Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH-EVT-Copula model

This paper introduces GARCH-EVT-Copula model and applies it to study the risk of foreign exchange portfolio. Multivariate Copulas, including Gaussian, t and Clayton ones, were used to describe a portfolio risk structure, and to extend the analysis from a bivariate to an n-dimensional asset allocation problem. We apply this methodology to study the returns of a portfolio of four major foreign currencies in China, including USD, EUR, JPY and HKD. Our results suggest that the optimal investment allocations are similar across different Copulas and confidence levels. In addition, we find that the optimal investment concentrates on the USD investment. Generally speaking, t Copula and Clayton Copula better portray the correlation structure of multiple assets than Normal Copula.     

Measuring the default risk of sovereign debt from the perspective of network

Recently, there has been a growing interest in network research, especially in the fields of biology, computer science, and sociology. It is natural to address complex financial issues such as the European sovereign debt crisis from the perspective of network. In this article, we construct a network model according to the debt–credit relations instead of using the conventional methodology to measure the default risk. Based on the model, a risk index is examined using the quarterly report of consolidated foreign claims from the Bank for International Settlements (BIS) and debt/GDP ratios among these reporting countries. The empirical results show that this index can help the regulators and practitioners not only to determine the status of interconnectivity but also to point out the degree of the sovereign debt default risk. Our approach sheds new light on the investigation of quantifying the systemic risk.     

The decay of theτ lepton to six pions

We present the branching ratio predictions of an isospin statistical model forτ decays to four, five and six pions. Limits on the branching ratios of the three possible six pionτ decay modes using the Conserved Vector Current (CVC) Hypothesis and thee ⁺ e ^?→6π cross section data are also presented. We find that the isospin model prediction is in good agreement with the well-measured four pion decay modes and consistent with the five pion decay modes. However, we find that some of the recent six pionτ measurements do not agree with the isospin model and the CVC prediction.     

Power law and multiscaling properties of the Chinese stock market

We investigate the cumulative probability density function (PDF) and the multiscaling properties of the returns in the Chinese stock market. By using returns data adjusted for thin trading, we find that the distribution has power-law tails at shorter microscopic timescales or lags. However, the distribution follows an exponential law for longer timescales. Furthermore, we investigate the long-range correlation and multifractality of the returns in the Chinese stock market by the DFA and MFDFA methods. We find that all the scaling exponents are between 0.5 and 1 by DFA method, which exhibits the long-range power-law correlations in the Chinese stock market. Moreover, we find, by MFDFA method, that the generalized Hurst exponents h(q) are not constants, which shows the multifractality in the Chinese stock market. We also find that the correlation of Shenzhen stock market is stronger than that of Shanghai stock market.     

Multifractal properties of the Indian financial market

We investigate the multifractal properties of the logarithmic returns of the Indian financial indices (BSE & NSE) by applying the multifractal detrended fluctuation analysis. The results are compared with that of the US S&P 500 index. Numerically we find that qth-order generalized Hurst exponents h(q) and τ(q) change with the moments q. The nonlinear dependence of these scaling exponents and the singularity spectrum f(α) show that the returns possess multifractality. By comparing the MF-DFA results of the original series to those for the shuffled series, we find that the multifractality is due to the contributions of long-range correlations as well as the broad probability density function. The financial markets studied here are compared with the Binomial Multifractal Model (BMFM) and have a smaller multifractal strength than the BMFM.     

Long term memory in extreme returns of financial time series

It is well known that while daily price returns of financial markets are uncorrelated, their absolute values (‘volatility’) are long-term correlated. Here we provide evidence that certain subsequences of the returns themselves also exhibit long-term memory. These subsequences consist of maxima (or minima) of returns in consecutive time windows of R days. Our analysis shows that for both stocks and currency exchange rates, long-term correlations are significant for R≥4. We argue that this long-term memory which is similar to that observed in volatility clustering sheds further insight on price dynamics that might be used for risk estimation.     

Proton decay in supersymmetric models

We discuss proton decay in supersymmetric theories. We find that it is possible to obtain rates which are comparable with those of standard SU(5). In the presence of a discrete symmetry which occurs in an SU(5) supersymmetric unified model we obtain a definite prediction for the dominant decay mode, i.e. p → K^+?ν_μ and n → K^0?ν_μ.     

Multifractality in stock indexes: Fact or Fiction?

Multifractal analysis and extensive statistical tests are performed upon intraday minutely data within individual trading days for four stock market indexes (including HSI, SZSC, S&P 500, and NASDAQ) to check whether the indexes (instead of the returns) possess multifractality. We find that the mass exponent τ(q) is linear and the singularity α(q) is close to 1 for all trading days and all indexes. Furthermore, we find strong evidence showing that the scaling behaviors of the original data sets cannot be distinguished from those of shuffled time series. Hence, the so-called multifractality in the intraday stock market indexes is merely an illusion.     

Measuring time dependent volatility and cross-sectional correlation in Australian equity returns

In this study we examine the time-dependent nature of volatility and cross-correlation of Australian equity returns data. Volatility and correlation estimates are calculated using methods that allow for non-stationary behaviour. By averaging the estimates across the entire data set we show that the correlation in ASX stock returns displays evidence of significant time-dependent behaviour. We also find that the volatility estimates do not display similar non-stationary patterns.     

Default risk modeling with position-dependent killing

Diffusion in a linear potential in the presence of position-dependent killing is used to mimic a default process. Different assumptions regarding transport coefficients, initial conditions, and elasticity of the killing measure lead to diverse models of bankruptcy. One “stylized fact” is fundamental for our consideration: empirically default is a rather rare event, especially in the investment grade categories of credit ratings. Hence, the action of killing may be considered as a small parameter. In a number of special cases we derive closed-form expressions for the entire term structure of the cumulative probability of default, its hazard rate, and intensity. Comparison with historical data on aggregate global corporate defaults confirms the validity of the perturbation method for estimations of long-term probability of default for companies with high credit quality. On a single company level, we implement the derived formulas to estimate the one-year likelihood of default of Enron on a daily basis from August 2000 to August 2001, three months before its default, and compare the obtained results with forecasts of traditional structural models.     

Testing an agent-based model of bacterial cell motility: How nutrient concentration affects speed distribution

We revisit a recently proposed agent-based model of active biological motion and compare its predictions with own experimental findings for the speed distribution of bacterial cells, Salmonella typhimurium. Agents move according to a stochastic dynamics and use energy stored in an internal depot for metabolism and active motion. We discuss different assumptions of how the conversion from internal to kinetic energy d(v) may depend on the actual speed, to conclude that d ₂ v ^ξ with either ξ = 2 or 1 < ξ < 2 are promising hypotheses. To test these, we compare the model’s prediction with the speed distribution of bacteria which were obtained in media of different nutrient concentration and at different times. We find that both hypotheses are in line with the experimental observations, with ξ between 1.67 and 2.0. Regarding the influence of a higher nutrient concentration, we conclude that the take-up of energy by bacterial cells is indeed increased. But this energy is not used to increase the speed, with 40 μm/s as the most probable value of the speed distribution, but is rather spend on metabolism and growth.     

Testing the PCAC prediction for the π0 lifetime

We compare the PCAC prediction based on the chiral anomaly for the π⁰ lifetime with a recent experiment. We find that the QED corrections are as large as 3%. After these corrections the experiment and the PCAC prediction disagree by 8.5 ± 2%. We estimate the corrections to the PCAC prediction. We find that reliably calculable corrections are not large enough to account for the discrepancy. However, contributions from the excited π⁰'s may be able to explain the difference.     

Chaotic time series prediction: From one to another

In this Letter, a new local linear prediction model is proposed to predict a chaotic time series of a component x(t) by using the chaotic time series of another component y(t) in the same system with x(t). Our approach is based on the phase space reconstruction coming from the Takens embedding theorem. To illustrate our results, we present an example of Lorenz system and compare with the performance of the original local linear prediction model.     

Zeroing in on SU(3)

We present an improved numerical method for calculating the density of states for lattice field theories. We use it to study the SU(3) pure gauge theory at both zero and finite temperature. We also compute strong and weak coupling expansions for the density of states and find excellent agreement with our data. Using a specially developed algorithm for solving high-order polynomials, we find the zeroes of the partition function. For lattices with L_t = 2, we test the finite-size scaling prediction for the rounding of the transition by following the motion of these zeroes for L_s=6, 8, 10, and 12. We find that the correlation length exponent is 1/v = 3.02 ± 0.05, in excellent agreement with the value d=3 expected for a first-order deconfinement transition.     

The minority game with incomplete strategies

This paper proposes a new model of Incomplete Minority Game (IMG), which features a default hierarchy of rules. This model introduces random bits into players’ individual strategies and is capable of applying the exception rules in the absence of the default one. Analysis of the numerical experiment results indicates that, in comparison with the standard Minority Game (SMG) model, this IMG model expands the maximum ensemble of uncorrelated strategies (MEUS) and excels in the effective strategy set and dynamic evolution of individual strategies, which enhance the overall performance by reaching an approximate ideal status in a shorter time with less memory steps and more stable combination of strategies. This paper also discusses the practical implication of the new IMG model.     

Universal fluctuations of the AEX index

We compute the analytic expression of the probability distributions F_AEX,+ and F_AEX,− of the normalized positive and negative AEX (Netherlands) index daily returns r(t). Furthermore, we define the α re-scaled AEX daily index positive returns r(t)^α and negative returns (−r(t))^α, which we call, after normalization, the α positive fluctuations and α negative fluctuations. We use the Kolmogorov-Smirnov statistical test as a method to find the values of α that optimize the data collapse of the histogram of the α fluctuations with the Bramwell-Holdsworth-Pinton (BHP) probability density function. The optimal parameters that we found are α⁺=0.46 and α⁻=0.43. Since the BHP probability density function appears in several other dissimilar phenomena, our result reveals a universal feature of stock exchange markets.     

Effective return,risk aversion and drawdowns

We derive two risk-adjusted performance measures for investors with risk averse preferences. Maximizing these measures is equivalent to maximizing the expected utility of an investor. The first measure, X_eff, is derived assuming a constant risk aversion while the second measure, R_eff, is based on a stronger risk aversion to clustering of losses than of gains. The clustering of returns is captured through a multi-horizon framework. The empirical properties of X_eff, R_eff are studied within the context of real-time trading models for foreign exchange rates and their properties are compared to those of more traditional measures like the annualized return, the Sharpe Ratio and the maximum drawdown. Our measures are shown to be more robust against clustering of losses and have the ability to fully characterize the dynamic behaviour of investment strategies.     

The predictive performance of a path-dependent exotic-option credit risk model in the emerging market

Most empirical research of the path-dependent, exotic-option credit risk model focuses on developed markets. Taking Taiwan as an example, this study investigates the bankruptcy prediction performance of the path-dependent, barrier option model in the emerging market. We adopt Duan’s (1994) [11], (2000) [12] transformed-data maximum likelihood estimation (MLE) method to directly estimate the unobserved model parameters, and compare the predictive ability of the barrier option model to the commonly adopted credit risk model, Merton’s model. Our empirical findings show that the barrier option model is more powerful than Merton’s model in predicting bankruptcy in the emerging market. Moreover, we find that the barrier option model predicts bankruptcy much better for highly-leveraged firms. Finally, our findings indicate that the prediction accuracy of the credit risk model can be improved by higher asset liquidity and greater financial transparency.     

Ensemble Linear Subspace Analysis of High-Dimensional Data

Regression models provide prediction frameworks for multivariate mutual information analysis that uses information concepts when choosing covariates (also called features) that are important for analysis and prediction. We consider a high dimensional regression framework where the number of covariates (p) exceed the sample size (n). Recent work in high dimensional regression analysis has embraced an ensemble subspace approach that consists of selecting random subsets of covariates with fewer than p covariates, doing statistical analysis on each subset, and then merging the results from the subsets. We examine conditions under which penalty methods such as Lasso perform better when used in the ensemble approach by computing mean squared prediction errors for simulations and a real data example. Linear models with both random and fixed designs are considered. We examine two versions of penalty methods: one where the tuning parameter is selected by cross-validation; and one where the final predictor is a trimmed average of individual predictors corresponding to the members of a set of fixed tuning parameters. We find that the ensemble approach improves on penalty methods for several important real data and model scenarios. The improvement occurs when covariates are strongly associated with the response, when the complexity of the model is high. In such cases, the trimmed average version of ensemble Lasso is often the best predictor.