Realized Volatility Risk

In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.

Modelling and Forecasting Dynamic VaR Thresholds for Risk Management and Regulation

The paper presents methods of estimating Value-at-Risk (VaR) thresholds utilising two calibrated models and three conditional volatility or GARCH models. These are used to estimate and forecast the VaR thresholds of an equally-weighted portfolio, comprising: the S & P500, CAC40, FTSE100 a Swiss market index (SMI). On the basis of the number of (non-)violations of the Basel Accord thresholds, the best performing model is PS-GARCH, followed by VARMA-AGARCH, then Portfolio-GARCH and the RiskmetricsTM -EWMA models, both of which would attract a penalty of 0.5. The worst forecasts are obtained from the standard normal method based on historical variances.Value at Risk (VaR) modelling, forecasting risk thresholds, Portfolio Spillover-Garch, risk management and regulation Acknowledgements: The authors wish to thank Felix Chan, Suhejla Hoti, Alex Zsimayer and seminar participants at the Institute of Economics, Academia Sinica, Taiwan, Ling Tung Institute of Technology, Griffith University, Queensland University of Technology, and University of Queensland for helpful comments and suggestions. The first and second authors wish to thank the Australian Research Council for financial support. The third author wishes to acknowledge a University Postgraduate Award and an International Postgraduate Research Scholarship at the University of Western Australia.

Volatility Spillovers from the Chinese Stock Market to Economic Neighbours

This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China’s increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential effects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA-GARCH, and multivariate VARMA-AGARCH models are used to test for constant conditional correlations and volatility spillover effects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacific Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover effects from China to related markets during the GFC. This is presumably because the GFC was initially a US phenomenon, before spreading to developed markets around the globe, so that it was not a Chinese phenomenon.Volatility spillovers;VARMA-GARCH; VARMA-AGARCH; Chinese stock market

"Realized Volatility Risk"

In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.

Realized Volatility Risk

In this paper we document that realized variation measures constructed from high- frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.Realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity

A Capital Adequacy Buffer Model

In this paper, we develop a new capital adequacy buffer model (CABM) which is sensitive to dynamic economic circumstances. The model, which measures additional bank capital required to compensate for fluctuating credit risk, is a novel combination of the Merton structural model which measures distance to default and the timeless capital asset pricing model (CAPM) which measures additional returns to compensate for additional share price risk

Comparison of Alternative ACD Models via Density and Interval Forecasts: Evidence from the Australian Stock Market

In this paper a number of alternative ACD models are compared using a sample of data for three major companies traded on the Australian Stock Exchange. The comparison is performed by employing the methodology for evaluating density and interval forecasts, developed by Diebold, Gunther and Tay (1998) and Christoffersen (1998), respectively. Our main finding is that the generalized gamma and log-normal distributions for the error terms have similar performance and perform better that the exponential and Weibull distributions. Additionally, there seems to be no substantial difference between the standard ACD specification of Engle and Russel (1998) and the log-ACD specification of Bauwens and Giot (2000).ACD models, Density forecasts Acknowledgements: This paper forms part of an ARC Linkage Grant research project, ÃModelling stock market liquidity in Australia and the Asia Pacific RegionÓ. We are grateful to the Australian Research Council for financial support. The financial data has been graciously provided by the Securities Research Institute (SIRCA) which is our industry partner.

Finite Sample Properties of the QMLE for the Log-ACD Model: Application to Australian Stocks

This paper is concerned with the finite sample properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Although the distribution of the QMLE for the log-ACD model is unknown, it is an important issue as it is used widely for testing various market microstructure models and effects. Knowledge of the distribution of the QMLE is crucial for purposes of valid inference and diagnostic checking. This paper investigates the structural and statistical properties of the log-ACD model by establishing the relationship between the log-ACD model and the Autoregressive-Moving Average (ARMA) model. The theoretical results developed in the paper are evaluated using Monte Carlo experiments. The experimental results also provide insights into the finite sample properties of the log-ACD model under different distributional assumptions.Conditional duration, Asymmetry, ACD, Log-ACD, Monte Carlo simulation Acknowledgement: The authors are grateful for the financial support of the Australian Research Council.

President Trump tweets supreme leader Kim Jong-Un on nuclear weapons: A comparison with climate change

A set of 125 tweets about North Korea\u27s Supreme Leader Kim Jong-Un by President Trump from 2013 to 2018 are analysed by means of the data mining technique, sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their implications about President Trump\u27s understanding and approach to international diplomacy. The results suggest a predominantly positive emotion in relation to tweets about North Korea, despite the use of questionable nicknames such as Little Rocket Man . A comparison is made between the tweets on North Korea and climate change, madefrom 2011-2015, as Trump has tweeted many times on both issues. It is interesting to find that Trump\u27s tweets on North Korea have significantly higher positive polarity scores than his tweets on climate change

A nonlinear autoregressive distributed lag (NARDL) analysis of west texas intermediate oil prices and the DOW JONES index

© 2020 by the authors. The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthlyWest Texas Intermediate (WTI) crude oil series from Federal Reserve Bank of St Louis (FRED), commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect