The purpose of this thesis was to test the hypothesis that multiple-listing of equity options leads to lower bid-ask spreads because of increased competition. This competition can come in two forms, actual or potential, both of which are theorized to have the same effect on spreads. A model of the determinants of the bid-ask spread was formulated. Separate tests were conducted on 1985 and on 1992 CBOE data. The first test arose from the fact that in 1985, only a certain number of options were multiple-listed, or eligible to be multiple-listed. Spreads for multiple-listed options were conjectured to be below spreads for single-listed options across low levels of volume, and equal to single-listed option spreads at higher levels of volume. The evidence for this was mixed based on several regressions with different functional forms. The second test arose from the fact that in 1992, because of an SEC rule change, all options were eligible to be multiple-listed but still only a few were. Spreads for multiple-listed options were conjectured to be equal to spreads for single-listed options because the single-listed options had the potential to become multiple-listed. Again, the evidence for this was mixed. It appears that the actual and potential competitive effects from multiple-listing had yet to come to fruition as of 1992. Further testing revealed that, on an option-by-option basis, spreads generally rose from 1985 to 1992. / Master of Arts

This paper measures the channels by which private information is incorporated in prices in the equity and option markets. Using a mispricing events approach and conditioning on the option market being the cause of the mispricing event, I analyse the subsequent behaviour of both the options and equity markets and I find that options markets play an important role in the price discovery process. When conditioning on option caused mispricing events, the equity price adjusts towards the options price to reconcile the prices. I find that around 40% of the option caused mispricing events contain information, and the equity prices adjust 35-40%, depending on the exchange, of the maximum discrepancy before prices reconcile. When the equity market causes the mispricing, the option market follows due to the autoquote mechanism. Additionally, I use Monte Carlo to assess the suitability of the Hasbrouck (1995) Information Share and Gonzalo-Granger (1995) Component Share measures in the option-equity context. I find that neither metric is suitable, however the Putnins (2013) Information Leadership metric is and the options market has on average a 35% information leadership share.

This paper analyses options/warrants price behavior around an ex-dividend day of underlying shares. Both equity options as financial instruments traded on options exchanges, and warrants/certificates as OTC financial instruments are analyzed. First, the paper analyzes the ex-dividend day share price drop. Findings of this part are further used to analyze the impact of unexpected share price decline on options prices. Further, the paper focuses on volumes of traded options contracts and changes in options prices around the ex-dividend day. The paper focuses on European shares and related options and warrants. The options data was collected from the options exchange EUREX and also from several OTC sources -- Vontobel, Lang & Schwarz, Erste, and xMarkets by Deutsche Börse. The main aim of the paper is to identify market inefficiencies in trading in and valuation of equity options. There are two main conclusions that around the ex-dividend day there is a significantly increased trading activity and the call options depreciate whereas put options appreciate between the cum-dividend and the ex-dividend day. This shows insufficient implementation of the share price drops into options valuation models of options dealers or investors / speculators. Further an impact of unexpected share price behavior was analyzed but no particular pattern has been identified. The impact of the unexpected share price drop (either too high or too low) has ambiguous implications on the options prices. Finally, ways how to utilize on knowledge of inefficient trading in options around the ex-dividend day were suggested. The suggestions were done both from the perspective of an investor / speculator and of an options dealer.

Synthetic short positions constructed by equity options and stock loan short sells are linked by arbitrage. This thesis analyses the link by considering the implied volatility surface (IVS) at 80%, 100%, and 120% moneyness, and stock loan variables such as benchmark rate (rt), utilization, short interest, and transaction trends to inspect time-dependent structures between the two assets. By applying multiple multivariate time-series analyses in terms of vector autoregression (VAR) and the recurrent neural networks long short-term memory (LSTM) and gated recurrent units (GRU) with a sliding window methodology. This thesis discovers linear and complex relationships between the IVS and stock loan data. The three-day-ahead out-of-sample LSTM forecast of IV at 80% moneyness improved by including lagged values of rt and yielded 19.6% MAPE and forecasted correct direction 81.1% of samples. The corresponding 100% moneyness GRU forecast was also improved by including stock loan data, at 10.8% MAPE and correct directions for 60.0% of samples. The 120% moneyness VAR forecast did not improve with stock loan data at 26.5% MAPE and correct directions for 66.2% samples. The one-month-ahead rt VAR forecast improved by including a lagged IVS, at 25.5% MAPE and 63.6% correct directions. The presented data was optimal for each target variable, showing that the application of LSTM and GRU was justified. These results indicate that considering stock loan data when forecasting IVS for 80% and 100% moneyness is advised to gain exploitable insights for short-term positions. They are further validated since the different models yielded parallel inferences. Similar analysis with other equity is advised to gain insights into the relationship and improve such forecasts. / Syntetiska kortpositioner konstruerade av aktieoptioner och blankning med aktielån är kopplade med arbitrage. Denna tes analyserar kopplingen genom att överväga den implicerade volatilitetsytan vid 80%, 100% och 120% moneyness och aktielånvariabler såsom referensränta rt, låneutnyttjande, låneintresse, och transaktionstrender för att granska tidsberoende strukturer mellan de två tillgångarna. Genom att tillämpa multipel multidimensionell tidsserieanalys såsom vektorautoregression (VAR) och de rekursiva neurala nätverken long short-term memory (LSTM) och gated recurrent units (GRU). Tesen upptäcker linjära och komplexa samband mellan implicerade volatilitetsytor och aktielånedata. Tre dagars LSTM-prognos av implicerade volatiliteten vid 80% moneyness förbättrades genom att inkludera fördröjda värden av rt och gav 19,6% MAPE och prognostiserade korrekt riktning för 81,1% av prover. Motsvarande 100% moneyness GRU-prognos förbättrades också genom att inkludera aktielånedata, resulterande i 10,8% MAPE och korrekt riktning för 60,0% av prover. VAR-prognosen för 120% moneyness förbättrades inte med alternativa data på 26,5% MAPE och korrekt riktning för 66,2% av prover. En månads VAR-prognos för rt förbättrades genom att inkludera en fördröjd implicerad volatilitetsyta, resulterande i 25,5% MAPE och 63,6% korrekta riktningar. Presenterad statistik var optimala för dessa variabler, vilket visar att tillämpningen av LSTM och GRU var motiverad. Därav rekommenderas det att inkludera aktielånedata för prognostisering av implicerade volatilitetsytor för 80% och 100% moneyness, speciellt för kortsiktiga positioner. Resultaten valideras ytterligare eftersom de olika modellerna gav dylika slutsatser. Liknande analys med andra aktier är rekommenderat för att få insikter i förhållandet och förbättra sådana prognoser.