<p>As mentioned in Herr K's comment, asset pricing is the theory to price assets (such as equity, bonds, options, futures, swaps, etc). For this, you can use models like CAPM/Fama-French (returns), Black-Scholes (options), Swensonn (interest rates), and many many others. When you say empirical asset pricing, this means that you go to the data (and each model deals with a different type of data) and try to price an existing asset based on the information that you see on the market. </p>

<p>For example, in the Black-Scholes model, given the current stock price (S), the strike price for the option (K), the time until exercise (T-t), the risk-free rate (r) and the volatility of returns (sigma), you can use the formula directly and calculate the price of the option. Notice that this would be a theoretical price. In practice, you might observe something else in the market. You may want to do some arbitrage from here since there is mispricing (Don't go using this in the real market, but you get my point).</p>

<p>For your last question, asset pricing is not necessarily just about the current value of an asset. You probably would also want to estimate future values based on your models. In fact, it is really of no use a model that tells you today's price. For that, you just look at the price on the tape and ignore your model. The whole point of building models is to make predictions. </p>

CC BY-SA 3.0
2017-11-07T12:14:09.370Z
Thanks for the response. I also had a couple of questions on empirical asset pricing. What tools are used? What kind of books(which are good) do you recommend?  As I understood, the theory is on one other side, through which applications on data are tested. On the other hand, some theories are useless.
Kassak
CC BY-SA 3.0
2017-11-07T13:19:56.253Z
If possible, do you know why summary statistics is used a lot in empirical asset pricing?
Kassak
CC BY-SA 3.0
2017-11-07T15:08:54.377Z
Well, as for tools, I would say you'd have to choose a programming language to run your models. But you do have to know the (theoretical) models if you are going to program them. I would recommend that you study Hull's book, 'Options, Futures and other Derivatives' and also look at something much more programming-like such as Yves Hilpisch's 'Python for Finance'. They don't cover every asset pricing theory available, but they are good starts.
python_enthusiast
CC BY-SA 3.0
2017-11-07T15:08:58.523Z
Lastly, you use summary statistical a lot in any kind of empirical study. This is always the first step when analysing data. The reason for this is that you want to know your data as much as possible before doing any kind of analysis/ model fitting.
python_enthusiast
CC BY-SA 3.0
2017-11-07T04:48:53.067Z
2017-11-07T04:48:53.067Z
python_enthusiast
<p>I get confused on what is the difference between asset pricing vs empirical asset pricing? Could you clarify your answer? Also, is asset pricing just assessing the current value of an asset?</p>

<p>Thanks,</p>

CC BY-SA 3.0
2017-11-07T04:20:57.677Z
The former is theory and the latter empirical tests/applications of the theory?
Herr K.
<asset-pricing>
Asset pricing vs Empirical asset pricing
Kassak
# Asset pricing vs Empirical asset pricing

(Asked by: username_0 on 2017-11-07)

I get confused on what is the difference between asset pricing vs empirical asset pricing? Could you clarify your answer? Also, is asset pricing just assessing the current value of an asset?

Thanks,

**Tags:** asset-pricing
> **Comments:**
> **username_1**: The former is theory and the latter empirical tests/applications of the theory?  

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## 1 answers

### Answer by username_2 on 2017-11-07. Score: 3 | Accepted answer

As mentioned in Herr K's comment, asset pricing is the theory to price assets (such as equity, bonds, options, futures, swaps, etc). For this, you can use models like CAPM/Fama-French (returns), Black-Scholes (options), Swensonn (interest rates), and many many others. When you say empirical asset pricing, this means that you go to the data (and each model deals with a different type of data) and try to price an existing asset based on the information that you see on the market.

For example, in the Black-Scholes model, given the current stock price (S), the strike price for the option (K), the time until exercise (T-t), the risk-free rate (r) and the volatility of returns (sigma), you can use the formula directly and calculate the price of the option. Notice that this would be a theoretical price. In practice, you might observe something else in the market. You may want to do some arbitrage from here since there is mispricing (Don't go using this in the real market, but you get my point).

For your last question, asset pricing is not necessarily just about the current value of an asset. You probably would also want to estimate future values based on your models. In fact, it is really of no use a model that tells you today's price. For that, you just look at the price on the tape and ignore your model. The whole point of building models is to make predictions.

> **Comments:**
> **username_0**: Thanks for the response. I also had a couple of questions on empirical asset pricing. What tools are used? What kind of books(which are good) do you recommend?  As I understood, the theory is on one other side, through which applications on data are tested. On the other hand, some theories are useless.  
> **username_0**: If possible, do you know why summary statistics is used a lot in empirical asset pricing?  
> **username_2**: Well, as for tools, I would say you'd have to choose a programming language to run your models. But you do have to know the (theoretical) models if you are going to program them. I would recommend that you study Hull's book, 'Options, Futures and other Derivatives' and also look at something much more programming-like such as Yves Hilpisch's 'Python for Finance'. They don't cover every asset pricing theory available, but they are good starts.  
> **username_2**: Lastly, you use summary statistical a lot in any kind of empirical study. This is always the first step when analysing data. The reason for this is that you want to know your data as much as possible before doing any kind of analysis/ model fitting.  

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