8

With difficulty and high costs and secretively. Successful ones are the ones that are able to do it more cheaply. This is also the reason for their secretiveness: prices would go up. The costly but straightforward approach would be to buy equity index puts. However, I don't think anyone here can or will explain how you can tail hedge at scale significantly ...


6

Let me try to answer. I have worked at an Algo-trading firm that trades equities and have seen how trades are executed at the order book level. Let's say the price of the stock is 100 (last traded price). Let's say the order book is as follows: Bids: Bid1 = 99 (size = 10,000), Bid2 = 98 (size = 20,000), Bid3 = 97 (size = 25,000), Bid4 = 96 (size = 30,000), ...


4

The publication is made by the UK institute of actuaries so I'm answering from the perspective of the insurance industry in the European union. Is it allowed? For European Insurance companies EIOPA gathers a number of statistics, among which asset exposures. The table below shows figures as of 2019Q4: So, at least for insurance companies that are ...


4

First off, I agree with the comments and answers already here. "Simple" tail hedging is expensive in the long run and WILL lose you money. Best example is the CBOE Put Protection index (PPut). Even through COVID-19 it barely outperformed the SPX and that was the mother of all tail risks. In all other market phases you basically buy reduced ...


4

The merger of The Walt Disney Company and Twenty-First Century Fox led to a company temporarily called TWDC Holdco 613 Corp. This holding company is the New Disney; it's a distinct company from the Old Disney, aka TWDC Enterprises 18 Corp. Hence the new CIK.


3

Take the analogy of equations modelling something in physics. Just because you write down an equation, it does not mean it has to be connected to anything in reality. It only do so to the extent you have adapted the equation and it's parameters to fit reality. In finance things are a bit more complicated when it comes to the predicting power though. ...


3

The SDE you are describing is called the Geometric Brownian Motion. In the end its just a model, which underlies certain assumptions, which are usually not met in the real world scenarios. There are many further extensions and variation of SDEs for modelling prices f.e. including a jump component (jump diffusion models), mean reversion (f.e. Ornstein-...


2

In addition to regulatory considerations for investments, the presence or absence of equity risk depends on the products sold. Two products which expose life companies to substantial equity risk are Variable Annuities or even just standard Unit Linked policies. Variable Annuities provide guarantees on equity funds. So they obviously create equity risk. But ...


2

As long as you use the same observation period (month, year, or whatever you choose) for all components in the formula, you're good. So the three parts of it Correlation coefficient Standard deviation of the stock Standard deviation of the market all need to be calculated over the same period, e.g. a month or a year. So you could look at long term ...


2

I think your best shot is to share with us your 3,000 stocks. How far can that be from FF sample? As a quick check I took the 25 book-to-market portfolios and the Fama-French 3 factor model and run the standard fama macbeth regressions. First stage results: Only 6 alphas are statistically significant from zero (which is good news for the model). Second ...


2

I'd try Pandas DataReader library in Python (which has to be installed separately from Pandas): this library allows direct data feed from Google, Yahoo Finance or Morningstar fo equity data: Installation: pip install pandas_datareader Code: (I typed this just now, it's a very short code: in your case, you don't need time series, but a cross-section, so you ...


2

I’m by no means an “expert”, though I’ve spent a fair amount of time studying this and writing quant software. There are three important starting places to study this question, in this order: 1 dark pools ( see https://squeezemetrics.com/monitor/dix ) 40% to 60% of large trades are now done in dark pools. 2 the “closing auction” at 4pm 3 the “on balance ...


2

The drift in your code is: drift = (mu - 0.5 * sigma**2) * delta_t So I assume you are using the Geometric Brownian Motion to simulate your stock price, not just plain Brownian motion. Therefore your model is Lognormal, not Normal. Also, I assume that the time series that you're downloading is daily closing prices. The solution to the GBM model is the ...


2

The GBM model is liked by practitioners for the modelling of stock prices for the following reasons: (i) The solution is log-normal, so the stock price distribution varies between zero and infinity: which is what we would expect from a real-world stock price. (ii) The model has independent increments, which means the future distribution of the stock only ...


2

Taleb equates famous put sellers like Niederhoffer with all derivative traders who short derivatives, so does this guy with an axe to grind, https://steadyoptions.com/articles/how-victor-niederhoffer-blew-up-twice-r124/ They are ignorant of risk management a naked put need not be more risky than buying a share, but I am not in the business of educating. I ...


1

The WRDS/CRSP dataset has that data available. But you need to pay to access it: Historical constituents are in the (SAS) sets dsp500list and msp500list. It contains the start and ending date for each security (identified by PERMNO). Prior to March, 1957, the index contains 90 issues. Currently, they are only available in our UNIX server. According to CRSP ...


1

you should be buying and selling over whatever the prediction time interval was. So, if you're return prediction was positive and was defined as being over the period wednesday from 10:00 am to 4:00 pm , then you should be buying on wednesday at 10:00 am and selling at 4:00 pm. The prediction time interval is defined by the model one is using to predict the ...


1

The value of things change continuously. Take for example oil. It can close on Friday at $50/bbl, and then on Saturday the Saudis could have one of their large processing facilities destroyed. This would have a meaningful impact on the global supply and demand balance, such that you, and everyone else, can reasonably expect that the supply has been reduced, ...


1

You could use a Python library such as PandasDatareader for downloading the data. The main issue here is that you need the list of tickers for the stocks you want to download, and writing 9000 tickers by hand is not the best. You could exploit the fact that wikipedia offers you the list of tickers in the main indices. Here is the code for creating a list of ...


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