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Modeling The Return of Stock – A Derivative Pricing Approach Experimental Data Report

Modeling The Return of Stock – A Derivative Pricing Approach Experimental Data Report. 指導教授： 戴天時 交大財金 周立軒. Outline. Model Review Settings of Experiment Problems Result. Model Review.

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Modeling The Return of Stock – A Derivative Pricing Approach Experimental Data Report

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  1. Modeling The Return of Stock – A Derivative Pricing ApproachExperimental Data Report 指導教授：戴天時 交大財金 周立軒

  2. Outline • Model Review • Settings of Experiment • Problems • Result

  3. Model Review • Our target is to use the asset-pricing model to generate the implied asset return, and furthermore, to get the implied stock return • Because the data we use as the input to the models are partially from option market, which contains the information of expectation of the market. So, we believe our method can capture the trend of market.

  4. 股權（每日在外流通股數，股價，股利，及隱含波動度）、公司債務、無風險利率股權（每日在外流通股數，股價，股利，及隱含波動度）、公司債務、無風險利率 公司債（每日交易資料、發行價、債息） 公司股價隱含報酬 公司資產價值及資產波動度 公司資產隱含報酬 Model Review Merton Model, FPM Implied Return Formula

  5. Settings of Experiment • 1. Data Selection

  6. Settings of Experiment • 2. Duration • From 2008/5/20~2011/6/14, 800 Trading days • 3. Parameters • Dividend: The PV of the summation of 1-year • Interest: Continuously Payout

  7. Problem • P1: We don’t know the future • According to the settings, we must know the information about dividend policy for 1-year • The duration of Outstanding Shares data in CRSP is end in 2010/12/31 • Solution: • For Outstanding Shares in 2011, we use the number in 2010/12/31 as proxy • For dividend policy, we use the prediction in FT.com

  8. Problem • P2: Bond Data • The bond data preserved in DataStream Database in NTHU is unavailable partially ( Issue Date After 2000) • We don’t actually know about the source of daily price in DataStream • Solution: • Now we are trying to use the data downloaded from TRACE via WRDS.

  9. Problem • P3: Stock Volatility • Here is the definition of implied volatility in DataStream • Datatypes "VL" and "VI" are different. Here are their definitions: • VL (Implied Volatility) • The market’s current estimate of future volatility. Implied volatility is availble for individual option series only. Forecast volatility is the average of implied volatility and historical volatility, used in the options continuous series. • VI (Implied Volatility at-the-money (ATM) interpolated) • The nearest two options series at-the-money are used: One above and one below the underlying price. For example, if the underlying is 655 and the two closest ATM strikes are 650 and 700, the implied volatility of the 650 strike will be weighted 45/50 against the implied volatility 700 strike which is weighted 5/50 • Solution: • We use VI now.

  10. Result

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