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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 inpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the 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.

The drivers of mining stock prices are known to be several. Sharp spikes on the stocks return distribution have been linked to the presence of unusually high volatility signifying the presence of high levels of kurtosis. The accurate measurement of the stocks’ underlying co-movements for more accurate CVaR portfolio optimization requires, therefore, the utilization of sophisticated and specific-specialized techniques which could adequately capture and model these characteristics. Here this issue is addressed by applying statistical-graphical models for dependence estimation. Twenty mining stocks, out of the 801 listed in the ASX as of December 2012, have been selected for the analysis under the criteria of satisfying the eight years trading period sought, having very weak or no autocorrelation of residuals and displaying the highest kurtosis. Models’ estimations of dependence are compared and inserted into a differential evolution algorithm for non-convex global optimization in order to conduct risk controlled CVaR portfolio optimization (Ardia, Boudt, Carl, Mullen & Peterson, 2011) and be able to identify the one yielding the highest portfolio return. The findings are of relevance in portfolio allocation and portfolio risk management. Energy and mining stock markets are subjected to numerous price drivers holding complex relationships. The dynamics of production and consumption based on seasonality features, transportation and storage, weather conditions, commodity price fluctuations, currency changes, market confidence and expectations, trading speculations and the domestic and international states of the economy impact mining stock prices in particular and unobvious ways reflected in high volatility with sudden spikes in the stock’s return distribution (Pilipovic, 1998). The generation of accurate measurements of the dependence matrix of mining stock’s return series is therefore both, a non-trivial task due to the hard to decipher characteristics present in retur

The huge volatility experienced by equities markets during the Global Financial Crisis (GFC) underlined the importance of understanding market risk in extreme economic conditions. Whilst the Australian economy is widely considered to have fared better than many of its global counterparts during the GFC, there was nonetheless extreme volatility experienced in Australian financial markets. To understand the extent to which emerging Australian entities were impacted by these extreme events as compared to established entities, this paper compares entities comprising the Emerging Markets Index (EMCOX) to established entities comprising the S&P/ASX 200 Index using four risk metrics. The first two are Value at Risk (VaR) and Distance to Default (DD) which are traditional measures of market and credit risk. The other two focus on extreme risk in the tail of the distribution and include Conditional Value at Risk (CVaR) and Conditional Distance to Default (CDD), the latter metric being unique to the authors and which applies CVaR techniques to default measurement. We apply these measures both prior to and during the GFC, including an analysis of high, medium and low risk quantiles and find that Emerging Market shares show higher risk for all metrics used, the spread between the emerging and established portfolios narrows during the GFC period and that the default risk spread between the two portfolios is greatest in the tail of the distribution. This information can be important to both investors and lenders in determining share or loan portfolio mix in extreme economic circumstances.

The aim of this study is to examine the determinants of the capital structure of Thai banks. The data spans a ten year period from 1999 – 2008. The differentiation point of this study is that, whereas most studies on capital structure focus predominantly on internal bank variables, this study, in addition to internal variables includes market-based risk variables. A range of market-based default and value at risk variables were considered which were then narrowed down to improve the model. Fixed effects panel data analysis is employed, with both market and book leverage used as dependent variables. The Thai bank study is benchmarked to Japanese and Malaysian banks. The study found that the significance of the market-based variables is greater for market leverage than for book leverage. The results can assist regulators and banks in understanding the impact of different market and internal variables on capital.

This article examines the contribution of foreign investors to price discovery by applying a vector error correction model (VECM) to 30 frequently-traded stocks on the Indonesia Stock Exchange. We use the Lee and Ready (1991) rule to determine the initiator of a trade and compute the volume weighted average price (VWAP) for every 30 min interval during the period 2004–2006. There are three key findings. First, foreign investors are more aggressive than local investors in placing their orders yet foreigners do not trade as often as locals. Second, the profits of foreign investors are smaller than local investors. Third, foreign investors need to adjust their trade price at a higher magnitude to achieve price equilibrium, which indicates that the majority of price discovery occurs among local investors in the Indonesian market.

This study provides an overview of the characteristics of stockholdings of foreign and local investors in terms of firm sizes, price levels and liquidity. There are four key findings. First, the IDX is a highly concentrated market and foreign investors dominate the ownership of high market capitalization stocks. Second, foreign investors trade less frequently than domestic counterparts. Third, small, illiquid lower priced stocks dominate this market with domestic individual investors holding most of the stocks with these characteristics. Finally, the paper profits of foreign institutional and domestic individual investors are found to be higher than those of domestic institutional investors.

Traditionally, ordinary least square (OLS) regression methods are used to test asset pricing models. This study focuses on the use of quantile regression as an alternative approach to the analysis of risk and return distributions in quantitative finance. It empirically examines the behaviour of two widely used asset pricing factors, beta and book to market ratios, but the focus is on minimising absolute deviations around the median rather than minimising squared deviations around the mean of their distributions, as we apply quantile regressions as opposed to OLS. We show how OLS is less able to capture the extreme values or the adverse losses in the return distribution, which on the other hand are captured by quantile regressions. The study not only shows that the factors do not necessarily follow a linear relationship but also shows that the traditional use of OLS becomes less effective when it comes to analysing the extremes within a distribution, which are often a source of keen interest for investors and risk managers.

On the afternoon of May 6, 2010 Dow Jones Industrial Average (DJIA) plunged about 1000 points (about 9%) in a matter of minutes before rebounding almost as quickly. This was the biggest one day point decline on an intraday basis in the DJIA’s history. An almost similar dramatic change in intraday volatility was observed on April 4, 2000 when DJIA dropped by 4.8%. These historical events present very compelling argument for the need of robust econometrics models which can forecast intraday asset volatility. There are numerous models available in the finance literature to model financial asset volatility. Various Autoregressive Conditional Heteroskedastic (ARCH) time series models are widely used for modelling daily (end of day) volatility of the financial assets. The family of basic GARCH models work well for modelling daily volatility but they are proven to be not as efficient for intraday volatility. The last two decades has seen some research augmenting the GARCH family of models to forecast intraday volatility, the Multiplicative Component GARCH (MCGARCH) model of Engle & Sokalska (2012) is the most recent of them. MCGARCH models the conditional variance as the multiplicative product of daily, diurnal, and stochastic intraday volatility of the financial asset. In this paper we use MCGARCH model to forecast intraday volatility of Australia’s S&P/ASX-50 stock market, we also use the model to forecast the intraday Value at Risk. As the model requires a daily volatility component, we test a GARCH based estimate and a Realized Variance based estimate of daily volatility component.

The Indonesian market is a critical market to the South East Asian region, being that region’s largest economy. The primary sectors of the Indonesian economy, incorporating Agriculture and Mining, are of critical importance to the country, representing approximately one quarter of GDP and providing nearly 40% of the nation’s employment. Mining and Agriculture stock returns significantly outperformed the Indonesian Stock Exchange (IDX) composite index in the five years leading up to Global Financial Crisis (GFC), and experienced savage falls during the GFC. Against this background, we examine the market and credit risk of these sectors during the pre-GFC, GFC and post-GFC periods. Market risk is measured using Value at Risk (VaR) and Conditional Value at Risk (CVaR). VaR is a popular metric which measures potential losses over a specific time period, up to a selected threshold. A key downside of this metric is that it says nothing of the extreme risk beyond VaR, which is a major limitation for this study, given the extreme volatility experienced by the primary sectors in Indonesia over the studied period. We therefore also use CVaR, which measures the extreme risk beyond VaR. For credit risk, we use the Merton-KMV Distance to Default (DD) metric, as well as our own Conditional DD (CDD) metric to measure extreme default risk. The key advantage that the Merton-KMV model has over other credit models, it that it incorporates fluctuating asset values. This makes it more responsive to changes in market conditions than most other credit models which remain static between rating periods. The importance of fluctuating asset values in measuring credit risk has been raised by the Bank of England (2008), who make makes the point that not only do asset values fall in times of uncertainty, but rising probabilities of default make it more likely that assets will have to be liquidated at market values. Similar to VaR, the Merton-KMV model has deficiencies in that it uses the standar

This paper features the application of a novel and recently developed method of statistical and mathematical analysis to the assessment of financial risk, namely regular vine copulas. Dependence modelling using copulas is a popular tool in financial applications but is usually applied to pairs of securities. Vine copulas offer greater flexibility and permit the modelling of complex dependence patterns using the rich variety of bivariate copulas that can be arranged and analysed in a tree structure to facilitate the analysis of multiple dependencies. We apply regular vine copula analysis to a sample of stocks comprising the Dow Jones index to assess their interdependencies and to assess how their correlations change in different economic circumstances using three different sample periods around Global Financial Crisis (GFC).: pre-GFC (January 2005 to July 2007), GFC (July 2007 to September 2009) and post-GFC periods (September 2009 to December 2011). The empirical results suggest that the dependencies change in a complex manner, and there is evidence of greater reliance on the Student-t copula in the copula choice within the tree structures for the GFC period, which is consistent with the existence of larger tails in the distributions of returns for this period. One of the attractions of this approach to risk modelling is the flexibility in the choice of distributions used to model co-dependencies. The practical application of regular vine metrics is demonstrated via an example of the calculation of the Value at Risk of a portfolio of stocks.

The Global Financial Crisis (GFC) provided overwhelming evidence of the problems caused by inadequate credit ratings. Losses and problem loans experienced by banks over this period were staggering. Yet many of the securitized sub-prime parcels which were widely seen as an underlying cause of the GFC, as well as corporate obligors who experienced severe difficulties during the GFC, retained extremely strong external credit ratings. They may have had low perceived risk at the time of rating, but as circumstances changed, the ratings stayed static and became far removed from the underlying risk. A key problem is that the external credit ratings do not fluctuate with changing economic circumstances. Whilst there are models which measure changing default risk, they are not linked to credit ratings and it is often the rating itself, not the underlying risk that drives behavior, such as the purchase of securitized parcels, the pricing of credit risk, and the allocation of capital for credit risk, which under the Basel standardized model for corporates is based on the rating itself. This problem is exacerbated by the fact that these ratings carry descriptors such as “extremely strong capacity”. This descriptor may no longer be appropriate for the rated company if the market turns dramatically, yet the rating and descriptor remain unchanged. To overcome this problem, this paper shows how an innovative fluctuating credit ratings model can be generated by linking the Merton structural credit model to a credit ratings framework. The Merton model measures fluctuations in daily asset values and, using a combination of these fluctuating asset values and the capital structure of a company, it measures Distance to Default (DD) and the Probability of Default (PD) associated with each DD. Under the Merton structural model, default occurs when the firm’s debt exceeds asset values. Thus as fluctuations in asset values become more volatile, DD also becomes more volatile and PD increases.

Given the growing number of stocks and other financial instruments available in the investment market, there is always a need for quick and efficient methods of asset selection for investment purposes. The Fama–French three-factor model has the allure of simplifying asset selection by narrowing the number of parameters required to assess risk, but the usual technique of ordinary least squares regression (OLS) used for the estimation of the coefficients or sensitivity to the three factors suffers from the problem of modeling the conditional mean of the distribution inherent to OLS. In this chapter, we use the technique of data envelopment analysis applied to the Fama–French three-factor model to choose stocks from the Dow Jones Industrial Index. We also apply the more robust technique of quantile regression to estimate the coefficients for the factor model and show that the assets selected using this regression method lead to the construction of superior portfolios with higher returns in equally weighted portfolios when contrasted with the outcomes from OLS

Innovative transition matrix techniques are used to compare extreme credit risk for Australian and US companies both prior to and during the global financial crisis (GFC). Transition matrix methodology is traditionally used to measure Value at Risk (VaR), a measure of risk below a specified threshold. We use it to measure Conditional Value at Risk (CVaR) which is the risk beyond VaR. We find significant differences in VaR and CVaR measurements in both the US and the Australian markets. We also find a greater differential between VaR and CVaR for the US as compared to Australia, reflecting the more extreme credit risk that was experienced in the US during the GFC. Traditional transition matrix methodology assumes that all borrowers of the same credit rating transition equally, whereas we incorporate an adjustment based on industry share price fluctuations to allow for unequal transition among industries. Our revised model shows greater change between Pre-GFC and GFC total credit risk than the traditional model, meaning that those industries that were riskiest during the GFC are not the same industries that were riskiest Pre-GFC. Overall, our analysis finds that our innovative modelling techniques are better able to account for the impact of extreme risk circumstances and industry composition than traditional transition matrix techniques.

The phenomenon of the occurrence of rare yet extreme events, "Black Swans" in Taleb's terminology, seems to be more apparent in financial markets around the globe. This means there is not only a need to design proper risk modelling techniques which can predict the probability of risky events in normal market conditions but also a requirement for tools which can assess the probabilities of rare financial events; like the recent global financial crisis (2007-2008). An obvious candidate, when dealing with extreme financial events and the quantification of extreme market risk is extreme value theory (EVT). This proves to be a natural statistical modelling technique of relevance. Extreme value theory provides well-established statistical models for the computation of extreme risk measures like the return level, value at risk and expected shortfall. In this paper we apply univariate extreme value theory to model extreme market risk for the ASX-All Ordinaries (Australian) index and the S&P-500 (USA) Index. We demonstrate that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or expected shortfall (ES) and expected return level and also daily VaR using a GARCH(1,1) and EVT based dynamic approach.

The papers in this special issue of Mathematics and Computers in Simulation are substantially revised versions of the papers that were presented at the 2011 Madrid International Conference on "Risk Modelling and Management" (RMM2011). The papers cover the following topics: currency hedging strategies using dynamic multivariate GARCH, risk management of risk under the Basel Accord: A Bayesian approach to forecasting value-at-risk of VIX futures, fast clustering of GARCH processes via Gaussian mixture models, GFC-robust risk management under the Basel Accord using extreme value methodologies, volatility spillovers from the Chinese stock market to economic neighbours, a detailed comparison of Value-at-Risk estimates, the dynamics of BRICS's country risk ratings and domestic stock markets, U.S. stock market and oil price, forecasting value-at-risk with a duration-based POT method, and extreme market risk and extreme value theory.

Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.

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. We undertook some additional analysis for this period featuring an exploration of whether there was any spillover effect in the mean equations as well as in the variance equations. We used a bimean equation to model the conditional mean in the individual markets plus an ARMA model to capture volatility spillovers from China to the five markets considered. This augmented model showed much greater evidence of spillovers. We also suspected that the correlations were not constant and applied a moving window of 120 days of daily observations to explore time-varying conditional and fitted correlations. There was evidence of non-constant correlations and even a period of ne

This paper investigates the Estimating Function method in the context of ACD modelling and appraises the properties of these estimates. A simulation study is conducted to demonstrate that these estimates are more efficient than the corresponding ML and QML estimates.

This paper examines credit risk in the European automotive industry. Distance to Default (DD) is calculated using the Merton structural credit model. In addition, we modify the Merton model to generate an innovative measure of credit risk at the extremes of the asset value fluctuations distribution, which we call Conditional Distance to Default (CDD). The credit risk of all listed automotive stocks on the S&P Euro Index is compared to all the other industries on this index, which comprises 180 stocks with geographic and sectoral diversity. The study spans the 10 years from 2000 to 2009 divided into pre-GFC and GFC periods. Our metrics find the automotive industry to be of high risk relative to other European industries, particularly during the GFC. We also find that our CDD metric is better able to capture the extreme credit risk prevalent in the industry during the GFC than traditional DD metrics.