Stock Market Tools

Today we look at the Optiver Realized Volatility Kaggle Challenge and the role of market makers in financial markets. On this channel, over the next two months we will be completing this competition and attempting to provide an accurate methodology for predicting realized volatility over a 10-minute trading window. In this first video, first we look at some advice from Robbert Pullen (an Optiver Trainer) with regards to financial markets and marker making. The complete video link is provided below the video timeline, this was created by @Optiver Europe. We will then consider how we can place option prices in the marketplace into perspective, are they high or are they low? One visual method of considering our option price marketplace is to create a graph of a Historical Volatility Cone and compare this to Implied Volatility in financial markets (offered by Market Makers). I have used the same methodology as provided by a link in StackExchange (link below), and have replicated this for the ASX200 index. Full code available on my website: https://quantpy.com.au/black-scholes-model/historical-volatility-cones/ 00:00 Intro 01:06 Optiver – what are Market Makers? 05:51 Market Makers Profitability 07:43 Cheap or Expensive Options? 08:22 ASX200 Index || Creating the Historical Volatility Cone 12:10 ASX200 Index || Adding Implied Volatility of Call/Put Bid & Asks 15:40 Where does realized volatility come into it? Quantitative Finance StackExchange Question: https://quant.stackexchange.com/questions/1284/how-do-i-compare-implied-and-historic-volatility Introduction to financial markets and instruments - Kaggle edition: https://www.youtube.com/channel/UCLRlRArEpJUc6fdGfeokVWg Optiver Realized Volatility Kaggle Challenge: https://www.kaggle.com/c/optiver-realized-volatility-prediction/overview ★ A data driven path to getting a job in Quant Finance https://www.quantpykit.com/ ★ QuantPy GitHub Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise.

Today we look at the volatility smile or volatility smirk that arises for option prices in financial markets. We will also take a look at a real life example using the ASX200 index where we can potentially profit from the traditional option theory of Put-Call Parity. 00:00 Intro 00:27 Volatility Smile || Horizontal Skew 00:40 Volatility Smile || Vertical Skew 02:30 Put-Call Parity || Can you beat the Market Maker? 03:25 Put-Call Parity || ASX200 Index Order Book 04:12 Put-Call Parity || Can I profit? 05:05 Put-Call Parity || Short Call, Long Put 06:00 Put-Call Parity || Short Put, Long Call 06:50 The Market Maker Advantage Introduction to financial markets and instruments - Kaggle edition: https://www.youtube.com/channel/UCLRlRArEpJUc6fdGfeokVWg Optiver Realized Volatility Kaggle Challenge: https://www.kaggle.com/c/optiver-realized-volatility-prediction/overview ★ A data driven path to getting a job in Quant Finance https://www.quantpykit.com/ ★ QuantPy GitHub Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise.

Today we look at hedging options from a quant’s perspective. In this video we look at the difference in Profit and Loss (P&L) with three different strategies: dynamic delta hedging, static delta hedging and no delta hedging. Delta hedging is a way to reduce directional risk of the underlying to your options positions by transacting in the money markets (bank account) and the underlying (stocks/futures/etfs/index). By continually adjusting in the underlying and bank account, we can effectively replicate the changes in payoff of the ‘new’ option contract. Essentially instead of betting on the direction at one time spot (on entry) we are now making a series of bets at different levels. Hopefully in this video the importance and relevance of realized volatility becomes apparent and hence why market marking firms like Optiver are so keen on forecasting realized volatility as accurately as possible. ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2021/003%20Jul-Sep/2021-08-03%20What%20is%20Delta%20Hedging%20_%20Dynamic%20Delta%20Hedging%20like%20a%20Quant%20_%20Profit%20_%20Loss%20Options%20Trading.ipynb 00:00 Intro 00:22 What is Delta Hedging? 01:40 Importance of Realized Volatility 02:00 Real world examples 03:56 Full worked example: Short CBA Nov 102 Call 06:40 Looking at P&L over 1000 trades 07:45 P&L distributions for different hedging strategies Introduction to financial markets and instruments - Kaggle edition: https://www.youtube.com/channel/UCLRlRArEpJUc6fdGfeokVWg Optiver Realized Volatility Kaggle Challenge: https://www.kaggle.com/c/optiver-realized-volatility-prediction/overview ★ A data driven path to getting a job in Quant Finance https://www.quantpykit.com/ ★ QuantPy GitHub Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise.

The Wall Street Journal published an article on 31st October named ‘How Robinhood Cashes in on the Options Boom’, this inspired me to investigate Payment for Order Flow further. According to Bloomberg the 11 largest U.S. retail brokerages collected $2.2 billion for selling customers options orders. Payment for order flow (PFOF) is the compensation and benefit a brokerage firm receives for directing orders to different parties for trade execution (Investopedia, 2021). In this video we look at PFOF for Robinhood in Q3 2021 and the 4 largest brokerage firms in the world: TD Ameritrade, E Trade, Fidelity and Charles Schwab. Wall Street Journal Article: https://www.wsj.com/articles/how-robinhood-cashes-in-on-the-options-boom-11635681600?mod=Searchresults_pos6&page=1 Breaking Down the Payment for Order Flow Debate: https://a16z.com/2021/02/17/payment-for-order-flow/ ★ A data driven path to getting a job in Quant Finance https://www.quantpykit.com/ ★ QuantPy GitHub Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise.