<p>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</p><p>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</p><p>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.</p><p>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</p><p>well. I conclude that the VIX estimation can be used as a reliable estimator of market volatility.</p>

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.

No / This 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.

This study examines the Chicago Board Option Exchange (CBOE) Volatility Index (VIX) which is the implied volatility calculated from short-term option prices on the Standards & Poor’s 500 stock index (S&P 500). Findings suggest VIX overestimates average volatility by approximately 3% but explains 55% of S&P 500’s proceeding month’s volatility. The implied volatility (IV) from options on the VIX add additional explanatory power for the S&P’s 500 proceeding kurtosis values (a measure of tail risk). The VIX option’s volatility smirks did not add additional explanatory power for explaining the S&P 500 volatility or kurtosis. A simple trading rule based on buying the S&P 500 whether the VIX, IV from the options on the VIX, and the VIX option’s volatility smirk decline over the preceding month results in an additional 0.96% return in the following month. However, this only occurs approximately 10% of the time and does not outperform a simple buy-and-hold strategy as the strategy has the investor out of the market the majority of the time.

2015年下半年開始，許多有關市場黑天鵝的新聞佈滿各大媒體版面，其中不乏「某恐慌指標創歷史新高」此類令投資人恐懼的標題。然事實上卻未見到各國股價指數有大幅修正的現象，以MSCI全球指數而言，下半年總計僅修正6.49%。為了探討這些不同於傳統VIX指數的恐慌指標是否會顯著影響股價指數的表現。本論文透過VAR、VECM以及ARDL模型，探討金價油價比、CBOE偏態指數、瑞士信貸CSFB指數以及泰德價差這四種恐慌指標對於當前全球前四大經濟體股價指數的關聯性。 美國是全世界經濟的領頭羊，其經濟情勢與全球每一個國家的榮景息息相關，美國股價指數的表現亦是相當受到全球投資人所關注的。故本論文首先透過探討這四種恐慌指標對於S&P 500指數的影響，再利用S&P 500指數領先各國股價指數的特性進一步得出結論。實證結果發現，S&P 500指數對於其他三個股價指數確實具有短期同向的影響，長期而言亦具有穩定的線性關係。另外，金價油價比無論在短期及長期下皆無法有效代理市場的恐慌程度而影響S&P 500指數；CBOE偏態指數與瑞士信貸CSFB指數在長期下得以領先S&P 500指數的變化，當該二指數走高，代表 S&P 500指數在近期的波段高點可能即將來臨，亦即隱含該二指數對於S&P 500指數具有領先同向變化的現象；泰德價差為市場用以衡量信用風險的指標之一，當泰德價差擴大，隱含市場風險貼水增加，不利股市發展，其與S&P 500指數則具有長期穩定的負向關係。本論文最後也針對這四種恐慌指標的預測能力進行探討，發現瑞士信貸CSFB指數在預測S&P 500指數的能力上，相對其他三種恐慌指標優異。 / There were so many hearsays about the potential black swan events dominating the news in the second half of 2015. Headlines were about some fear indicators hit historic high but, in realistic, world stock market did not be significantly influenced under this panic atmosphere. Take MSCI World Index for instance, the index dropped only 6.49% in the second half of 2015, which was relatively unreasonable under this condition. In order to find out whether or not the fluctuations of these fear indicators can significantly affect stock indexes, VAR, VAEM and ARDL model to discuss the relationships between 4 fear indicators and 4 stock indexes─gold to oil ratio, CBOE Skew Index, Credit Suisse Fear Barometer Index, TED spread, S&P 500 Index, MSCI Europe Index, SSE A Share Index and Nikkei 225 Index are adopted in this study. Global investors pay close attention to the performance of the U.S. Stock indexes as U.S. economy condition can affect the economies of the rest of the world. Consequently, we investigated the effects of 4 fear indicators to the S&P 500 Index then employed relationships between S&P 500 Index and other 3 stock indexes to do further discussion. The results show S&P 500 positively affects the performances of other 3 stock indexes in short term and has a steady relationship with each of them respectively in the long term. The changes of gold to oil ratio could not significantly influence the performance of S&P 500 Index no matter in the short term or the long term. CBOE Skew Index and CSFB Index have significant positive influences on S&P 500 and are leading indicators to S&P 500 Index. Lastly, TED spread has a steady negative relationship with S&P 500 in long term, and CSFB Index has the highest predictive power among the 4 fear indicators.

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.