New Methods for Market Risk Assessment in Financial Econometrics

• Main Authors: Jin-Huei J. Yeh, 葉錦徽
• Other Authors: Chung-Ming Kuan
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/09395673754257909860

博士 === 國立臺灣大學 === 經濟學研究所 === 93 === Financial risk management is a core field in finance in which risk assessment plays a key role. Risk measures have been widely applied to pricing, hedging, portfolio optimization, capital allocation and performance evaluation. Over decades, the measurement of risks has long been a primary concern for both practitioners and regulators in the sector of financial industries. This dissertation considers three new econometric recipes for assessing systematic market risks, including high frequency market beta, realized volatility, and tail risks for financial assets. The first two chapters focus on market microstructure effects on estimating market beta and constructing realized covariance matrix based on high frequency data. It is well known that returns sampled at high frequency are contaminated by market frictions, such as nontrade, bid-ask bounce, among others. When considering a basket of assets, asynchronous financial return series due to non-synchronous trading complicate or bias many tasks of financial management. Profit and losses can be biased and the hedging strategy may be distorted. Hence, using non-synchronous returns is likely to result in systematic errors. To formally address this issue, in Chapter 1, we investigate the effect of Stochastic Temporal Aggregation (STA) of time series that exhibits non-synchronous trading. For various time series models, we observe that stochastic temporal aggregation will mask the dependence structure or the uncorrelated nature of the original process. We thus propose to resolve the non-synchronous trading problem by recovering the virtual return processes via the Markov chain Monte Carlo method utilizing information hinged upon the stochastically aggregated returns. We consider estimating high-frequency beta among several asynchronously traded asset returns. Theoretical results on auto-covariance and cross covariances derived for non-synchronously traded returns show that arbitrarily computed beta without considering the non-synchronous trading effect seriously underestimate the true beta. Applying the proposed recovering method to some mid- or small-cap NYSE equities in TAQ data, we found the empirical results are in line with our theoretical results. High frequency beta also differs substantially from low frequency estimates like daily beta. Furthermore, the recovering procedure can correct up to $60\%$ biases in beta due to non-synchronous trading, depending on the informational content of the observed returns. Hence overlooking non-synchronous trading bias can be dangerous. Realized volatility, summation over finely sampled intraday squared returns to approximate the latent time-varying daily volatility, has been justified by standard continuous time arguments. Nonetheless, so far the construction of realized measures for both volatility and correlation are restricted to those actively traded financial assets. The second chapter aims at constructing a bias-free realized covariance matrix among a group of assets including both liquid and illiquid equities using high frequency data, either to monitor the performance of a chosen portfolio or for strategic asset allocation in timing the market. The difficulty on how to deal with a set of non-synchronously traded intra-daily returns while at the same time correcting for the other market microstructure noises can be solved through the proposed synchronizing procedure in Chapter 1. We use the synchronized set of recovered return values to construct realized volatility and correlations. Our empirical results show the proposed method is effective in correcting for both non-synchronous trading and other market friction effects. We also found that non-synchronous trading bias plays a dominant role among the other market microstructure effects for less actively traded equities. After controlling the effect from non-synchronous trading, the effect from the other microstructure is evident but negligible in the context of realized measures construction. The new recovering and filtering procedures allow one to get more precise realized measures for volatilities and correlations that are applicable to further financial applications. Tail risks plays a prominent role in risk management. In the third chapter, we propose a new measure for Value at Risk based on expectile, EVaR, to properly account for the potential risk in the extreme tail, complementary to the commonly used quantile-based VaR (QVaR). Obtained from minimizing an asymmetrically weighted mean quadratic variations, EVaR is capable of incorporating the tail shape information, such as influential extreme risks. The statistical properties of expectile and its relationship with quantile are discussed. To allow for practical considerations in tail risk management, Conditional EVaR via a Conditional AutoRegressive Expectile (CARE) model is proposed and estimated by the method of asymmetric least squares. We generalize the results of Newey and Powell (1987) to encompass the class of weakly dependent processes. The empirical studies are conducted for six foreign exchange rate returns. Our results show that EVaR through CARE does outperform QVaR both in-sample and out-of sample in terms of rate of exceedance for exchange rates that experienced crises, such as Mexico Peso and Thai Baht. Moreover, combining the information from both measures further reduces the rate of exceedance both in-sample and out-of-sample.