The Relationship between High Frequency Trading and Market Volatility

HFT is a type of trading characterised with very high speed. The financial instruments are rapidly bought and sold by computer algorithm for a time interval measured by milliseconds.

The High-frequency trading (HFT, here onward) impacts over the capital market lead to tremendous changes in the financial industry. Ordinary traders face challenging competition of powerful computers, investment banks develop more sophisticated and faster trading algorithms, innovative HFT firms profit from superfast trading taking advantage over other market participants. These are only few of the consequences of arisen activity of HFT firms.

Using the advantage of speed, HFT firms are able to detect market orders submitted by other market participants before to be executed. As a result they are able to extract information from other traders and realise a profit from these actions.

HFT evaluate from Algorithmic trading and become a dominant force on capital market accounts for approximately 80% of trading volume. This fact raises concerns about potential risk of market stability.

HFT firms are using variety of strategies extracting advantage of their low-latency systems for fast trading and because a lot of them are operating with private capital, the regulators face enigma to understand how HFT work and whether increase the market risk.

The topic regarding HFT becomes widely discussed by both of the academic society and mainstream media. The first group research HFT via various empirical methods and report quite different results. A lot of academic papers show that HFT increase market liquidity and trading volume and improve price efficiency. Others argue that HFT lead to losses for traders because of its speed advantage. In addition, it absorbs market liquidity and decrement the price efficiency which reflects negatively over the markets.

The media coverage about HFT is rather negative than positive. The late reaction of regulators and few rapid market declines caused by HFT firms shaping public opinion for the disapprove of the HFT. However, HFT firms continue their activity and even increase it which creates some issues.

The main advantage of HFT against AT is the speed of submitting orders. Using superfast IT infrastructure and advanced trading algorithms, HFT firms are able to detect orders submitted by other market participants Zhang (2010). Consequently, they are able to detect a difference of a stock price among stock exchanges and gain profit from that submitting and cancelling foresight orders.

One of the most discussed issues is about the relationship between HFT and volatility for two reasons. First, HFT firms realise higher profit in case of high volatility. On the other hand, the volatility is a measure of risk and if HFT activity increases market volatility this would mean that HFT also increments the market risk. This is very important question regards all investors and the answer of it may help them for better understanding of HFT consequences over the capital market. Because the existing papers report very diverse results about this relation, there is no a clear answer how HFT impact over market volatility.

In the last few years the market had few unexpected movements which were caused by HFT. Undoubtedly, the most shocking was the Flash Crash happened on May 6, 2010 where the market collapse becomes only for few minutes without any obvious reason. Subsequently become clear that a HFT firm was the factor for this incident.

The topic which refers to the relationship between HFT activity and market volatility is widely discussed because high volatility means higher risk and therefore is undesirable for the investors. Bushee and Noe (2000).

The existing papers which research this relationship reports very diverse findings. Most of them show positive relation but there are few which report that there is no relationship between HFT and stock price volatility. The academics argue about HFT consequences on the capital market empirically provide various findings.

Zhang (2010) reports that HFT and stock price volatility are positively correlated. The author use quarterly data for institutional holdings and turnover for each share and each quarter.

After detecting the HFT activity on the market, Zhang uses fixed effects approach with various variables that are connected with the volatility of every company. As a result Zhang examined directly the effect of HFT on the capital markets and report the causality that high HFT activity increases volatility and also lead to overreaction of share prices in case of market news.

Can we investigate the such an relationship for Crypto Trading?
Yes. In my next articles I will review it.