One of the most popular investment and trading strategies over the last decade, has been the currency carry trade, which allows traders and investors to buy high-yielding currencies in the Foreign Exchange spot market by borrowing, low or zero interest rate currencies in the form of pairs, such as the Australian Dollar/Japanese Yen (AUD/JPY), with the purpose of investing the proceeds afterwards into fixed-income securities.To be able to determine the causality between the returns of equity markets and the foreign exchange market, we choose to observe the sensitivity and influence of two equity indexes on several pairs involved in carry trading. The reason for studying these relationships is to further explain the causes of the uncovered interest parity puzzle, thus adding our contribution to the academic field through this thesis.To accomplish our goals, data was gathered for daily quotes of 16 different currency pairs, grouped by interest differentials, and two equity indexes, the S&P 500 and FTSE All-World, along with data for the VIX volatility index, for the 2003-2012 period. The data was collected from Thomson Reuters Datastream and the selected ten year span was divided into three different periods. This was done in order to discover the differences on how equity indexes relate to typical carry trade currency pairs, depending on market developments before, during and after the world financial crisis.The tests conducted on the collected data measured the correlations, influences and sensitivity for the 16 different currency pairs with the S&P 500 Index, the FTSE All-World index, and the volatility index between the years of 2003-2012. For influences and sensitivity, we performed Maximum Likelihood (ML) regressions with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) [1,1], in Eviews software.After analyzing the results, we found that, during our chosen time period, the majority of currency pair daily returns are positively correlated with the equity indexes and that the FX pairs show greater correlation with the FTSE All-World, than with the S&P 500. Factors such as the interest rate of a currency and the choice of funding currency played an important role in the foreign exchange markets, during the ten year time span, for every yield group of FX pairs.Regarding the influence and sensitivity between currency pairs and the S&P 500 with its VIX index, we found that our models explanatory power seems to be stronger when the interest rate differential between the currency pairs is smaller. Our regression analysis also uncovered that the characteristics of an individual currency can show noticeable effects for the relationship between its pair and the two indexes.

Tato práce se zabývá návrhem, realizací a optimalizací systému určenímu k obchodování na finančních trzích, konkrétně s technologickými firmami trhu NASDAQ. K tomuto účelu jsou využívány technické indicatory a hlavně neuronových sítí. Vlastní řešení je pak realizováno v prostředi MATLAB.

In this paper, the price fluctuations of Bitcoin under speculative environment is studied. It has been seen that the market trend points out an existence of a speculative bubble. Over the course of the period from 2014 to 2018, the trend in price movements of bitcoin has proved to be strongly speculative. In that regard, investors might be curious about what drivers might be instrumental in these speculative price changes.  After reviewing of NPV, it was seen that NPV is not applicable to the case of cryptocurrencies due to their nature and lack of free cash flows to base the asset valuation to some fundamental facts. Later, LPPL model is reviewed, however, that also proved to be insufficient since it does not reflect the investor speculations and inform much about price dynamics regarding behavioral finance principles. Then, some papers from the past price fluctuations of bitcoin (for the period from 2010 to 2013) was reviewed and three key variables were determined which might explain price movements. Public interest towards Bitcoin as interest-driven, regulatory and political news about cryptocurrencies as event-driven and VIX as overall investor approach to Bitcoin market have been taken. After running regressions, the only significant variable happened to be public interest and popularity of Bitcoin. Although, for some cases, VIX variable also explain price fluctuations for some intervals, in none of the cases event-driven variable has long- terms effect on price fluctuations under speculative environment. Lastly, a robustness test is also handled considering the “weekend effect” and it has been seen public interest variable again proved to be a significant price determinant.

波動度對於金融市場影響甚多，同時為金融資產定價的重要參數以及市場穩 定度的衡量指標，尤其在金融危機發生時，波動度指數的驟升反映資產價格震盪。 本篇論文嘗試捕捉S&P500 指數實現波動度與VIX變動率未來之動態，並將VIX、 VIX 選擇權與VVIX 納入預測模型中，探討其資訊內涵。透過研究S&P500 指數 實現波動度，能夠預測S&P500 指數未來之波動度與報酬，除了能夠觀察市場變 動，亦能使未來選擇權定價更為準確；而藉由模型預測VIX，能夠藉由VIX 選 擇權或VIX 期貨，提供避險或投資之依據。文章採用2006 年至2011 年之S&P500 指數、VIX、VIX 選擇權與VVIX 資料。 在 S&P500 指數之實現波動度預測當中，本篇論文的模型改良自先前文獻， 結合實現波動度、隱含波動度與S&P500 指數選擇權之風險中立偏態，所構成之 異質自我回歸模型(HAR-RV-IV-SK model)。論文額外加入VIX 變動率以及VIX指數選擇權之風險中立偏態作為模型因子，預測未來S&P500 指數實現波動度。 研究結果表示，加入VIX 變動率作為S&P500 指數實現波動度預測模型變數後， 可增加S&P500 指數實現波動度預測模型之準確性。 在 VIX 變動率預測模型之中，論文採用動態轉換模型，作為高低波動度之 下，區分預測模型的方法。以VIX 過去的變動率、VIX 選擇權之風險中立動差 以及VIX 之波動度指數(VVIX)作為變數，預測未來VIX 變動率。結果顯示動態 轉換模型能夠提升VIX 預測模型的解釋能力，並且在動態轉換模型下，VVIX 與 VIX 選擇權之風險中立動差，對於VIX 預測具有相當之資訊隱涵於其中。 / This paper tries to capture the future dynamic of S&P 500 index realized volatility and VIX. We add the VIX change rate and the risk neutral skewness of VIX options into the Heterogeneous Autoregressive model of Realized Volatility, Implied Volatility and Skewness (HAR-RV-IV-SK) model to forecast the S&P 500 realized volatility. Also, this paper uses the regime switching model and joins the VIX, risk neutral moments of VIX options and VVIX variables to raise the explanatory ability in the VIX forecasting. The result shows that the VIX change rate has additional information on the S&P 500 realized volatility. By using the regime switching model, the VVIX and the risk neutral moments of VIX options variables have information contents in VIX forecasting. These models can be used for hedging or investment purposes.

本系統係藉由分析歷史交易資料來預測S&P500期貨市場之漲幅。 我們改進了Tsaih et al. (1998)提出的混和式AI系統。 該系統結合了Rule Base 系統以及類神經網路作為其預測之機制。我們針對該系統在以下幾點進行改善：(1) 將原本的日期資料改為使用分鐘資料作為輸入。(2) 本研究採用了“移動視窗”的技術，在移動視窗的概念下，每一個視窗我們希望能夠在60分鐘內訓練完成。(3)在擴增了額外的變數 – VIX價格做為系統的輸入。(4) 由於運算量上升，因此本研究利用TensorFlow 以及GPU運算來改進系統之運作效能。 我們發現VIX變數確實可以改善系統之預測精準度，但訓練的時間雖然平均低於60分鐘，但仍有部分視窗的時間會小幅超過60分鐘。 / The system is made to predict the Futures’ trend through analyzing the transaction data in the past, and gives advices to the investors who are hesitating to make decisions. We improved the system proposed by Tsaih et al. (1998), which was called hybrid AI system. It was combined with rule-based system and artificial neural network system, which can give suggestions depends on the past data. We improved the hybrid system with the following aspects: (1) The index data are changed from daily-based in into the minute-based in this study. (2) The “moving-window” mechanism is adopted in this study. For each window, we hope we can finish training in 60 minutes. (3) There is one extra variable VIX, which is calculated by the VIX in this study. (4) Due to the more computation demand, TensorFlow and GPU computing is applied in our system. We discover that the VIX can obviously has positively influence of the predicting performance of our proposed system. The average training time is lower than 60 minutes, however, some of the windows still cost more than 60 minutes to train.

Special Purpose acquisition company (SPAC) är ett skalbolag grundat av investerare med det enda syftet att samla kapital genom en börsintroduktion för att senare förvärva ett annat bolag. Tidigare empirisk forskning för SPAC har fokuserat på egenskaperna hos SPAC bolagen samt deras långsiktiga och kortsiktiga avkastning. Denna studie tar en kvantitativ ansats och syftar till att undersöka vilka underliggande makroekonomiska faktorer har bidragit till en ökning i antalet SPAC bolag. Detta undersöks genom en linjär regressionsanalys där studien undersöker korrelationen mellan olika variabler. Studien syftar även till att se om de underliggande makroekonomiska faktorer bakom ökningen av traditionella börsintroduktioner är detsamma som de bakom SPAC. All data för SPAC, makroekonomiska variabler och börsintroduktioner har samlats in genom olika databaser som Yahoo Finance och Nasdaq. Studiens resultat visar på att vissa makroekonomiska variabler som testats för en korrelation har en påverkan på antalet SPAC och börsintroduktioner medans vissa variabler inte bevisade någon påverkan. Variabeln som visade starkast korrelation med antalet SPAC är den generella marknadens avkastning där det är tydligt att om den generella marknaden är stark så är viljan att göra en börsnotering större Studien fann även att de makroekonomiska variabler bakom ökningen av antalet SPAC och vanliga börsnoteringar inte skiljer sig åt. / This paper is written in Swedish. A special purpose acquisition company (SPAC) is a shell company set up by investors with the sole purpose of raising money through an IPO to eventually acquire another company. Past empirical research on Special purpose acquisition companies has mostly focused on the characteristics of the SPACs and long term returns as well as the short term returns. This study takes a quantitative approach and aims to examine whether underlying macroeconomic factors have contributed to the increase in SPAC companies. This is done by a linear regression analysis that tests for correlations between different variables. The study also aims to see if the underlying macroeconomic factors behind the increase of traditional IPOs are the same as those behind SPACs. All data regarding SPACs, IPOs and the macroeconomic factors were collected from different databases and websites such as Yahoo finance and NASDAQ. The results from this study show that some of the macroeconomic factors which were tested for a correlation indeed have a positive impact on the number of SPACs and IPOs and some did not. The variable that showed the strongest correlation was the performance of the general market and it is clear that when the general market is strong the willingness to go public increases. The study also finds that the macroeconomic variables behind the increase in the number of SPACs and IPOs do not differ.

本文考量波動度不對稱效果(Volatility Asymmetric Effect)與均數回歸(Mean Reverting)兩個特性，並考量台股市場特性，嘗試建立一個適合台灣市場的波動度交易策略。利用GARCH(1,1)波動度與VIX指標建構第一個交易訊號，並建立當日沖銷部位。以賺取日內行情為出發點，利用時間序列模型捕捉波動度的高估或低估且搭配純跨式(Pure Straddle)策略或根據Delta調整後的跨式(Adjusted Straddle)策略。第二個交易訊號則是利用市場敏感指標，觀察外資與自營商在交易部位與未平倉部位的變化，找出對於波動度的影響。建立由選擇權與期貨組成的Delta-Hedged部位，藉由觀察市場上主力籌碼的變化，動態調整部位契約，尋找波段之間的獲利機會。 實証部分以期交所公布的每日交易資料與VIX日資料，利用2007至2008兩年的歷史資料，估計參數與測試交易訊號。樣本外期間為2009年1月開始至3月結束共55個交易日。考量交易成本後，兩個不同型態的交易訊號，仍然能夠藉由本研究的策略，獲得正的報酬。本文認為台灣為一個淺碟市場，過度反應資訊的特性，讓波動度策略出現獲利的機會。藉由這個波動度交易系統的研究，除了讓資金豐沛的機構投資人使用外，也能夠讓一般投資大眾建立自己的波動度交易策略 關鍵字:波動度交易，選擇權交易策略，GARCH(1,1)，VIX，市場情緒指標 / Trying to apply a preliminary study of volatility trading strategies in Taiwan derivative market is the topic of this dissertation. Capturing the market movement or even the dynamic of underlying asset is a Pandora’s Box for academic researchers and industry participants. Mean-reverting and asymmetrical effects are the two special characteristics of volatility for us to design our trading system according to the previous empirical studies. In our study, we use different type of volatility signal to capture the trading opportunities. Use the new released information form TAIFEX including VIX and Position Structure of Institutional Traders to design our signal. We apply the idea to use pure option position and delta-hedged position as our trading tools in this volatility trading system and look for the opportunities between realized volatility and implied volatility. An over-reaction may rises the uncertainty and also lead the market volatility change coherently. We use history data from 2007 to 2008 test our trading signal and parameters. The out sample period is from 2009 January to 2009 March which has 55 trading days to simulate our strategies. In the end, we see a positive result in both trading signals which earns positive return after considering the trading cost. Key words: Volatility Trading, Market Sentiment Indices, Option Strategies, VIX, GARCH(1,1)