Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets

Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar’s (2006) framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1), GARCH(3,1), and GARCH (1,1), respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.Volatility Forecasting, Capital Market, Risk Management

Extreme value prediction via a quantile function model

AbstractMethods for estimating extreme loads are used in design as well as risk assessment. Regression using maximum likelihood or least squares estimation is widely used in a univariate analysis but these methods favour solutions that fit observations in an average sense. Here we describe a new technique for estimating extremes using a quantile function model. A quantile of a distribution is most commonly termed a ‘return level’ in flood risk analysis. The quantile function of a random variable is the inverse function of its distribution function. Quantile function models are different from the conventional regression models, because a quantile function model estimates the quantiles of a variable conditional on some other variables, while a regression model studies the conditional mean of a variable. So quantile function models allow us to study the whole conditional distribution of a variable via its quantile function, whereas conventional regression models represent the average behaviour of a variable.Little work can be found in the literature about prediction from a quantile function model. This paper proposes a prediction method for quantile function models. We also compare different types of statistical models using sea level observations from Venice. Our study shows that quantile function models can be used to estimate directly the relationships between sea condition variables, and also to predict critical quantiles of a sea condition variable conditional on others. Our results show that the proposed quantile function model and the developed prediction method have the potential to be very useful in practice

AN ECONOMIC ANALYSIS OF SERVICE-ORIENTED INFRASTRUCTURES FOR RISK/RETURN MANAGEMENT

Risk/return management has not only evolved as one of the key success factors for enterprises especially in the financial services industry, but is in the times of the financial crisis crucial for the survival of a company. It demands powerful and at the same time flexible computational resources making it an almost ideal application for service-oriented computing concepts. An essential characteristic of service-oriented infrastructures is that computational resources can be accessed on demand and paid per use. Taking the estimation of covariances for a portfolio of risky investment objects as an example, we propose quantification for the economic value of fast risk/return management calculations. Our model analyzes the influence factors on the optimal computing capacity dedicated to these calculations and reveals interesting insights in how far the optimal computing capacity depends on market parameters. Our main result is that more volatile markets require a lower computing capacity as the optimal computing capacity depends positively on changes of the market risk but negatively on the risk itself

Estimando o VaR (Value-at-Risk) de carteiras via modelos da família GARCH e via Simulação de Monte Carlo

The objective this work is to calculate the VaR of portfolios via GARCH family models with normal and t-student distribution and via Monte Carlo Simulation. It was used three portfolios composite with preferential stocks of five companies of the Ibovespa. The results show that the t distribution adjusts better to data, because the violation ratio of the VaR calculated with t distribution is less violation ratio estimated with normal distribution

Estimando o VaR (Value-at-Risk) de carteiras via modelos da família GARCH e via Simulação de Monte Carlo

The objective this work is to calculate the VaR of portfolios via GARCH family models with normal and t-student distribution and via Monte Carlo Simulation. It was used three portfolios composite with preferential stocks of five companies of the Ibovespa. The results show that the t distribution adjusts better to data, because the violation ratio of the VaR calculated with t distribution is less violation ratio estimated with normal distribution

Forecasting VaR and ES using a joint quantile regression and implications in portfolio allocation

In this paper we propose a multivariate quantile regression framework to forecast Value at Risk (VaR) and Expected Shortfall (ES) of multiple financial assets simultaneously, extending Taylor (2019). We generalize the Multivariate Asymmetric Laplace (MAL) joint quantile regression of Petrella and Raponi (2019) to a time-varying setting, which allows us to specify a dynamic process for the evolution of both VaR and ES of each asset. The proposed methodology accounts for the dependence structure among asset returns. By exploiting the properties of the MAL distribution, we then propose a new portfolio optimization method that minimizes the portfolio risk and controls for well-known characteristics of financial data. We evaluate the advantages of the proposed approach on both simulated and real data, using weekly returns on three major stock market indices. We show that our method outperforms other existing models and provides more accurate risk measure forecasts compared to univariate ones

Measuring Predictability of Oil and Gas Stock Returns and Performance of Moving Average Trading Rules

The paper re-examines whether investors can predict oil and gas stock prices for abnormal returns using autocorrelation-based trading and filter rules and moving average strategies. In this paper, short and long lengths moving averages are employed and their performances are measured against the returns from simple buy and hold investment strategy. As a result, the paper finds that employed trading rules do not indicate that investors can make abnormal returns in oil and gas stocks. Moreover, the performances of short and long moving averages in predicting abnormal returns also do not suggest a conclusive evidence that any of the moving averages can result in more returns compared to others

Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index

This paper analyses the predictive power of the DJIA index returns, measured at different quantiles of its distribution, for future return distribution. The returns measured at quantile 0.75 have predictive power for most quantiles of future returns, except for their median. This result prevails after controlling for the predictive power of the lagged first four moments of returns and of other economic predictors used in the literature. Furthermore, this finding is stable over time. Forecasts of future mean returns based on predicted return quantiles have positive economic value, as do forecasts of future volatility, the latter especially for investors with low risk aversion. The predictive power of quantile 0.75 DJIA returns is shown to be the result of their ability to forecast shocks to future investment and consumption

Encompassing tests for value at risk and expected shortfall multi-step forecasts based on inference on the boundary

We propose forecast encompassing tests for the Expected Shortfall (ES) jointly with the Value at Risk (VaR) based on flexible link (or combination) functions. Our setup allows testing encompassing for convex forecast combinations and for link functions which preclude crossings of the combined VaR and ES forecasts. As the tests based on these link functions involve parameters which are on the boundary of the parameter space under the null hypothesis, we derive and base our tests on nonstandard asymptotic theory on the boundary. Our simulation study shows that the encompassing tests based on our new link functions outperform tests based on unrestricted linear link functions for one-step and multi-step forecasts. We further illustrate the potential of the proposed tests in a real data analysis for forecasting VaR and ES of the S&P 500 index

Υποδείγαμτα εκτίμησης κινδύνου αγοράς με μέτρα value at risk. Εμπειρική ανάλυση σε αποδόσεις μετοχών εισηγμένων εταιρειών στις Η.Π.

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Μαθηματική Προτυποποίηση σε Σύγχρονες Τεχνολογίες στην Οικονομία