Kittawit Autchariyapanitkul

Capital Asset Pricing Model with Interval Data

We used interval-valued data to predict stock returns rather than just point valued data. Specifically, we used these interval values in the classical capital asset pricing model to estimate the beta coefficient that represents the risk in the portfolios management analysis. We also use the method to obtain a point valued of asset returns from the interval-valued data to measure the sensitivity of the asset return and the market return. Finally, AIC criterion indicated that this approach can provide us better results than use the close price for prediction.

A Copula-Based Stochastic Frontier Model for Financial Pricing

We use the concept of a stochastic frontier in production to analyses the problem of pricing in stock markets. By modifying the classical stochastic frontier model to accommodate for errors dependency, using copulas, we show that our extended stochastic frontier model is more suitable for financial analyses. The validation is achieved by using AIC in our model selection problem.

Volatility Linkages Between Price Returns of Crude Oil and Crude Palm Oil in the ASEAN Region: A Copula Based GARCH Approach

This paper used the copula based ARMA-GARCH to examine the dependence structure between the weekly prices of two commodities, namely Crude oil and Crude palm oil. We found evidence of a weak positive dependence between two commodities prices. These findings suggest that the crude oil market of the Middle East and the crude palm oil market of Malaysia are linked together. This information is useful for decision making in various area, such as the risk management in financial field and the international trade in agricultural commodities.

Quantum Econometrics: How to Explain Its Quantitative Successes and How the Resulting Formulas Are Related to Scale Invariance, Entropy, and Fuzziness

Many aspects of human behavior seem to be well-described by formulas of quantum physics. In this paper, we explain this phenomenon by showing that the corresponding quantum-looking formulas can be derived from the general ideas of scale invariance and fuzziness. We also use these ideas to derive a general family of formulas that include non-quantum and quantum probabilities as particular cases – formulas that may be more adequate for describing human behavior than purely non-quantum or purely quantum ones.

Joint Plausibility Regions for Parameters of Skew Normal Family

The estimation of parameters is a challenge issue for skew normal family. Based on inferential models, the plausibility regions for two parameters of skew normal family are investigated in two cases, when either the scale parameter \(\sigma \) or the shape parameter \(\delta \) is known. For illustration of our results, simulation studies are proceeded.

Measures of Mutually Complete Dependence for Discrete Random Vectors

In this paper, a marginal-free measure of mutually complete dependence for discrete random vectors through subcopulas is defined, which generalizes the corresponding results for discrete random variables. Properties of the measure are studied and an estimator of the measure is introduced. Several examples are given for illustration of our results.

Fuzzy techniques explain empirical power law governing wars and terrorist attacks

The empirical distribution of the number of casualties in wars and terrorist attacks follows a power law with exponent 2.5. So far, there has not been a convincing explanation for this empirical fact. In this paper, we show that by using fuzzy techniques, we can explain this exponent. Interesting, we can also get a similar explanation if we use probabilistic techniques. The fact that two different techniques lead to the same explanation makes us reasonably confident that this explanation is correct.

Uncertain information fusion and knowledge integration: How to take reliability into account

In many practical situations, we need to fuse and integrate information and knowledge from different sources — and do it under uncertainty. Most existing methods for information fusion and knowledge integration take into account uncertainty. In addition to uncertainty, we also face the problem of reliability: sensors may malfunction, experts can be wrong, etc. In this paper, we show how to take into account both uncertainty and reliability in information fusion and knowledge integration. We show this on the examples of probabilistic and fuzzy uncertainty.

The Impact of Extreme Events on Portfolio in Financial Risk Management

We use the concept of copula and extreme value theory to evaluate the impact of extreme events such as flooding, nuclear disaster, etc. on the industry index portfolio. A t copulas based on GARCH model is applied to explain a portfolio risk management with high-dimensional asset allocation. Finally, we calculate the condition Value-at-Risk (CVaR) with the hypothesis of t joint distribution to construct the potential frontier of the portfolio during the times of crisis.

Stochastic Frontier Model in Financial Econometrics: A Copula-Based Approach

This study applies the principle of stochastic frontier model (SFM) to calculate the optimal frontier of the stock prices in a stock market. We use copula to measure dependence between the error terms in SFM by examining several stocks in Down Jones industrial. The results show that our modified stochastic frontier model is more applicable for financial econometrics. Finally, we use AIC for model selection.