There are several methods to predict financial crises. There are also several types of indicators used by financial institutions. These indicators, which are estimated in different ways, often show various developments, although it is not possible to directly assess which is the most suitable. Here, we still try to find what characteristics that industry group has and forecast financial crises In this paper, our data started from monthly of 1977 January to 2008 December in S&P100. We consider Fama-French and Cluster Analysis to process data to make data with same characteristic within a group. Then, we use GARCH type models and apply it to VaR predicting stock turmoil. In conclusion, we found that the group which has high kurtosis value is the key factor for predicting stock crises instead of volatility. Moreover, the characteristics of this industry which can predict stock crises is a great scale. On the other hand, we can through this model to double check the reaction for anticipating. Therefore, people can do some actions to control risk to reduce the loss.

This dissertation consists of two chapters that examine the construction of financial market volatility indexes and their forecasting efficiency across predictive regression models. Each of the chapter is devoted to diferent volatility measures which are related and evaluated in thframework of forecasting regressions. The first chapter studies the sampling and liquidity issues in constructing volatility indexes, VIX and VXO, in emerging options market like Taiwan. VXO and VIX have been widely used to measure the 22-day forward volatility of the market. However, for an emerging market, VXO and VIX are difficult to measure with accuracy when tradings of the second and next to second nearby options are illiquid. The chapter proposes four methods to sample the option prices across liquidity proxies ¡V five different days of rollover rules ¡V for option trades to construct volatility index series. The paper finds that, based on the sampling method of the average of all midpoints of bid and ask quote option prices, the volatility indexes constructed by minute-tick data have less missing data and more efficient in volatility forecast than the method suggested by CBOE. Additionally, illiquidity in emerging options market does not, based on different rollover rules, lead to substantial biases in the forecasting effectiveness of the volatility indexes.Finally, the forecasting ability of VIX, in terms of naive forecasts and forecasting regressions, is superior to VXO in Taiwan. The second chapter uses high-frequency intraday volatility as a benchmark to measure the efficacy of model-free and model-based econometric models. The realized volatility computed from intraday data has been widely regarded as a more accurate proxy for market volatility than squared daily returns. The chapter adopts several time series models to assess the fore-casting efficiency of future realized volatility in Taiwan stock market. The paper finds that, for 1-day directional accuracy forecast performance, semiparametric fractional autoregressive model (SEMIFAR, Beran and Ocker, 2001) ranks highest with 78.52% hit accuracy, followed by multiplicative error model (MEM, Engle, 2002), and augmented GJR-GARCH model. For 1-day forecasting errors evaluated by root mean squared errors (RMSE), GJR-GARCH model augmented with high-low range volatility ranks highest, followed by SEMIFAR and MEM model, both of which, however, outperform augmented GJR-GARCH by the measure of mean absolute value (MAE) and p-statistics (Blair et al., 2001).

Inline with the third pillar of the Basel accords, quantitative market risk measurements are investigate and evaluated comparing JP Morgan’s RiskMetrics and Bollerslev’s GARCH with the Peek over Threshold and Block Maxima approaches from the Extreme Value Theory framework. Value-at-Risk and Expected Shortfall (Conditional Value-at-Risk), with 95% and 99% confidence, is predicted for 25 years of the OMXS30. The study finds Bollerslev’s suggested t distribution to be a more appropriate distributional assumption, but no evidence to prefer the GARCH to the RiskMetrics. The more demanding Extreme Value Theory procedures trail behind as they are found wasteful of data and more difficult to backtest and therefore evaluate.

Darbe nagrinėjamas akcijų portfelio rizikuojamosios vertės nustatymas dispersijos-kovariacijos metodu taikant daugiamačius sąlyginio heteroskedastiškumo modelius. Naudojant Baltijos šalių akcijų rinkos duomenis yra įvertinami du daugiamačiai sąlyginio heteroskedastiškumo modeliai – DCC ir O-GARCH. Remiantis šių modelių rezultatais, apskaičiuojamos portfelių, sudarytų iš dešimties Baltijos šalių bendrovių akcijų, rizikuojamosios vertės, taikant dispersijos-kovariacijos metodą. Siekiant gauti tikslesnius rizikuojamosios vertės įverčius, dispersijos-kovariacijos metodas yra modifikuojamas, vietoje standartinio normaliojo atsitiktinio dydžio kvantilio imant skirstinių iš apibendrintų hiperbolinių skirstinių šeimos kvantilius. Parodoma, jog apibendrintų hiperbolinių skirstinių šeimos taikymas žymiai pagerina rizikuojamosios vertės vertinimo tikslumą. Todėl siūloma apibendrintų hiperbolinių skirstinių šeimą taikyti praktikoje vietoje normaliojo skirstinio. / The thesis examines the variance-covariance approach to the estimation of portfolio Value-at-Risk using multivariate GARCH models. Two multivariate GARCH models, DCC and O-GARCH, are estimated using Baltic stock market data. Based on the results of these models, Value-at-Risk of randomly generated portfolios is calculated using the variance-covariance approach. This approach was improved by taking quantiles of generalized hyperbolic distributions instead of standard normal ones. The analysis suggests that the use of generalized hyperbolic distribution considerably improves the accuracy of Value-at-Risk estimates. Therefore, it is proposed to use the family of generalized hyperbolic distributions in practice.

A growing concern among investors for social responsibility in relation to the business world and its effect on the environment, society, and government has increased and therefore different types of stock indices and funds that incorporate socially responsible ideals have been developed. However, a literature review revealed that there does not seem to be much information about the volatility of Green Funds or Socially Responsible Investments (SRI). Volatility is an important part of understanding the financial markets and is used by many to understand asset allocation, risk management, option pricing and many other functions. Therefore, the purpose of this thesis is to investigate the volatility performance of SRIs, REFs and Conventional Indices by using different models CAPM, SR, JA and EGARCH, and monthly and daily data from the US, UK, Japan and Eurozone financial markets to compare results.   This thesis has been conducted by following an objective ontological and positivist epistemological position, because the data used for analysis in this thesis is independent from the author and has studied what actually exists, not what the author seeks to interpret. The research approach is functionalist, because this thesis sought to explain how the investments function in relation to volatility comparisons in different financial markets and if this volatility can be predicted through a framework of rules designed by previous researchers. The design is a deductive study of quantitative, longitudinal, secondary data, because hypotheses are derived from theory to test the volatility of time series data between the year 2007 and 2012 through empirical evidence.   Statistical evidence was found to suggest that the EGARCH model for volatility measurement is the best fit to model volatility and daily data can give more information and better consistency between results. SRIs were found to be less volatile than CIs in all financial markets; REFs were found more volatile than CIs in the US and Eurozone markets but not in the UK and Japan markets; REFs were found to be more volatile than SRIs in all markets except the UK; REFs were also found to be more volatile than SRIs and CIs during a recession in all markets except the UK. Evidence also indicated that the correlations between REFs and SRIs in the US and Eurozone were significant, but not significant in the UK and Japan market samples. The correlations were low between the UK and Japan SRIs, Japan and Eurozone SRIs and Japan SRI and Eurozone REF, which suggest that an investor may consider to diversify between these investments. However, all other statistically significant correlations between financial markets were high and could consequentially deliver poor long term investment performance.

In this thesis we use the GARCH(1,1) and GJR-GARCH(1,1) models to estimate the conditional variance for five equities from the OMX Nasdaq Stockholm (OMXS) stock exchange. We predict 95% and 99% Value-at-Risk (VaR) using one-day ahead forecasts, under three different error distribution assumptions, the Normal, Student’s t and the General Error Distribution. A 500 observations rolling forecast-window is used on the dataset of daily returns from 2007 to 2014. The empirical size VaR is evaluated using the Kupiec’s test of unconditional coverage and Christoffersen’s test of independence in order to provide the most statistically fit model. The results are ultimately filtered to correspond with the Basel (II) Accord Penalty Zones to present the preferred models. The study finds that the GARCH(1,1) is the preferred model when predicting the 99% VaR under varying distribution assumptions.

There is a large body of research that indicates that international equity markets co-move over time. This co-movement manifests in various instruments, ranging from equities and bonds to soft commodities. However, this co-movement is more prevalent over crisis periods and can be seen in returns and volatility transmission effects. The recent financial crisis demonstrated that no local market is immune to transmission effects from international markets. South African financial market participants, such as investors and policymakers, have a vested interest in understanding how the equity market in particular and the economy in general react to international financial crises. This study aims to contribute an improved understanding of how the South African equity market interacts with international equity markets, by identifying the degree of price and volatility transmission before, during, and after an international financial crisis. This was done by investigating the possibility of changes in price and volatility transmissions from the Asian financial crisis (1997–1998), the dotcom bubble (2000–2001) and the more recent subprime financial crisis (2007–2009). An Exponential Generalized Autoregressive Conditional Heteroskedasticity (E-GARCH) model was employed within the framework of an Aggregate Shock model. The results indicate that during the international financial crises studied, the JSE All Share Index was directly affected through contagion effects inherent in the returns of the originating crisis country. Volatility transmissions during international financial crises came directly from the originating crisis country. Finally, the FTSE 100 Index was the main exporter of price and volatility transmission to the JSE All Share Index. / Thesis (M.Com. (Risk management))--North-West University, Potchefstroom Campus, 2012

This study attempts to quantify the shocks to a banking network and analyze the transfer of shocks through the network. We consider two sources of shocks: external shocks due to market and macroeconomic factors which impact the entire banking system, and idiosyncratic shocks due to failure of a single bank. The external shocks will be estimated by using two methods: (i) non-parametric simulation of the time series of shocks that occurred to the banking system in the past, and (ii) using the extreme value theory (EVT) to model the tail part of the shocks. The external shocks we considered in this study are due to exchange rate and treasury bill rate volatility. Also, an ARMA/GARCH model is used to extract iid residuals for this purpose. In the next step, the probability of the failure of banks in the system is studied by using Monte Carlo simulation. We calibrate the model such that the network resembles the Canadian banking system.

資產報酬的波動程度隱含風險與不確定性，不同的投資者存在不同之風險偏好與風險承受能力，因此瞭解報酬波動之特性有其必要性；又鑑於過去不動產市場對於商用與住宅不動產兩次市場之相關研究較欠缺。因此本研究擬分別探討商用與住宅不動產市場報酬波動特性與差異，並檢視其風險與報酬間之關係。此外，總體經濟環境之變動會影響不動產市場供需關係，進而影響其價格與報酬之波動，因此本研究最後再進一步討論影響其市場報酬之總體經濟因素。 為捕捉不動產市場報酬之波動特性，本研究擬透過GARCH模型分別檢驗商用與住宅不動產市場報酬波動特性與差異；進而應用GARCH-M模型，探討商用與住宅不動產市場報酬與風險之關係；最後透過落遲分配模型實證比較分析顯著影響商用與住宅不動產市場報酬之總體經濟因素。樣本取自台北地區，資料期間為1997年2月至2009年3月之月資料。 實證結果顯示，商用不動產市場中投資人較容易透過自身過去的報酬波動推測未來的波動，反觀住宅不動產市場部分，投資人則傾向注意整體市場消息的散佈，因為其較容易受到外在因素影響而導致報酬波動；由GARCH-M模型實證結果顯示，住宅與商用不動產市場報酬與風險間均呈現顯著正相關，顯示其市場波動風險增加時期，會提供更高之報酬以均衡投資者所面對之較高市場波動風險；由落遲分配模型實證結果顯示，商用與住宅不動產市場報酬確實和總經變數之間有著程度不同的關聯性，所有當期總經變數與不動產報酬間均不存在顯著影響關係，顯示各總經變數對不動產報酬的影響存在時間落差。此外，總經變數對商用報酬的影響程度相對大於對住宅報酬的影響，且總體經濟環境變動對於商用不動產市場報酬之衝擊相對較為迅速。 / This research plans to study the relative volatility characteristic of commercial and residential property returns. In addition, the changing real estate environment can be linked to the macro economy, so we further discusses the relationship between property returns and the macro economy. In order to catch the volatility characteristic of real estate returns, we use GARCH model to examine the volatile behavior of real estate returns of commercial and residential property in the Taipei area during the period of February 1997 to March 2009, and because risk is time-varying in the market, we continue to employ GARCH-M model to observe whether can explain the change in expected returns of commercial and residential property. Furthermore, we use distributed-lag model to explore the relationship between macroeconomic factors and real estate returns. The major findings of this article can be summarized as follows. First, it is easier for investors to infer the future fluctuation through oneself returns in the past in the commercial real estate market, but part on the residential real estate market, the volatility of residential property returns is influenced by external factor more easily. Second, our empirical applications in both commercial and residential real estate markets show that the risk is positively correlated with both property returns and high risk can bring high return. Third, there are different relations of intensity between real estate returns and macroeconomic factors and the impact of macroeconomic factors on real estate returns exist time-lag. In addition, macroeconomic factors’ impact on commercial returns is relatively great, and the environmental change takes place to the impact of the commercial property returns comparatively fast.

風險和報酬彼此之間的關係常常都是資產持有者所關心的，人們願意承受高風險以換取高報酬的情形，似乎相當地合乎直覺，然而學者使用不同模型來估計風險趨避係數，卻發現結果大不相同，而本文採2000年到2010年的台灣加權股價指數報酬率為樣本，延續前人研究利用了不對稱每日報酬平方(Asymmetric MIDAS) 、三個不對稱GARCH in Mean模型: Asymmetric GARCH(1,1)-M，Exponential GARCH(1,1)-M還有考慮金融資產報酬率通常非為常態分配的設定下採取的Exponential GARCH(1,1)-M(GED分配)所計算的條件變異數來替代風險，置入跨期資本資產定價模型(Intertemporal CAPM, ICAPM)來估計風險趨避係數。結果發現Asymmetric MIDAS估計者為正值且顯著，而不對稱GARCH模型下僅有EGARCH(1,1)-M(GED分配)所估計者於金融風暴兩年子樣本期間為正值但不顯著外，其餘皆為負值且不顯著。