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1
Kozyreva, Maria. "How reliable is implied volatility A comparison between implied and actual volatility on an index at the Nordic Market." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1635.
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Volatility forecast plays a central role in the financial decision making process. An intrinsic purpose of any investor is profit earning. For that purpose investors need to estimate the risk. One of the most efficient
methods to this end is the volatility estimation. In this theses I compare the CBOE Volatility Index, (VIX) with the actual volatility on an index at the Nordic Market. The actual volatility is defined as the one-day-ahead prediction as calculated by using the GARCH(1,1) model. By using the VIX model I performed consecutive predictions 30 days ahead between February the 2nd, 2007 to March
the 6th, 2007. These predictions were compared with the GARCH(1,1) one-day-ahead predictions for the same period. To my knowledge, such comparisons have not been performed earlier on the Nordic Market. The conclusion of the study was that the VIX predictions tends to higher values then the GARCH(1,1) predictions except for large prices upward jumps, which indicates that the VIX is not able to predict future shocks.
Except from these jumps, the VIX more often shows larger value than the GARCH(1,1). This is interpreted as an uncertainly of the prediction. However, the VIX predictions follows the actual volatility reasonable
well. I conclude that the VIX estimation can be used as a reliable estimator of market volatility.
2
Xin, Mao. "The VIX Volatility Index." Thesis, Uppsala universitet, Analys och tillämpad matematik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-153705.
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Olofsson, Isak. "@TheRealDonaldTrump’s tweets correlation with stock market volatility." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275683.
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The purpose of this study is to analyze if there is any tweet specific data posted by Donald Trump that has a correlation with the volatility of the stock market. If any details about the president Trump's tweets show correlation with the volatility, the goal is to find a subset of regressors with as high as possible predictability. The content of tweets is used as the base for regressors. The method which has been used is a multiple linear regression with tweet and volatility data ranging from 2010 until 2020. As a measure of volatility, the Cboe VIX has been used, and the regressors in the model have focused on the content of tweets posted by Trump using TF-IDF to evaluate the content of tweets. The results from the study imply that the chosen regressors display a small significant correlation of with an adjusted R2 = 0.4501 between Trump´s tweets and the market volatility. The findings Include 78 words with correlation to stock market volatility when part of President Trump's tweets. The stock market is a large and complex system of many unknowns, which aggravate the process of simplifying and quantifying data of only one source into a regression model with high predictability.Syftet med denna studie är att analysera om det finns några specifika egenskaper i de tweets publicerade av Donald Trump som har en korrelation med volatiliteten på aktiemarknaden. Om egenskaper kring president Trumps tweets visar ett samband med volatiliteten är målet att hitta en delmängd av regressorer med för att beskriva sambandet med så hög signifikans som möjligt. Innehållet i tweets har varit i fokus använts som regressorer. Metoden som har använts är en multipel linjär regression med tweet och volatilitetsdata som sträcker sig från 2010 till 2020. Som ett mått på volatilitet har Cboe VIX använts, och regressorerna i modellen har fokuserat på innehållet i tweets där TF-IDF har använts för att transformera ord till numeriska värden. Resultaten från studien visar att de valda regressorerna uppvisar en liten men signifikant korrelation med en justerad R2 = 0,4501 mellan Trumps tweets och marknadens volatilitet. Resultaten inkluderar 78 ord som de när en är en del av president Trumps tweets visar en signifikant korrelation till volatiliteten på börsen. Börsen är ett stort och komplext system av många okända, som försvårar processen att förenkla och kvantifiera data från endast en källa till en regressionsmodell med hög förutsägbarhet.
4
Vikberg, Sara, and Julia Björkman. "How Well Does Implied Volatility Predict Future Stock Index Returns and Volatility? : A Study of Option-Implied Volatility Derived from OMXS30 Index Options." Thesis, Stockholms universitet, Företagsekonomiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-187552.
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The purpose of this thesis is to study if and how well implied volatility can predict realised volatility and returns on the OMXS30 index one month in the future. The findings are put in relation to how historical volatility can predict realised volatility and how changes in implied volatility can predict returns. The study covers the time period from 10th of May 2012 to 9th of February 2020 and the implied volatility used in the study is derived from an unweighted average of OMXS30 call and put option implied volatility. Six different OLS-regressions are performed to study the prediction capability of implied volatility. This study finds support of implied volatility to be a statistically significant estimate for future realised returns in a univariate regression. However, our results show that historical volatility performs slightly better predictions of realised volatility than implied volatility. These are contradictory results to the majority of the papers studied in this thesis. These papers share the common notion that implied volatility is superior to historical volatility in predicting realised volatility. Further our results show that implied volatility nor change in implied volatility are significant estimates to future realised returns and perform poorly as predictors. This result is supported by the larger part of previous research, which found implied volatility to be a weak predictor of returns.
5
Lu, Yu Hang. "Hedging and volatility of Hang Seng Index." Thesis, University of Macau, 2006. http://umaclib3.umac.mo/record=b1676381.
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Reuterhäll, Fredrik. "Forecast quality of the Swedish Volatility Index." Thesis, Stockholm University, School of Business, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-6007.
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In this paper, I investigate the forecasting power of implied volatility via a new volatility index for the Swedish stock market (SVIX). By implementing the same methodology as the new VIX index originated from CBOE, I examine the information content of implied volatility and appraise the forecast quality of SVIX using two methods. Firstly, I use option valuation to evaluate the information content of implied volatility. I use four different volatilities and the evidence is clear. Using historical volatility or lagged one day at-the-money implied volatility generates poor results. Evaluating the quality of the Swedish volatility index SVIX and the average between the implied volatility lagged one day of one at-the-money call and one at-the-money put option (AIV), the results are diverted and there is no clear evidence whether to use AIV or the SVIX. Secondly, I evaluate the forecasting performance of the GARCH (1,1) model, SVIX and the AIV. Evidence point in the directions that SVIX and AIV forecasts is of higher quality than the GARCH (1,1) model, which uses historical information to produce volatility forecasts.
7
Blair, Bevan John. "Modelling Standard and Poors 100 index volatility." Thesis, Lancaster University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340564.
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Nilsson, Oscar, and Okumu Emmanuel Latim. "Does Implied Volatility Predict Realized Volatility? : An Examination of Market Expectations." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-218792.
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The informational content of implied volatility and its prediction power is evaluated for time horizons of one month. The study covers the period of November 2007 to November 2013 for the two indices S&P500 and OMXS30. The findings are put in relation to the corresponding results for past realized volatility. We find results supporting that implied volatility is an efficient, although biased estimator of realized volatility. Our results support the common notion that implied volatility predicts realized volatility better than past realized volatility, and that it also subsumes most of the informational content of past realized volatility.
9
Pachentseva, Marina, and Anna Bronskaya. "On Stock Index Volatility With Respect to Capitalization." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1189.
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Condfidence in the future is a signicant factor for business development. However frequently, accurate and specific purposes are spread over the market environment influence.Thus,it is necessary to make an appropriate consideration of instability, which is peculiar to the dynamic development. Volatility, variance and standard deviation are used to
characterize the deviation of the investigated quantity from mean value.
Volatility is one of the main instruments to measure the risk of the asset.
The increasing availability of financial market data has enlarged volatility research potential but has also encouraged research into longer horizon volatility forecasts.
In this paper we investigate stock index volatility with respect to capitalization with help of GARCH-modelling.
There are chosen three indexes of OMX Nordic Exchange for our research. The Nordic list segment indexes comprising Nordic Large Cap,
Mid Cap and Small Cap are based on the three market capitalization groups.
We implement GARCH-modeling for considering indexes and compare our results in order to conclude which ones of the indexes is more volatile.
The OMX Nordic list indexis quiet new(2002)and reorganized as late as October 2006. The current value is now about 300 and no options do exist. In current work we are also interested in estimation of the Heston
model(SVmodel), which is popular in financial world and can be used in option pricing in the future.
The results of our investigations show that Large Cap Index is more volatile then Middle and Small Cap Indexes.
10
Süss, Stephan. "Volatility indices and their derivatives /." [S.l.] : [s.n.], 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=018685872&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Seifert, Bonifaz Manuel. "Party volatility in Peru: Rethinking democratic institutionality." Politai, 2017. http://repositorio.pucp.edu.pe/index/handle/123456789/92178.
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This article presents a contribution to the approach when with political parties at the sub-national level. After the political parties crisis of 1989-1993 the Peruvian political system was reconfigured. Within this framework, regional and municipal elections were held (2002-2014) and showed how regional movements became hegemonic electoral actors at the subnational level. However, these elections also exhibited a high party volatility, both for regional and national parties. Thus, the present work seeks to rethink our approach to the Peruvian party system and democratic institutionality, assuming that instability is its essential characteristic and, from this, to understand democratic consolidation under this scenario.Este artículo presenta un aporte a la manera de abordar la problemática de los partidos políticos a escala sub-nacional. Luego de la crisis partidaria de 1989 -1993, se reconfiguró el sistema político peruano. En este marco, se desarrollaron las elecciones regionales y municipales (2002-2014), que mostraron cómo los movimientos regionales se volvieron actores hegemónicos electorales en el ámbito subnacional. Sin embargo, estas elecciones también exhibieron una alta volatilidad partidaria, tanto en los partidos nacionales como en los regionales. Así, el presente trabajo busca repensar nuestro acercamiento al sistema de partidos peruanos y la institucionalidad democrática, asumiendo que la inestabilidad es su característica esencial y, a partir de ello, comprender la consolidación democrática desde este escenario.
12
Kalogeropoulou, Joanna. "Arbitrage in the FTSE 100 index futures." Thesis, Brunel University, 1998. http://bura.brunel.ac.uk/handle/2438/5396.
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This thesis presents five empirical papers investigating the issue of arbitrage trading of the FTSE 100 stock index futures. The first paper explores the effects of nonsynchronous trading on the spot index and develops a new technique as well as improving current methodologies for removing them. Studies in U. S. have shown that if the problem of non-synchronous trading is severe, the reported spot index is not reliable affecting the correct pricing of futures contracts. The second paper investigates the elasticity of supply of arbitrage in the futures market and the ability of the spot and the futures markets to respond to new information. It shows that arbitrage trading is initiated when spot prices largely drift apart from the futures prices. In addition, the futures prices tend to uncover new information before the spot prices, although this relationship is not stable over time. The analysis incorporates all possible channels of information to the -markets, which previous research fails to consider. The third paper analyses the behaviour of the deviation of the actual futures price from its theoretical value. Although this deviation is seen to have decreased its size over the years, it is still significant and persistent. Furthermore, it cannot be explained by the tax-timing option on pricing the futures or the effects of nonsynchronous trading. The fourth paper examines the presence, size and frequency of the profitability of the observed arbitrage opportunities by applying different transactions costs bounds to account for different classes of traders. After applying trading simulations arbitrage profitability is found to be frequent and significant, despite the fact that its size has decreased over the years. Finally, the thesis concludes with the fifth empirical paper which investigates the impact of futures trading on the spot and futures market volatility. It finds that arbitrage increases spot and futures price volatility but a volatile market brings the two markets closer on the whole, the thesis shows that although profitable arbitrage opportunities are not present in the long-run, they are not quickly removed in the short-run, allowing the spot and futures prices to drift apart.
13
Zevallos, Mauricio, and Carlos del Carpio. "Metal Returns, Stock Returns and Stock Market Volatility." Economía, 2015. http://repositorio.pucp.edu.pe/index/handle/123456789/118122.
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Given the extensive participation of mining stocks in the Peruvian stock market, the Lima Stock Exchange (BVL) provides an ideal setting for exploring both the impact of metal returns on mining stock returns and stock market volatility, and the comovements between mining stock returns and metal returns. This research is a first attempt to explore these issues using international metal prices and the prices of the most important mining stocks on the BVL and the IGBVL index. To achieve this, we use univariate GARCH models to model individual volatilities, and the Exponentially Weighted Moving Average (EWMA) method and multivariate GARCH models with time-varying correlations to model comovements in returns. We found that Peruvian mining stock volatilities mimic the behavior of metal volatilities and that there are important correlation levels between metals and mining stock returns. In addition, we found time-varying correlations with distinctive behavior in different periods, with rises potentially related to international and local historical events.Dada la amplia participación de acciones mineras en el mercado de valores peruano, la Bolsa de Valores de Lima (BVL) resulta un escenario ideal para explorar tanto el impacto de los ren- dimientos de acciones de metales en los rendimientos de las acciones mineras y la volatilidad del Mercado de valores, así como los co-movimientos entre los rendimientos de las acciones mineras y los rendimientos de los metales. Este estudio es un primer intento en explorar estos temas usando precios internacionales de los metales y los precios de las acciones mineras más importantes de la BVL y del índice IGBVL. Para conseguir esto, hemos usado modelos GARCHunivariados para modelar las volatilidades individuales, y el método de Media Móvil Ponderada Exponencialmente (EWMA) y modelos GARCH multivariados con correlaciones de variantes en el tiempo a modelos de co-movimientos en rendimientos. Hemos encontrado que las volatilidades imitan el comportamiento de las volatilidades de los metales y que hay importantes niveles de correlación entre los metales y el retorno de las acciones mineras. Adicionalmente, encontramos correlaciones variantes en el tiempo con un comportamiento distintivo en periodos diferentes, el que aumenta potencialmente en relación con eventos históricos internacionales o nacionales.
14
Yang, Qianqian. "An empirical study of implied volatility in Australian index option markets." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16325/.
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With the rapid development of option markets throughout the world, option pricing has become an important field in financial engineering. Among a variety of option pricing models, volatility of underlying asset is associated with risk and uncertainty, and hence is treated as one of the key factors affecting the price of an option. In particular, in the framework of the Black-Scholes option pricing model, volatility of the underlying stock is the only unobservable variable, and has attracted a large amount of attention of both academics and practitioners. This thesis is concerned with the implied volatility in the Australian index option market. Two interesting problems are examined. First, the relation between implied volatility and subsequently realized volatility is investigated by using the S&P/ASX 200 (XJO) index options over a five-year period from April 2001 to March 2006. Unlike the S&P 100 index options in the US market, the XJO index options are traded infrequently, in low volumes, and with a long maturity cycle. This implies that the errors-in-variable problem for the measurement of implied volatility is more likely to exist. After accounting for this problem by the instrumental variable method, it is found that both call and put options implied volatilities are nearly unbiased and superior to historical volatility in forecasting future realized volatility. Second, the volatility structure implied by the XJO index options is examined during the period from April 2001 to June 2005. The volatility structure with respect to moneyness and time to maturity are investigated for both call and put option price series. It is found that the volatility smile largely exists, with call (put) option implied volatilities decreasing monotonically as the call (put) goes deeper out of the money (in the money). This result is consistent with the welldocumented evidence of volatility smile on other index options since the stock market crash of 1987. In summary, this thesis presents some important findings on the volatility inferred from the XJO index options traded on the ASX.
15
Chen, Chia-Hua, and 陳家華. "Price Clustering Phenomenon in CBOE Index Option Quotes." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/68f8r6.
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碩士國立高雄第一科技大學
金融營運所
96
The asset price clustering phenomenon has been found for financial market. But the price clustering phenomenon in the index option market shows a blank. This paper provides the evidence of price clustering for the index options contract traded on the CBOE. We offer evidence that the clustering phenomenon in low-price index options are clustered on x.05 and x.1 for bids. Low-price index options are highly clustered on x.5 for offers. But each index options clustered on different prices. High-price index options are highly clustered on round dollars and half-dollars price for bids and offers. Nonetheless, in the money index options and out of the money index options are also highly clustered on round dollars and half-dollars for bids and offers. The price clustering on x.0 and x.5 increases with the volatility for bids and offers. The SPX price clustering on x.0 increases with bid-ask spread but decreases with date until expiry. The SPX price clustering on x.5 decreases with bid-ask spread, return and open interest. The OEX price clustering on x.0 and x.5 decreases with delta but increases with date until expiry.
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HUNG, MING-WEI, and 洪銘威. "Impacts of the CBOE Skew Index on the Taiwan Stock Index." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/55412283349435496945.
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碩士國立臺北大學
經濟學系
104
Investors pay lots of attention to variations of stock prices. However, stock markets crash from time to time without warning. This study thus tries to use the information-leading characteristic of stock option markets and adopt the volatility Skew Index proposed by the CBOE to explore the movements and trends of the Taiwan stock price index. The daily data employed are from January 1, 2010 to December 31, 2015. After controlling volatilities of the Taiwan stock price index or the S&P 500 index and exchange rates, this study uses autoregressive dynamic models to investigate how lagged Skew Index movements would affect the current 1-day, 5-day, 20-day and 60-day Taiwan stock returns. Three kinds of models are designed to examine the effects of the Skew Index in details. The empirical results show that the lagged Skew index movements have positive impacts on the Taiwan stock returns, and these impacts become more significant when the previous Skew indices decrease. That is, the current Taiwan stock price indices would decrease if the previous Skew indices fall, other things being equal. In addition, the fitness of the models improves with the longer periods of stock returns. This study also finds that volatilities of the S&P 500 index perform better than volatilities of the Taiwan stock price index in explaining and forecasting 1-day Taiwan stock returns.
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Chang, Ying-Kung, and 張英冠. "The Relationship between Gold Spot Price and CBOE Gold Index." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/88080090727126690486.
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碩士國立高雄第一科技大學
金融研究所
101
Because of the recent financial crises, gold market commodities have become a new choice of investment allocation. The reason is that gold prices are relatively stable and resistant to drops in value. Furthermore, gold has played critical hedging and value retention roles in the global economic recession. Therefore, this study explores the correlation between gold spot prices and the gold index between January 2007 and December 2012. A root test, co-integration test, Granger causality test, analysis of variance (ANOVA), and impulse response analysis were used to conduct this empirical research. The empirical results of the co-integration test showed that the relationship between the two variables is not co-integrated. In other words, their long-term relation is non-significant. Although investors cannot accurately predict the changing correlation between gold spot prices and the gold index, they can achieve risk diversification. CBOE Gold Index and the causal relationship between the London spot gold,it can be seen CBOE Gold Index change is caused by changes in the London spot gold , by the impulse response analysis showed,two variables in the face of shocks from their reactions are more intense degree.Alternatively, you can learn through impulse response analysis,CBOE Gold Index messaging market faster,the London spot on CBOE Gold Index weaker influence)On the other hand CBOE Gold Index on the London spot gold strong influence.Concluded that to,you wish to invest in gold spot, should deliberate CBOE Gold Index volatility trends.
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Chiu, Tzu-Ling, and 邱紫菱. "The Risk and Return of CBOE S&P500 PutWrite Index." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/geb7p6.
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碩士國立清華大學
計量財務金融系
105
This paper focuses on the return and the risk of one of CBOE options-selling strategies, CBOE S&P 500 PutWrite Index （PUT）. In this strategy, we divide risk into three components, which include passive equity, short volatility and dynamic equity. We will target on the impact to PUT within these risk components. From the empirical results, we find that active equity exposure is a significant source of risk. And this paper also figures out the benefit of using the dynamic equity in different market conditions. In the end, we use S&P500 futures to propose a risk-managed PutWrite strategy to hedge away this active equity exposure and analyze how the adjustment affects the return and risk.
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Chiang, Chen-Yi, and 江貞頤. "Finding Alpha in CBOE Standard & Poor 500 Covered Combo Index." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/85nbz9.
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碩士國立清華大學
計量財務金融系
105
The S&P 500 BuyWrite Index have, on average, outperformed the S&P 500 Index over the past 15 years while realizing lower standard deviations of returns. This analysis dissects the new strategy CBOE S&P 500 Covered Combo Index, introduces the strategy’s construction and its story. Then, we see the relationship between the factors and strategies, and compare the performance with other strategies and S&P 500 index. Finally, we focus on the alpha in this strategy, which is the difference between absolute return and expecting return, and then we make a conclusion about this strategy and discuss which investors are recommended to use this strategy.
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Mazouz, Khelifa. "The impact of CBOE options listing on the volatility of NYSE traded stock: a time varying risk approach." 2004. http://hdl.handle.net/10454/3155.
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NoThis paper employs the standard General Auto-regressive Conditional Heteroskedasticity (GARCH(1,1)) process to examine the impact of option listing on volatility the underlying stocks. It takes into consideration the time variation in the individual stock's variance and explicitly tests whether option listing causes any permanent volatility change. It also investigates the impact of option listing on the speed at which information is incorporated into the stock price. The study uses clean samples to avoid sample selection biases and control samples to account for the change in the volatility and/or information flows that may be caused by factors other than option listing.
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Cheng, I.-Fen, and 鄭伊凡. "Forecasting Volatility of Stock Index Using Long Memory and Market Volatility Index." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/mb9t43.
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碩士銘傳大學
財務金融學系碩士班
94
In this study we explore the forecasting value of unobserved autoregressive moving average、long memory and generalised autoregressive conditional heteroskedasticity models. First we consider unobserved components and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of unobserved autoregressive moving average and long memory models are compared with generalised autoregressive conditional heteroskedasticity models for daily return series. The generalised autoregressive conditional heteroskedasticity models is extended to include realised and implied volatility measures as explanatory variables. The main focus is on forecasting the daily variability of Taiwan Stock Exchange Weighted、Electron and Banking Insurance index series for which trading data. Incorporating implied volatility and realized volatility into the GARCH model, the volatility of Electron and Banking Insurance index will be more stable. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility. We explore the forecast accuracy statistics that consist of MSE、MAE and MAPE functions. The empirical results indicate that long memory model produce far more accurate volatility forecasts compared to Unobserved autoregressive moving average and generalised autoregressive conditional heteroskedasticity model. Long memory models seem to provide the most accurate forecasts.
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Jian, Yu Shi, and 簡育昰. "The Information Content of CBOE SKEW Index - Trading Strategy Under Markov Regime Switching Model." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/pn7z3a.
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碩士國立政治大學
金融研究所
104
This paper divided into two parts to investigate on the information content of CBOE SKEW Index. For the first part, we do time series analysis to observe the relationship between SKEW Index and other variables. First, we found that SKEW index is totally different from VIX index. VIX index is a proxy for the standard deviation of the returns. The standard deviation describes the average spread of the distribution of returns around its mean. This is not a sufficient measure of risk because the distribution of S&P 500 log returns is not normal. SKEW Index captures the tail risk of the distribution. Next, SKEW Index is good at predict future S&P500 ETF returns especially weekly speaking. Also, we found that the correlation between SKEW index & S&P500 index is too unstable to interpret. We argue that it’s not easy to interpret SKEW Index directly but we can combine SKEW Index with VIX Index. Regarding the above reason, in second part, we combined SKEW Index with VIX Index to construct trading strategy under Markov Switching Model. By comparing with FTP Model, which included VIX index only, we found that TVTP model, which encompassed VIX Index and SKEW Index together, significantly outperform others. When the model detected regime switching, we buy/short SPY ETF in the market separately. We did the simulation test from 2002.4.15 to 2013.3.29. Without considering tax, fee and dividend, we earned yearly average rate of return 13.61%. After considering tax, fee and dividend, we earned yearly average rate of return 9.51%.
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Chung, Wen-tau, and 鍾文韜. "Volatility Index And Derivatives Pricing Under Stochastic Volatility Model." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/05668999975361240047.
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碩士國立暨南國際大學
財務金融學系
100
The aim of this paper is to explore effects of the stochastic volatility for volatility index and volatility derivatives. We price VIX and VIX futures under stochastic volatility model. In empirical study, we use GMM and nonlinear least square method to estimate the parameters of the pricing model, and we compared the results with nonlinear method. The empirical study shows that the VIX model with parameters estimated from this procedure has a good fit on most of period, although some outliers perform not very well. While pricing VIX futures, we find that the discrepancy between the model price and market price is just 0.09% to 0.82% undervalued for the futures contracts. These results demonstrate that the stochastic volatility risk can have a significant effect on the VIX and VIX futures.
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Chung, Wen-Fu, and 鍾文富. "The Effect of Volatility Index on Stock Market Volatility." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/98840971679714870156.
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碩士逢甲大學
財務金融學所
97
In 1993, the Chicago Board Options Exchange (CBOE) introduced the CBOE S&P100 Volatility Index (VXO), which provided a measure of market expectations of near-term volatility conveyed by the S&P100 index options. In 2003, CBOE changed the method of constructing the index (CBOE Volatility Index, VIX) and its underlying asset to measure the expected volatility derived from the S&P500 index options. On December 18, 2006, the Taiwan Futures Exchange (TAIFEX) established the Volatility Index of Taiwan Stock Index Options, which is constructed by using the method suggested by CBOE in 2003. Based on Arnold and Earl (2007), this study constructs a new Volatility Index of Taiwan Stock Index Options. The new index is then used in different models to test its ability of forecasting stock market volatility. Besides, the trading volumes of call options and put options are also included in the models to reflect the asymmetric influences on stock market volatility from the information of option markets. The empirical results of this study reveal that our new Volatility Index has a better performance on forecasting stock market volatility. However, the impact of the option trading volume on stock market volatility is insignificant. Among the different models tested, the two-stage GJR-GARCH model, including the option volume in the mean equation, has the better performance, and the volatility index with an optimal lag-length of 10 days is a better predictor for future stock market volatility.
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Huang, Guo-lun, and 黃國綸. "Tail Risk Trading Strategy Using Volatility-of-volatility Index." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8b2e8h.
Full textAbstract:
碩士國立中山大學
財務管理學系研究所
106
The purpose of this paper is to use a new model-free measure to proxy for tail risk and exploit option induced order imbalance (OOIB) to predict the return of this tail risk indicator. Unlike VaR or VIX based literatures, this paper exploits the volatility of volatility as measured by the CBOE VVIX index to measure tail risk events. In this study, the option induced order imbalance (OOIB) is the dynamic hedging position from VIX option market makers. The OOIB positively and significantly predicts the return of VVIX index, and it was mainly contributed by at-the-money options. This result indicates that the order imbalance in VIX option market has the information and predictability toward market volatility of volatility and tail risk events, this paper then develops a long straddle position on VIX options to capture tail risk returns.
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Li, Wan-Jou, and 李宛柔. "The Study on Volatility Index and Its Relationship between Realized Volatility and Stock Index Return." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/kvbt9a.
Full textAbstract:
碩士國立中央大學
企業管理研究所
94
This article makes a description of the market volatility indices (VXO and VIX) which were introduced by CBOE in 1993 and 2003. We apply the two formulas of volatility indices to calculate volatility indices of TXO. The research period is from April 1, 2002 to March 31, 2006.The main topic of this research is to test the ability of the volatility index to forecast realized volatility and test the relationship between volatility index and stock index return. Finally, the research is also supplemented with robustness analysis to make the conclusion more credible. The major empirical results are shown as follows. 1. Three models are used to forecast realized volatility. By regression analysis, VXO has the best forecasting ability among all volatility models and the forecasting ability increases with the increase of calculation period of realized volatility. If options trading volume is added to the regression model, the forecasting performance increases as well. Besides, VXO has the lowest error from MAE and RMSE analysis. 2. There is a negative relationship between the changes in the stock index return and volatility indices. For VXO, the relationship is asymmetric. The extent of this asymmetric effect depends on the market trend. The bear market enhances VXO's asymmetric effect while there is no significant asymmetric effect in the bull market. For VIX, there is no asymmetric effect in either market. 3. As for the VXO and VIX to forecast the stock index return, there is significant negative relationship only for 10, 20 and 60 days. VXO has the better power to forecast the stock index return. VXO also has the better forecasting power when options trading volume is added. 4. If the volitility is sorted, the low volatility from VXO and VIX is accompanied by negative return while the high volatility is accompanied by the positive return. 5. If the sample is divided into two stages according to the trading environment to test the ability of VXO and VIX to predict the volatility and stock index return, VXO is the best estimator. This result is consistent with that of the whole sample period. For the application in Taiwan option market, VXO is able to provide more information and ability to forecast. It is hoped that Taiwan market can establish the appropriate volatility index.
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Chiu, Yung-Chin, and 邱永金. "The Information Content of Implied Volatility Index for Realied Volatility and Index Return: Evidence from Taiwan Index Option Market." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/11836261379900614899.
Full textAbstract:
碩士嶺東科技大學
財務金融研究所
96
In 1993, the Chicago Board Options Exchange（CBOE）presented the Volatility Index （VIX）of S&P100, which is used for estimate investors’ expectation to the volatility of stock market in the future, afterwards, it is widely accepted by market. Because it can describe the change of investors’ various expectation concretely, it is also called “The investor fear gauge”. With the development of financial theory and in order to press close to the market more, CBOE reorganized VIX index of S&P500the in September, 2003.Recently, the Taiwan Futures Exchange（TAIFEX）uses the volatility index model constructed by Chicago Board Option Exchange to set up the intraday volatility index database of Taiwan’s option market.The main topic of this research is using high frequency data of five-minute, fifteen–minute, and thirty-minute intraday data of the VIX and VXO（that is the VIX of 1993）to examine information content of volatility indices for realied volatility and index return.The results indicate that the VIX has the best explanatory ability for the realied volatility, and there is a positive relationship between the realied volatility and VIX. Furthermore, there is a negative relationship between the changes in the stock index return and volatility indices, but the asymmetric effect is not very significant. The VIX not only has the best explanatory ability for forecasting the stock index return, but there is a negative relationship between the future stock index return. Meanwhile, we found that if the more term and information we consider, the explanatory ability of volatility indices for realied volatility and stock index return will increase.
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Dickson, Samuel, and 迪生山姆. "Investment Strategy Utilizing the Volatility Index." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/52022068615259021033.
Full textAbstract:
碩士國立中山大學
企業管理學系研究所
101
This thesis is an investment strategy that seeks to profit from increases in market volatility. There have been several boom and bust cycles during the past fifteen years and volatility is projected to continue forward as a result of global asset misallocation and challenges stemming from debt liquidity. Volatility is measured by the Chicago Board of Options Exchange VIX volatility index. A proposed mean reversion strategy uses the VIX as a contrarian indicator of hope and fear to time decisions at extreme levels that have been determined through statistical analysis. This thesis found through back testing that market timing is possible at extreme levels of fear but is less reliable during extreme levels of hope and complacency. This strategy that utilizes measures of sentiment does however outperform the general market despite being active only five months on average per year. By synthesizing a broad range of fundamental, technical, and behavioral research, this thesis develops a unique contribution and practical set of market trading guidelines. The significance of these findings will help the individual investor to make better decisions during times of increased volatility.
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Wu, Guo-Yu, and 吳國裕. "Presidential Election and Stock Index Volatility." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/57899676188415164730.
Full textAbstract:
碩士國立高雄第一科技大學
財務管理研究所
103
This paper investigated whether the Presidential Election will affect the Taiwan stock market. The investigated period covered the Presidential Elections in the year of 2000, 2004, 2008 and 2012. In general, the government''s finances and monetary policy have a greater influence to the traditional domestic sectors. In Taiwan, the President has a decisive influence to finances and monetary policy. The paper investigated whether the presidential election will affect the Taiwan stock market shares of each sector through a dummy variable GJR-GARCH pattern. The empirical results found that the construction sector which uncertainty disappeared after the presidential election cause to the information transmission increasing and the fluctuation of stock pricing decreasing, which is the most obvious in all sectors. According to the result, it can be explained the president-elect had considerable influence to the real estate industry, but the real estate rose in Taiwan is also related to campaign finance.
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Huang, You-Cheng, and 黃宥澄. "TAIFEX OPTION VOLATILITY INDEX AND FORECASTING." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/05208934192757581693.
Full textAbstract:
碩士國立雲林科技大學
財務金融系碩士班
95
The research period is from January 2, 2006 to December 29, 2006 of TXO. The main topic of this research is to test the ability of the volatility index and to forecast realized volatility. The method used in this paper is to modify the market volatility index which was introduced by CBOE in 1993. First, the research transforms the linear weighted method into the parabolic method. Then the research discusses the Historical volatility model, Implied volatility model, EWMA model and GARCH(1,1) model. Finally, the research is to compare the ability of forecasting realized volatility and to calculate the errors. The conclusion is as follows： 1. The ability to forecast the realized volatility of Historical volatility model becomes worse as the period increases. 2. The VIX II model has the best ability to forecast the realized volatility. 3. The EWMA model has the worst ability to forecast the realized volatility. 4. GARCH(1,1) model is among the others.
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Kuo, Wei-Ting, and 郭葦庭. "The Relationship among Gold ETF Volatility Index, Crude Oil ETF Volatility Index and Stock Markets in Asia." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v4k4gs.
Full textAbstract:
碩士淡江大學
財務金融學系碩士班
106
This study supplements previous research which usually focused on the spot gold market, spot crude oil market or gold and crude oil futures. In this paper, we investigate the Linear-Regression Model among CBOE Gold Volatility Index (GVZ), CBOE Crude Oil Volatility Index (OVX) and Stock Markets in Asia. And we use two variables which is The CBOE Gold ETF Volatility Index and the CBOE Crude Oil ETF Volatility Index. Because the volatility index implied the investor sentiment index and the sentiment index used to reflect the market investment predictions. The experimental results show that during the sample average period, the GVZ, OVX both show negative effect to the stock markets. Furthermore divided the volatility into rising or falling. The result shows the impact of the falls of GVZ and OVX on the stock market is greater than when the volatility index rises in the developed markets；Also in emerging markets, the impact of the OVX on the stock market is greater than the GVZ, which is most obvious when the OVX falls. In addition, the relationship between the three is a one-way change, both of GVZ and OVX are ahead of the stock market, and the reverse is not.
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Kao, Ju-Chieh, and 高如潔. "The Correlated Jump Relationship between Volatility Index and Equity Index." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/00416274711087448585.
Full textAbstract:
碩士淡江大學
財務金融學系碩士在職專班
98
This thesis adopts the ARJI model to realize the jump activity in VIX and S&P 500. In addition, this thesis also applies the CBP-GARCH model to analyze the discontinuous jump and the time-varying correlated jump intensity for VIX and S&P 500 over the period extending from January 2, 1990 to May 29, 2009. The empirical results include：(1) VIX and S&P 500 have jump activity respectively by ARJI model. (2) The jump intensity and jump volatility of VIX are stronger than S&P 500 by CBP-GARCH model. On the other hand, there are the common jump activity between VIX and S&P 500. (3) It is discovered that the correlated jump intensity at the time the events take place are all higher than either before or after the respective events, as is the case with the correlated jump intensity time-varying co-movements.
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施富鐘. "A Study on Dynamic Relationship between Market Volatility and Stock Index Volatility." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/02892982431137262351.
Full textAbstract:
碩士國立中興大學
企業管理學系研究所
92
This study investigates the dynamic relationships between stock index volatility and market volatility using the monthly data running from January 1984 to March 2003 in Taiwan. The market volatility being investigated in this study involves interest rate volatility, exchange rate volatility, unemployment rate volatility, oil price volatility, inflation rate volatility and industrial production volatility. All the variables examined present a unit root by the ADF and the KPSS test. By Johansen cointegration test, this study finds the result that there exists the long run equilibrium relationship between the stock index volatility and the market volatility. The stability test of CUSUM of squares also confirms that the series are consistent and stable. The result of Granger causality test indicates that, among all volatilities analyzed, only the stock index volatility one-way leads the inflation rate volatility. The empirical results of impulse response functions point out that, the stock index volatility reacts in a steady and smoothing way while the market volatility changes over time. Evidence of variance decomposition shows that stock index volatility caused by all the other volatilities would gradually diminish after twelve periods. In addition, the other four variables underlined, including the stock index volatility, the interest rate volatility, the unemployment rate volatility, oil price volatility and the exchange rate volatility present strong exogeneity orderings whereas both the inflation rate volatility and the industrial production volatility show weak exogeneity orderings in the system.
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Yu, Mei-Jie, and 余美潔. "Forecasting Power of Comparing TAIEX Options Volatility Index and Extreme-Value Volatility." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/73349759191356065582.
Full textAbstract:
碩士國立屏東科技大學
財務金融研究所
99
In this paper, the daily data of Taiwan Stock Weighted Index calculated volatility from December 1st, 2006 to October 29th, 2010 as sample. Under the framework of GJR-GARCH (1,1)model, added extreme-value volatility and VIX of Taiwan index options to examine (1) VIX will increase the forecasting performance of volatility or not, (2) extreme-value volatility will increase the forecasting performance of volatility or not, (3) compared extreme-value volatility and VIX will increase the forecasting power of volatility or not. The empirical results show, extreme-value volatility and VIX are both increase the forecasting power of volatility and the forecasting power of extreme-value volatility beter than volatility index.
35
Wu, Kuan-Yi, and 吳冠億. "Estimation and Comparison for Stock Index Volatility with Various Dynamic Volatility Models." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/23303657722225821469.
Full textAbstract:
碩士國立高雄第一科技大學
財務管理所
98
By using S&P 500, NASDAQ and DJIA stock index, in this paper we examine the relationships among realized return, realized range and implied volatility, finding that the realized range and implied volatility are possessed of excellent explanation power toward the realized return, and thereby can be presented as proxy variables for volatilities. On the other hand, in comparison with the empirical performance on the CARR model and GARCH model. Whatever in-of-sample or out-of-sample volatility forecast, we found that the result from CARR model is better than from GARCH model, which means that range has a better ability than return to present the descriptions of volatilities. Finally, we find that leverage effect and implied volatility can actually increase the explanation power of volatility in short time.
36
Sang, Dahai. "The "volatility smile" of Canadian index options." Thesis, 2004. http://spectrum.library.concordia.ca/8295/1/MR04481.pdf.
Full textAbstract:
Estimating the representative agent's or the market's degree of risk aversion from securities prices has a long history. However, it is only since this century that scholars have begun using options data to do so. Options provide a particularly promising context for studying risk preferences. Embedded in the prices of traded options is rich information set relating to various aspects of the underlying asset. One piece of embedded information is the state price density. To the best of my knowledge, the implied risk aversion deriving from state price density or the "risk neutral" distribution has not been well studied in Canadian context. In this paper, the author investigates the volatility smile derived from call options on the Canadian S&P/TSX 60 Index, which is one of the most heavily traded index options in the Canadian market. Then from the option pricing function, the author recovers the risk neutral distribution by using the nonparametric approach--the regularization method proposed by Jackwerth and Rubinstein (1996). The final step is to compare the option inferred risk neutral distribution with the estimated actual distribution of index prices based on, for example, the historical price path over the same time interval. The relationship between these two distributions depends on the preference (utility) of investors about money, as they grow richer or poorer. Knowing both distributions allows us to infer what the preferences must be within an economy to be consistent with the option price and the historical returns.
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Lin, Hsiu-Jung, and 林秀蓉. "Forecasting the Volatility of Stock Index Returns." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/82001170583505247851.
Full textAbstract:
碩士淡江大學
財務金融學系碩士班
96
Volatility plays an important role in finance. If we can capture the characteristics of the motions of assets precisely, we could make good portfolios and control risks efficiently. This study investigates how specification of returns distribution influences the performance of volatility forecasting using two GARCH models and two GJR GARCH models（GARCH-N, GARCH-GED, GJR GARCH-N and GJR GARCH-GED）. Daily spot prices on the DOW, S&P500, NASDAQ and PHLX indices provide empirical sample for discussing and comparing the relative sample volatility predictive ability, given the growth potential of stock markets in America to the eyes of global investors. Empirical results indicate that the GJR GARCH-GED model is superior to the GARCH-GED model in forecasting U.S. stock market indices volatility, for all forecast horizons when model selection is based on RMSE or MAE. These findings show the signification of the asymmetry in the volatility specification. In other words, the empirical results show that bad news induces volatility greater than good news.
38
Wang, Shiou-Shan, and 王秀珊. "The Relationship between Volatility Index and Portifolio." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/50974287842254310481.
Full textAbstract:
碩士國立交通大學
經營管理研究所
95
VIX is the benchmark of American stock market volatility. VIX measures market expectations of near term volatility conveyed by stock index option prices. VIX is often referred to as the investor fear gauge, because volatility often signifies financial turmoil. CBOE has renewed the VIX methodology in 2003, and continued to provide a minute-by-minute snapshot of expected stock market volatility over the next 30 calendar days. VIX is a leading indicator. When VIX increases portfolios of large-capitalization stocks outperform portfolios of small-capitalization stocks and value-based portfolios outperform growth-based portfolios. We observed that different portfolios have different returns, so we can choose portfolio by different volatility. When the holding period is longer, t-statistic is more significant, especially for portfolios of large-capitalization. No matter we use VIX, VXD or VXN as a indicator, accumulative return(VF-GF) is the highest in VIX10％,VXD10％, and VXN10％, and accumulative return(SPF-VLF) is the highest in VIX-10％,VXD-10％, and VXN10％ under most circumstance. Observing table 13~18, there are 5 out of 12 investing strategies that its accumulative return will be positive exceeding 50％.
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Hung-Ying, Tang, and 湯惠英. "Trading strategies of Taiwan Index Option Volatility." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/99140124913526307415.
Full textAbstract:
碩士輔仁大學
金融研究所
96
This research utilizes data from Taiwan Index Futures and Options for a time interval of 9:01 to 13:50 every day during the sample period of September 3rd, 2007 to March 31st, 2008. Implied volatility (IV) and GARCH volatility are calculated per minute from the data and with Volatility Index (VIX) obtained from Taiwan Futures Exchange, these three types of volatility are acting as trading signals and used to define volatility degree (high and low). These three types of volatility with eight strategies (Long Iron Butterfly and Iron Condor, Short Iron Butterfly and Iron Condor, Long Strangle and Straddle, Short Strangle and Straddle) and two market offset methods (offset with stop and offset in next day ) are explored in combination to find a best trading strategy. The empirical results of this research show that short straddle and short strangle one strike price out of money strategies obtain net profit for all three trading signals, no matter the offset methods used and the volatility degree. Thus for these two strategies, further analyses demonstrate that, when taking IV as the trading signal, net profits in average transaction differ by volatility degree for either offset method and profited better in high degree of volatility than in low degree of volatility. This conclusion of net profit maintained for stop offset method when taking VIX as the trading signal. However, when taking GARCH as the trading signal, net profit in average transaction differ by volatility degree only in next day offset method but do not profit better in high degree of volatility. Also, when volatility degree is high, the net profit in average transaction differ by offset methods and profited better by using stop offset method than using next day offset method.
40
Tsai, Cheng-Han, and 蔡承翰. "The Relationship between Volatility of Volatility and Return: Evidence on Taiwan Index Futures." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/94725569702220273876.
Full textAbstract:
碩士國立臺灣師範大學
管理研究所
104
This study discusses the relationship between the volatility-of-volatility and the monthly return of Taiwan index futures. After considering market risk, skewness, kurtosis, and open-interest, the phenomenon that volatility-of-volatility affects the return of Taiwan index futures still exists. Base on empirical research when the volatility-of-volatility increases, the monthly return decreases simultaneously. Moreover, there is a specific relation between the monthly return of Taiwan index futures and skewness or kurtosis, even open-interest. The OLS regression estimate also coincides with the relation for all factors between return. The reason for volatility-of-volatility leading the return to decreases could be based on two concepts. First, investors who prefer uncertainty of risk might pay some premium in order to gain higher uncertainty of risk. Second, the higher the heterogeneity for preference in the uncertainty of risk, the greater decreases in premium. Among other variables that have effects on monthly return only return skewness has a significant effect.
41
Hsieh, Tai-Lin, and 謝岱霖. "An Analysis of the Relationship between Volatility Index and Stock Index ETF." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/61791699678479511481.
Full textAbstract:
碩士國立中興大學
應用經濟學系所
104
As stock market indices are not tradable, the importance and trading volume of Exchange Traded Fund (ETF) cannot be understated. ETFs track an index and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 index and the Volatility Index (VIX). However, few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the dissertation is to investigate whether VIX returns affect ETF returns. The dissertation uses vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test show conditional heteroskedasticity in the estimation of ETF returns. The diagonal BEKK model is used to accommodate the conditional heteroskedasticity in the VAR estimates of ETF returns. The dissertation uses daily data on ETF returns that follow different stock indices in the USA and Europe. The empirical results show that daily VIX returns: (1) have a significant negative effect on European ETF returns in the short run; (2) have a stronger significant effect on single market ETF returns than on European ETF returns; and (3) together with ETF returns on U.S. stocks, VIX returns are less significant than S&P500 returns.
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Huang, Yao-Tang, and 黃堯塘. "Nonlinear Asymmetric Dynamics between the Nikkei 225 Volatility Index and Index Futures." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/5872f8.
Full textAbstract:
碩士銘傳大學
財務金融學系碩士在職專班
94
Abstract The volume of Japanese stock market is second largest all over the world. Both Nikkei 225 stock index and its derivatives deeply influence global financial market. Based on rules of CBOE volatility index, we properly revise the calculation of VIX to fit Japanese stock market. Intraday 5 minutes data is used in our research to construct smooth transition vector error-correction model and analyze nonlinear dynamics between Nikkei 225 index futures (NF225) and Nikkei 225 volatility index (NVI). Our contribution is to construct and confirm the fitness of LSTVECM in examining nonlinear dynamics between NF225 and NVI. Moreover, comparing to VECM, LSTVECM could capture effect of price indication from NF225 to NVI.
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Liao, Kuei-Ping, and 廖桂苹. "Dynamic relationships between S&P 500 stock index and volatility index." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/58844726372505364328.
Full textAbstract:
碩士國立高雄第一科技大學
金融營運所
94
ABSTRACT This study examines the relationships between S&P 500 stock index and volatility index. The results indicate that the relationships between S&P 500 stock index and volatility index is usually negative. But sometimes volatility index does not move opposite its underlying index. During the high volatility period, the investor fear is greater. The stock market and volatility index are more volatile, the dynamic correlation between S&P 500 stock index and volatility index becomes weaker. And volatility index moves in the same direction with S&P 500 stock index more often.
44
Huang, Yu-Wen, and 黃幼雯. "The empirical analysis of the implied volatility index." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/59513248432919061054.
Full textAbstract:
碩士國立中正大學
財務金融研究所
99
In this paper, we use GARCH model to assess and forecast VIX’s trend, and comparing error distribution between normal distribution and t distribution. On the other hand, lots of reference say that VIX and S&P 500 stock index return have negative relationship, so in this paper we also join this relationship into the GARCH model, in order to improve model’s forecasting ability and efficiency. In symmetrical model, daily data show that GARCH(1,1) model in normal distribution is better than others model, and weekly data show that GARCH(1,1) model in normal distribution is better than others model. In asymmetrical model, daily data show that GARCH(1,1) model in normal distribution is better than others model, and weekly data show that GARCH(1,1) model in t distribution is better than others model. Finally, asymmetry can actually increase the explanation power of model.
45
Hsieh, Yun-Feng, and 謝昀峰. "The Forecasting Performance of Liquidity-Adjusted Volatility Index." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/86659718171746446310.
Full textAbstract:
碩士國立臺灣大學
國際企業學研究所
101
Options with same underlying asset, but different maturity months and strike prices trade simultaneously. It is conceivable that different liquidity among these options will have significant impact on option valuation. Low liquidity options may not be able to reflect market information fully, as high liquidity options do. As a result, the Implied Volatility (IV) calculated from these option prices will be full of noises. This study compares the performance of two liquidity weighted index: SVIX & TVVIX, where SVIX is an adjusted spread spectrum (spread-adjusted) volatility Index, and TVVIX is a trading volume index weighted by VIX. We use the weighted IV index as reference in forecasting futures market volatility to reduce noises of the high liquidity option IV, as suggested by Grover and Thomas (2012). The empirical results show that TVVIX has better performance than SVIX in forecasting TX volatility.
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SYU, SHIH-JHE, and 徐士哲. "The Behavior of Volatility Index by Quantile Autoregression." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9y93b8.
Full textAbstract:
碩士國立高雄第一科技大學
財務管理系碩士班
106
This study uses the quantile autoregression to provide a comprehensive description of the dependence pattern of volatility index (VIX). Through the VIX analysis of self-related circumstances, the early remuneration for the extreme or normal, positive or negative, economic events and non-economic events before and after the impact of differences in-depth discussion. The empirical results initially show that the relationship between the current VIX index changes and the previous index changes is negative. Then, we discuss the previous VIX index changes was extreme or normal. we find that the effect of the previous VIX index change was greater than the extreme value. And in high quantile, the return of the previous VIX index change which is positive was greater than the negative one. In low quantile, the return of the previous VIX index change which is positive was less than the negative one. In the end, we analyze the impact of the VIX index when specific events occur, and empirically show that the effect of VIX index changes is significant with non-economic events but it’s not significant with economic events.
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Pan, Li-Jung, and 潘麗容. "The impact of volatility Index on investors\'\'sentiment." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5457018%22.&searchmode=basic.
Full textAbstract:
碩士國立中興大學
高階經理人碩士在職專班
107
The impact of volatility index on the investor sentiment is an important empirical issue in recent market research. Many researchers pay a lot attention on this issue and provide evidence to show that volatility index affect the investor sentiment. In addition, they also show that the influence may affect the investors’ trading behavior in stock market. In this paper, we utilize the volatility index, VIX, to analyze its impact on proxy variables of investor sentiment, which include bid-ask spread, total market turnover, relative strength index (RSI), and liquidity index. Based on the summary statistics and regression analyses, we have the following important findings. First, the volatility index reflects the status of investors’ panic and affects the investors’ investment decisions. Second, there exist significantly negative impact of VIX on the S&P 500 index return. This implies that the volatility index could affect the movement of the market index. Third, the relationship between VIX and market turnover is significantly positive, which implies that investors increase their trading volume when they feel more panic. Finally, VIX also has a significantly negative impact on individual stocks in S&P500 index. We conclude that the volatility index has significant effect not only on the broad market index but also on individual stocks in the index. Investors can refer the change of the volatility index to their trading decision.
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TZU-LING, CHEN, and 陳姿伶. "The study of volatility on Taiwan index options." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/82425527122849490024.
Full textAbstract:
碩士國立彰化師範大學
商業教育學系
91
The influence option pricing factor has the stock price, strike price, the dividend yield, maturity date, the volatility and the market rate , among them, also the volatility most unpredictable. In tradition theoretically, the volatility is fixed but the volatility will change in time-series. Therefore, this study is to compare volatility forecasting power of option pricing. We use three methods to compare that are historic volatility, implied volatility and GARCH model. This paper will employ MAE and MAPE as our standard evaluating principles. The findings that, First, the forecast ability of either historic volatility, implied volatility or GARCH model to option pricing that regardless which of these following principles are employed (MAE or MAPE), the implied volatility model is the best. Second, the model price with implied volatility is closer to market price. Third, the influence option pricing factor has the in-the-money , out-the-money and the volatility.
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Hwu, Chau-Yun, and 胡僑芸. "TAIFEX OPTION VOLATILITY INDEX and TRANSACTION STRATEGY ANALYSIS." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/81745739680333240664.
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Chen, Min-hwa, and 陳敏華. "The relationships between volatility index and stock markets." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/40600666654358860990.
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碩士國立高雄第一科技大學
金融研究所
102
This paper analyzes the relationships of the VIX and DOW JONES, S&;P500, FTSE, CAC40, DAX stock markets from January, 2008 to June, 2013, using Unit root test, Vector auto regression, Granger causality test, forecast error variance decomposition model and impulse response function. The result shows that the VIX index has an adverse impact on other Europe and United States index. When the market is active, VIX index will decrease. In contract, VIX will increase when the market goes down.