Numerical computation of Theta in a jump-diffusion model by integration by parts

Using the Malliavin calculus in time inhomogeneous jump-diffusion models, we obtain an expression for the sensitivity Theta of an option price (with respect to maturity) as the expectation of the option payoff multiplied by a stochastic weight. This expression is used to design efficient numerical algorithms that are compared with traditional finite-difference methods for the computation of Theta. Our proof can be viewed as a generalization of Dupire's integration by parts to arbitrary and possibly non-smooth payoff functions. In the time homogeneous case, Theta admits an expression from the Black–Scholes PDE in terms of Delta and Gamma but the representation formula obtained in this way is different from ours. Numerical simulations are presented in order to compare the efficiency of the finite-difference and Malliavin methods.     

An empirical re-examination of the dividend–investment relation

Modigliani and Miller show that, in perfect capital markets, the optimal investment decisions of a firm are not affected by how these investments are financed. Miller and Modigliani further imply that, under the assumption of perfect capital markets, a firm's investment decisions are not affected by its dividend decisions, although dividend decisions may or may not be influenced by investment decisions. Fama and Miller label this result the separation principle. Most recent studies of the separation principle that take into account the existence of market imperfections report sharply contradictory results. This paper tests for linear and nonlinear causality between dividends and investments using both firm-specific and aggregate data for a sample of 417 firms over the 1962 to 2004 period. In general, linear causality tests support the separation principle, whereas nonlinear causality test results contradict the separation principle by revealing strong bi-directional linkages between dividends and investments.     

Portfolio allocation and the investment horizon: a multiscaling approach

A new approach is proposed for analysing portfolio allocation over various time scales. This new approach is based on wavelet analysis, which decomposes a given time series on a scale-by-scale basis. Empirical results indicate that, as the investment horizon lengthens, a greater weighting should be allocated to stocks. An explanation for this result is that the mean-reverting property of stock returns causes investors to perceive that stocks are less risky than bonds and T-bills at longer time scales compared with shorter time scales. When we include the effect of risk aversion, it is found that the higher the risk aversion, the less the Sharpe ratio, indicating that a more conservative investor prefers a smoother consumption stream.     

An economic evaluation of stock–bond return comovements with copula-based GARCH models

Owing to their importance in asset allocation strategies, the comovements between the stock and bond markets have become an increasingly popular issue in financial economics. Moreover, the copula theory can be utilized to construct a flexible joint distribution that allows for skewness in the distribution of asset returns as well as asymmetry in the dependence structure between asset returns. Therefore, this paper proposes three classes of copula-based GARCH models to describe the time-varying dependence structure of stock–bond returns, and then examines the economic value of copula-based GARCH models in the asset allocation strategy. We compare their out-of-sample performance with other models, including the passive, the constant conditional correlation (CCC) GARCH and the dynamic conditional correlation (DCC) GARCH models. From the empirical results, we find that a dynamic strategy based on the GJR-GARCH model with Student-t copula yields larger economic gains than passive and other dynamic strategies. Moreover, a less risk-averse investor will pay higher performance fees to switch from a passive strategy to a dynamic strategy based on copula-based GARCH models.     

Gram–Charlier densities: a multivariate approach

This paper introduces a new family of multivariate distributions based on Gram–Charlier and Edgeworth expansions. This family encompasses many of the univariate semi-non-parametric densities proposed in financial econometrics as marginal of its different formulations. Within this family, we focus on the analysis of the specifications that guarantee positivity to obtain well-defined multivariate semi-non-parametric densities. We compare two different multivariate distributions of the family with the multivariate Edgeworth–Sargan, Normal, Student's t and skewed Student's t in an in- and out-of-sample framework for financial returns data. Our results show that the proposed specifications provide a reasonably good performance, and would therefore be of interest for applications involving the modelling and forecasting of heavy-tailed distributions.     

MaxVaR for non-normal and heteroskedastic returns¶

In this work we propose Monte Carlo simulation models for dynamically computing MaxVaR for a financial return series. This dynamic MaxVaR takes into account the time-varying volatility as well as non-normality of returns or innovations. We apply this methodology to five stock market indices. To validate the proposed methods we compute the number of MaxVaR violations and compare them with the expected number. We also compute the MaxVaR-to-VaR ratio and find that, on average, dynamic MaxVaR exceeds dynamic VaR by 5–7% at the 1% significance level, and by 12–14% at the 5% significance level for the selected indices.     

When does low interconnectivity cause systemic risk?

We analysed a systemic liquidity crisis by using a unique money market data-set in which the coded identity of the counterparties of each trade is known. Contrary to recent findings, we did not observe a positive relationship between interconnectivity and systemic risk. We have concluded that our conflicting findings can be related to the degree of market concentration on the borrowing side of the funding market. High level of concentration in the borrowing side led to lower interconnectivity but higher systemic risk prior to the crisis. We conclude that measures of market heterogeneity should be used to generalize the relationship between systemic risk and interconnectivity.     

个人投资者的行为金融学分析

行为金融是当前金融经济学的研究热点。它注重研究投资者的实际决策行为,解释金融市场上的各种现象。个人投资者行为分析是行为金融研究的重要组成部分。行为金融较为成功地解释了个人投资者的判断与决策、投资组合、策略与交易以及退休储蓄等方面的行为。     

Predictability of stock markets with disequilibrium trading

This paper analyses the predictability of a hypothetical market with freely negotiated prices on which exists a censoring of one-period returns which are in excess of an arbitrary level (‘floor’ and ‘ceiling’). It is shown that the expected value of returns (adjusted for drift) conditional on last period information regarding the censoring are equal to zero (and therefore the market is not predictable in mean) if there is no intertemporal spillover on the market. A simple simulation model is proposed and applied for the analysis of the effects of intertemporal and cross-spillovers resulting from quantity constraints. Statistical predictability tests are proposed, based on the corrected Student-t statistic of a regression of returns of some information concerning the previous censoring. An illustrative empirical analysis of six main time series of returns on the Warsaw Stock Exchange confirms their ex-ante, but not ex-post, predictability.