{"id":6325,"date":"2015-10-07T10:44:05","date_gmt":"2015-10-07T10:44:05","guid":{"rendered":"http:\/\/revoscience.com\/en\/?p=6325"},"modified":"2015-10-07T10:44:05","modified_gmt":"2015-10-07T10:44:05","slug":"the-race-against-time","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/the-race-against-time\/","title":{"rendered":"The Race Against Time"},"content":{"rendered":"

A researcher at the Singapore Management University is developing econometric models that can provide us with a clearer picture of global markets such as the inter-continental relationships and daily shifts of stock markets, currency exchanges and interest rates.<\/strong><\/em><\/span><\/p>\n

\"Copyright<\/a>
Copyright : Cyril Ng<\/figcaption><\/figure>\n

SMU Office of Research\u2013 Having broken through most geographical borders, the capital markets have moved on to a new frontier\u2014time. Computing technology has pushed trading well beyond the second-by-second barrier, forcing analysts and investors to grapple with yet another dimension of complexity as they seek to manage their risks.<\/span>

Mastering the complexities of time, place and market movements is Tse Yiu Kuen, Professor of Economics at the Singapore Management University (SMU). He develops econometric models to isolate the phenomena affecting the tidal flows of the financial world\u2014the inter-continental relationships and daily shifts of stock markets, currency exchanges and interest rates.<\/span>

<\/p>\n

[pullquote]Professor Tse has developed econometric models to identify correlated activities among multiple markets, for example between exchanges in Hong Kong and Singapore.[\/pullquote]<\/p>\n

Identifying correlated activities across markets<\/span>

For many people, econometrics conjures up thoughts of esoteric and complicated formulas and algorithms. After all, the application of statistical models to economic theory does seem like an intimidating combination. Yet econometrics could prove essential to our understanding of the markets as the bustle and shouts of trading floors succumb to powerful algorithms trading quietly at a speed and complexity beyond human processing power. A few seconds of activity in New York might significantly affect markets in Singapore or Hong Kong.<\/span>

High-speed global trading presents a significant challenge for risk management. Even the most basic of investment principles, i.e., not to put all eggs in one basket, is getting more complicated. What if the fate of one basket hangs on the other?<\/span>

\u201cIf two markets are moving in tandem, then you are not diversifying at all,\u201d says Professor Tse. \u201cTo know whether they are sufficiently diversifying, financial analysts should understand how different markets are correlated.\u201d<\/span>

Finding these relationships amongst the tangle of global markets is difficult, but econometrics can shed light on the threads that tie the markets together. Professor Tse has developed econometric models to identify correlated activities among multiple markets, for example between exchanges in Hong Kong and Singapore.<\/span>

\u201cIn the 1990s, some companies moved from Hong Kong\u2019s capital market to Singapore\u2019s,\u201d recounts Professor Tse. \u201cWe developed a model to examine changes in Singapore\u2019s Straits Times Index and Hong Kong\u2019s Hang Seng Index before and after this trend, and found a stronger correlation after the companies had moved.\u201d<\/span>

But even as we get a clearer picture of global markets, they are being wrapped in a fog of speed. Professor Tse says that while no one knows exactly, it has been estimated that high frequency trading forms as much as 70 percent of trading volume in the New York Stock Exchange. Professor Tse\u2019s research helps to clear the fog in delineating the pattern of volatility, a key element of risk.<\/span>

The lunch break problem<\/span>

\u201cThe great difficulty we have had to overcome is intra-day periodicity. This is a statistical way to describe different trading intensities throughout the day,\u201d he explains with a laugh. \u201cMarket activity is very active in the morning, and even though we are looking at New York where they don\u2019t stop for lunch, the activity falls off, then picks up again at closing.\u201d\u00a0<\/span>

Analysts need to clear away these daily activity fluctuations to understand the market factors that cause volatility in stock prices, he says. His research has explored various ways of estimating intra-day volatility more reliably, such as in his 2012 paper, \u201cEstimation of High-Frequency Volatility: An Autoregressive Conditional Duration Approach\u201d in the Journal of Business and Economic Statistics. Professor Tse has achieved this by applying a \u201ctime-transformation function\u201d method, which transforms real time to hypothetical time.<\/span>

\u201cThis [model] creates a hypothetical day in which activity is uniform from opening to closing, allowing researchers to more accurately estimate volatility,\u201d he describes. \u201cOther methods can only measure volatility over a day, but now we can estimate it over an hour.\u201d<\/span>

One can use this model to derive a more reliable and accurate assessment of whether other factors, such as insider trading and regulatory requirements, are causing volatility, says Professor Tse.<\/span>

The \u2018volatility smile\u2019<\/span>

Using this model, Professor Tse\u2019s empirical analysis has found that intra-day volatility exhibits a \u2018volatility smile\u2019, and his methods allow us to see the real smile rather than one distorted by daily activity fluctuations, as explained in his 2014 paper, \u201cIntraday periodicity adjustments of transaction duration and their effects on high-frequency volatility estimation\u201d published in the Journal of Empirical Finance.<\/span>

Volatility is highest in the morning. It then drops in the afternoon but rises to complete the smile at the end of the day. \u201cTraders are reacting to news that have accumulated over the night; and then towards closing, they are digesting what has happened over the course of the day,\u201d he says.<\/span>

According to Professor Tse, intra-day volatility affects high frequency traders who go in and out of the market many times a day, instead of traditional traders, such as those who operate mutual funds and generally trade far less frequently.<\/span>

In fact, Professor Tse\u2019s model is most useful to traders who need to estimate their \u2018Value-at-Risk\u2019 over a short intraday period, which is also known as \u2018intraday VaR\u2019. While traditional financial analysts assess the volatility risks in their portfolio day-by-day or week-by-week, high frequency traders may need to know their volatility exposure over a horizon as short as three hours.<\/span>

Even within this narrow window, real time analysis is possible, he says. \u201cAlthough it requires a lot of steps, my model can be used in real time, using the Monte Carlo method,\u201d he explains, referring to the statistical technique of generating hypothetical data many times over to estimate what might happen in reality. And Professor Tse\u2019s work is improving the risk management tools available to analysts in this short timeframe. He demonstrates how his method outperforms earlier models in a forthcoming paper, \u201cIntraday Value-at-Risk: An asymmetric autoregressive conditional duration approach\u201d to be published in the Journal of Econometrics.<\/span>

Fortunately, data is not a problem. For a decade now, stock exchanges have opened up trading data in real time, so researchers can study \u2018tick by tick\u2019 data to examine trends, be it over a year or a day.<\/span>

But even econometric models require more developments in order to catch up with the speed of modern trading, says Professor Tse, with an eye to the future of his field. \u201cOur models deal with second-by-second trading, but now high frequency trading happens nano-second by nano-second. That is the future for our research.\u201d<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

SMU Office of Research\u2013 Having broken through most geographical borders, the capital markets have moved on to a new frontier\u2014time. <\/p>\n","protected":false},"author":6,"featured_media":6326,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[34,32],"tags":[],"class_list":["post-6325","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-economics","category-social-science"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072-150x150.jpg",150,150,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",95,63,false],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",300,200,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2015\/10\/3072.jpg",150,100,false]},"author_info":{"info":["Amrita Tuladhar"]},"category_info":"Economics<\/a> Social Science<\/a>","tag_info":"Social Science","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/6325","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=6325"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/6325\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/6326"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=6325"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=6325"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=6325"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}