As one of the world’s largest securities markets, the Hong Kong stock market plays a significant role in facilitating the development of Chinese economy. In this paper, we investigate a suite of widely-used models, the GARCH models in risk management of the Hong Kong stock market returns. To account for conditional volatilities, we consider a new type of fat-tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), and compare its empirical performance with two other popular types of fat-tailed distribution, the Student’s t distribution and the normal inverse Gaussian distribution (NIG). We show that the NRIG distribution performs slightly better than the other two types of distribution. Also, our results indicate that it is important to introduce both GJR-terms and the NRIG distribution to improve the models’ performance. Our results illustrate that the asymmetric GARCH NRIG model has practical advantages in quantitative risk management, and serves as a very useful tool for industry participants.

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GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

This paper studies how information is transmitted across nations by focusing on three types of commodities: copper, soybean and wheat. The paper utilizes Johansen cointegration model, vector error correction model (VECM) and the generalized autoregressive conditional heteroskedastic model (GARCH) to investigate the price discovery and volatility spillover process of informationally-linked futures markets. The empirical results indicate that the models provide evidence to support the long-term equilibrium relationships and significant bidirectional information flows between copper futures markets in China and in the United States. Although innovations in one market can predict the futures volatility in another market, the volatility spillovers from U.S. futures to Chinese futures are more significant than the other way around. As for the soybean futures, there is a one-lag price transmission across markets, while no volatility spillover has been detected. As for the wheat futures, no information transmission is found across markets.

How Information Is Transmitted Across the Nations? An Empirical Investigation of the US and Chinese Commodity Markets

The so-called “foreign exchange rate determination puzzle” has been a hard topic in international finance for several decades. The puzzle illustrates the weak explanatory power of macroeconomic-based models of the nominal exchange rate fluctuations. We investigate the foreign exchange rate determination puzzle in a continuous-time framework. Following the market microstructure literature, a simple model of the determination of foreign exchange rates is developed, and the model concludes a result which is essentially a continuous-time version of the equation in Evans and Lyons (2002a). For estimation, we take an advantage of a newly-developed econometric tool based on a time change from calendar to volatility time. With this new estimation methodology, our results indicate that the effect of order flow on exchange rate is significantly improved compared with the traditional econometric tools. Keywords: Order Flow; Time-Change Sampling; Martingale Estimator JEL Classifications : C22; G15; G17

/pdf/order-flow-and-exchange-rate-dynamics-in-continuous-time-new-5c8ahnwftf.pdf

Order Flow and Exchange Rate Dynamics in Continuous Time: New Evidence from Martingale Regression

The purpose of this study was to investigate the behavior of various indices of Tehran Stock Exchange firstly in the boom period from 2018-03-21 to 2018-11-02 and secondly in the recession period f...

https://www.econstor.eu/bitstream/10419/245258/1/10.1080-23322039.2019.1630931.pdf

Double-sided balanced conditional Sharpe ratio

The world palladium demand has increased steadily and dramatically in the years of 21th century. However, its spot price still has not reached the peak level observed in January 17, 2001. In 2008, a single-day increase of the palladium spot price has exceeded 37%, which witnesses significant risk for investments in the world palladium market. In this paper, we apply the GARCH model with heavy-tailed distributions into the palladium spot returns series for risk management purpose. We compare empirical performance of the Student’s t distribution and the normal reciprocal inverse Gaussian (NRIG) distribution. Our results show the newlydeveloped distribution, the NRIG, cannot outperform the older fashion one, the Student’s t distribution. Nevertheless, our results do demonstrate that it is important to incorporate conditional heavy tails for precious metals’ spot return modelling.

https://hrmars.com/papers_submitted/3131/Conditional_Heavy_Tails,_Volatility_Clustering_and_Asset_Prices_of_the_Precious_Metal.pdf

Conditional Heavy Tails, Volatility Clustering and Asset Prices of the Precious Metal

We adopt Schwartz and Smith’s model (2000) to calculate risk measures of Brent oil futures contracts and light sweet crude oil (WTI) futures contracts and Mirantes, Poblacion and Serna’s model (2012) to calculate risk measures of natural gas futures contracts, gasoil futures contracts, heating oil futures contracts, RBOB gasoline futures contracts, PJM western hub peak and off-peak electricity futures contracts. We show that the models present well goodness of fit and explain two stylized facts of the data: the Samuelson effect and the seasonality effect. Our backtesting results demonstrate that the models provide satisfactory risk measures for listed energy commodity futures contracts. A simple estimation method possessing quick convergence is developed.

Zi-Yi Guo

Papers

Models with Short-Term Variations and Long-Term Dynamics in Risk Management of Commodity Derivatives

Models with Short-Term Variations and Long-Term Dynamics in Risk Management of Commodity Derivatives