model for longitudinal data. The MDS profile model can solve for the growth parameters such that each MDS dimension corresponds to a major growth profile. It is argued that the MDS model provides an exploratory tool for identifying growth trends and studying individual differences with respect to those growth trends. Since MDS has not traditionally been used for longitudinal studies, the MDS growth analysis can serve as the basis for studies of the kind discussed in the paper.