Nonlinear analysis for forecasting currencies: are they useful to the portfolio manager?

The importance of a time-varying specification for both the return and the risk of financial assets is well known. The purpose of this study is to investigate if some of the most recently developed econometric models, combined with technical indicators often used by practitioners, can significantly predict future returns. While most studies have focused on either univariate series or in-sample analyses of a given econometric specification, this study considers a multivariate framework where a US based investor daily reallocates a portfolio of three currencies (Deutschmark, Swiss Franc and Japanese Yen). Series of three years out-of-sample forecasts are analysed in terms of risk and return and it is shown that some of the tested speciications can indeed signiicantly predict future daily returns and correlations over this three-year period.     

Discussions on the spurious hyperbolic memory in the conditional variance and a new model

This paper studies the spurious hyperbolic memory in the conditional variance caused by the Markov Regime-Switching GARCH (MRS-GARCH) process. We firstly propose an illustrative cause of this spuriousness and provide simulation evidence. An MRS Hyperbolic GARCH (MRS-HGARCH) model is then developed to successfully address it. Related statistical properties including the stationarity conditions and asymptotic behaviours of the maximum likelihood estimators of the MRS-HGARCH process are also investigated. An empirical study of the S&P 500 and TOPIX indexes returns is then conducted which demonstrates that our MRS-HGARCH model can provide a more reliable estimator of the hyperbolic-memory parameter and outperform both the HGARCH and MRS-GARCH models.     

Estimation of tail‐related risk measures in the Indian stock market: An extreme value approach

The purpose of the study is to estimate tail-related risk measures using extreme value theory (EVT) in the Indian stock market. The study employs a two stage approach of conditional EVT originally proposed by McNeil and Frey (2000) to estimate dynamic Value at Risk (VaR) and expected shortfall (ES). The dynamic risk measures have been estimated for different percentiles for negative and positive returns. The estimates of risk measures computed under different quantile levels exhibit strong stability across a range of the selected thresholds, implying the accuracy and reliability of the estimated quantile based risk measures.     

Public information arrival and volatility persistence in financial markets

This paper explores the relationship between daily market volatility and the arrival of public information in four different financial markets. Public information is measured as the daily number of economic news headlines, divided in six categories of news. Statistical analysis of the news data suggests the presence of particular seasonality effects, as well as a strong degree of autocorrelation. Over the period 1994–1998, significant effects of specific news categories on the volatility of US stocks, treasury bills, bonds and dollar were detected. However, the effects – in size and duration – vary by news category and by financial market. It is demonstrated that most of the volatility persistence, as observed by GARCH models, tends to disappear when news is included in the conditional variance equation.     

Option pricing based on hybrid GARCH-type models with improved ensemble empirical mode decomposition

The exploration of option pricing is of great significance to risk management and investments. One important challenge to existing research is how to describe the underlying asset price process and fluctuation features accurately. Considering the benefits of ensemble empirical mode decomposition (EEMD) in depicting the fluctuation features of financial time series, we construct an option pricing model based on the new hybrid generalized autoregressive conditional heteroskedastic (hybrid GARCH)-type functions with improved EEMD by decomposing the original return series into the high frequency, low frequency and trend terms. Using the locally risk-neutral valuation relationship (LRNVR), we obtain an equivalent martingale measure and option prices with different maturities based on Monte Carlo simulations. The empirical results indicate that this novel model can substantially capture volatility features and it performs much better than the M-GARCH and Black–Scholes models. In particular, the decomposition is consistently helpful in reducing option pricing errors, thereby proving the innovativeness and effectiveness of the hybrid GARCH option pricing model.     

Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

(G)ARCH-type models are frequently used for the dynamic modelling and forecasting of risk attached to speculative asset returns. While the symmetric and conditionally Gaussian GARCH model has been generalized in a manifold of directions, model innovations are mostly presumed to stem from an underlying IID distribution. For a cross section of 18 stock market indices, we notice that (threshold) (T)GARCH-implied model innovations are likely at odds with the commonly held IID assumption. Two complementary strategies are pursued to evaluate the conditional distributions of consecutive TGARCH innovations, a non-parametric approach and a class of standardized copula distributions. Modelling higher order dependence patterns is found to improve standard TGARCH-implied conditional value-at-risk and expected shortfall out-of-sample forecasts that rely on the notion of IID innovations.     

The impact of FIFA’s official announcements on the stock market of Qatar: The case of the 2022 World Cup

This study examines the impact of the FIFA’s official announcements on Doha Stock Exchange (DSE) of Qatar with respect to the 2022 World Cup. Using the abnormal unsystematic volatility method of Hilliard and Savickas (2002), our empirical findings reveal that the DSE market is sensitive to FIFA’s announcements about the 2022 World Cup. We find that four out of six FIFA announcements act as primary drivers to the DSE market volatility. The significant reactions of the DSE market to these announcements unveil the investors’ sentiments about the fate of the governmental and private expenditures on medium- and long-term projects undertaken in anticipation of hosting the 2022 World Cup. The results have some implications to investors in this newly emerging market related to this global sporting event. Any future announcements, good or bad, are likely to impact share prices in DSE market and trigger portfolio reallocation by local and international investors, leading to increased volatility.     

The use of open source internet to analysis and predict stock market trading volume

The objective of this paper is to evaluate the impact of information demand and supply on stock market trading volume. Few studies have demonstrated the role of Google search data in analyzing trading volume activity. In this study, we employ a proxy for information demand which is derived from weekly internet search volume. The latest is from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. We use Garch model to analyze and predict trading volume.The empirical results present new evidences. First, information supply has an impact on trading volume but information demand's impact is much more important. Secondly, by applying MCA to results found, it could be concluded that the impact of public information on transaction volume is conditioned by two elements: the firm and market news disclosure and the second element relates to the characteristics of the market participants, more precisely their news interpretations and their risk aversion. Thirdly, we used Chow structural break test to verify the stability of our model. We found that for securities with structural changes, information demand is the responsible variable of the change in our model. Finally, we found that information variables have a predictive power on transaction volume.This paper contributes to existing literature by incorporating open source internet-based data into the analysis and prediction of transaction volume. Using internet information about the stock market, which has appeared recently as an interesting research for financial empiricists, computer scientists and practitioners, will have a very important utility because quantifying demand and supply of information becomes possible.     

Backtesting Value-at-Risk: A Duration-Based Approach

Financial risk model evaluation or backtesting is a key partof the internal model's approach to market risk management aslaid out by the Basle Committee on Banking Supervision. However,existing backtesting methods have relatively low power in realisticsmall sample settings. Our contribution is the exploration ofnew tools for backtesting based on the duration of days betweenthe violations of the Value-at-Risk. Our Monte Carlo resultsshow that in realistic situations, the new duration-based testshave considerably better power properties than the previouslysuggested tests.     

International Asset Excess Returns and Multivariate Conditional Volatilities

This paper constructs a multivariate model in relating multi-asset excess returns to their conditional variances. Applying weekly data to investigate the foreign-exchange risk premium, the evidence from a multivariate GARCH model shows that the foreign-exchange excess returns are significantly correlated with economic fundamentals such as the real interest-rate differential, long-short interest-rate spread differential, and equity-premium differential. The evidence also suggests that foreign-exchange excess returns are not independent of the conditional variances of these fundamental variables, supporting the time-varying risk-premium hypothesis.