Evaluating portfolio Value-at-Risk using semi-parametric GARCH models

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for evaluating the Value-at-Risk (VaR) of a portfolio with arbitrary weights. We specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within-sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations.     

GARCH options via local risk minimization

We apply a quadratic hedging scheme developed by Föllmer, Schweizer, and Sondermann to European contingent products whose underlying asset is modeled using a GARCH process and show that local risk-minimizing strategies with respect to the physical measure do exist, even though an associated minimal martingale measure is only available in the presence of bounded innovations. More importantly, since those local risk-minimizing strategies are in general convoluted and difficult to evaluate, we introduce Girsanov-like risk-neutral measures for the log-prices that yield more tractable and useful results. Regarding this subject, we focus on GARCH time series models with Gaussian innovations and we provide specific sufficient conditions concerning the finiteness of the kurtosis, under which those martingale measures are appropriate in the context of quadratic hedging. When this equivalent martingale measure is adapted to the price representation we are able to recover the classical pricing formulas of Duan and Heston and Nandi, as well as hedging schemes that improve the performance of those proposed in the literature.     

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.     

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.     

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.     

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.     

Bayesian Statistical Computations of Nonlinear Financial Time Series Models: A Survey with Illustrations

A survey of Bayesian statistical computations of quadratureformula, Laplace approximation, and Markov Chain Monte Carlo algorithms ispresentedand their applications to nonlinear financial time series models arediscussed.     

The information content of implied volatility in developed versus developing FX markets

This article predicts the daily movement of monthly foreign exchange (FX) rate volatility using a linear combination of a time-series model and implied volatilities from options. The focus is on analysing the FX volatilities in three developing economies (the Brazilian real (BRL), the Indian rupee (INR) and the Russian ruble (RUB)) against the US dollar (USD). The empirical exercise utilizes two time-series models, mixed data sampling (MIDAS) and GARCH. The analysis indicates that for both developed and developing economies the predictive power of MIDAS and that of GARCH is comparable. Further on in this article, we will ascertain whether the relationship between realized and implied volatility is fundamentally different in the case of developing economies from that among developed economies. Thus, we compare the pairs USD/BRL, USD/INR and USD/RUB against EURO/USD and USD/Japanese yen to determine the information content and predictive power of implied volatilities. Plots of the MIDAS coefficients show that the volatility is more persistent in developing economies than in developed economies.     

Volatility Relationship between Crude Oil and Petroleum Products

This paper utilizes calculated historical volatility and GARCH models to compare the historical price volatility behavior of crude oil, motor gasoline and heating oil in U.S. markets since 1990. We incorporate a shift variable in the GARCH/TARCH models to capture the response of price volatility to a change in OPEC’s pricing behavior. This study has three major conclusions. First, there was an increase in volatility as a result of a structural shift to higher crude oil prices after April 1999. Second, volatility shocks from current news are not important since GARCH effects dominate ARCH effects in the variance equation. Third, persistence of volatility in all commodity markets is quite transitory, with half-lives normally being a few weeks.

Impact of distance on the arrivals,behaviours and attitudes of international tourists in Hong Kong: A longitudinal approach

This study revisits the impact of distance on international tourist behaviours in Hong Kong. This work divides and cross-validates the concept of distance into physical and cultural distance. This work also proposes an alternative cultural distance measure by introducing optimal weight amongst Hofstede's dimensions and then compares the proposed measure with the traditional Kogut and Singh's and Kandogan's measures. By using data from the Visitor Profile Report of the Hong Kong Tourism Board and the World Trade Organisation from 2002 to 2017, along with latent growth curve modelling, multivariate regression and panel data analysis, findings confirmed the significant role of physical and cultural distance. In addition, quadratic relationships are detected using cross-validation methods. The effect of physical distance on tourist demands clearly dominates that of cultural distance in the overall market. The problem of spurious correlation and the results of three cultural distance measures are also discussed.