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Financial Techniques Market Volatility

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Discuss about the Financial Market Volatility.


During the 1980s and 1990s a number of countries have taken steps to facilitate domestic and cross-border trading in marketable financial instruments. During the same period there have been major advances in technology which, together with the development in financial techniques and hedging instruments, have significantly increased the integration of financial markets. (Tan, 2015)

These changes have, undoubtedly, improved the global allocation of financial capital. However, there is also a perception that the volatility of financial asset prices has risen, or perhaps has failed to decline, as might have been expected in the more stable inflation environment compared with the early 1980s. If true, this would be surprising and raises important questions with regard to both the measurement of volatility and its causes, in particular the effects of such factors as deregulation, internationalization of portfolio management, the use of new hedging instruments and macroeconomic policies. In turn, a possible rise in financial asset price volatility might have macro- and microeconomic consequences if there were to be effects on the allocation of financial resources and the stability of financial markets. Such implications might call for policy responses. (Bebczuk, 2013)

Since last summer volatility in many financial markets has picked up and there have been a number of short lived episodes of extreme volatility and impaired market liquidity. Implied volatilities have risen and in a number of the cases that can be shown have returned to around pre crisis levels. Moreover, longer term measures of volatility have generally increased alongside shorter term measures, as plotted charts can depict 2 year implied volatilities alongside 3 month volatilities. (Valdez, 2015)


The public has also witnessed some very large moves in financial markets over the past six months. Here, it is important to draw on two examples which include the 15 October increase in US Treasury yields following the publication of unexpectedly weak US retail sales data, and the 15 January appreciation of the Swiss franc following the SNB’s decision to remove its peg to the euro. The events had different drivers, but there are some common themes that would be drawn out. (Honeygold, 2012)

What unites the 15 October and 15 January episodes is that the immediate intraday reaction to the news was unprecedented. The intraday change in 10 Year US bond yields was 37 bps, with most of this move happening within just an hour of the data release. The intraday range represented nearly eight standard deviations, exceeding the price moves that happened immediately following the collapse of Lehman Brothers. On 15 January, the Swiss franc appreciated by 14%. The intraday range was several times that number, and market participants continue to debate the highest traded value of the franc on the day. (Cohen, 2013)

Such events could imply that a number of major asset markets may have become more sensitive to news, so that a given shock causes greater volatility. A number of recent statistical studies also imply this conclusion, suggesting that these recent episodes are a part of a broader pattern, even if they were exceptional in their scale. Some drawn charts, using the output of a model estimated by Bank staff, is representative of the statistical analysis just referred to. The charts compare the estimated impact on UK equity and corporate debt asset price volatility of a given price shock in the post crisis period relative to the pre crisis period. Post crisis, both corporate debt volatility and equity market volatility appear to have become more responsive to a given price shock. This pattern is also apparent when the model is applied to a number of other important asset markets, including for example US listed equities. (Duffy, 2013)

Liquidity was clearly affected on each of 15 October and 15 January. Measures of trading volumes, which provide one metric of liquidity, were reportedly high on both days for most affected markets. But the ability of market participants to trade without affecting prices, or in some cases, to trade at all, was clearly very limited at various points on these days. The intraday loss of liquidity was probably even starker on 15 January, from what we can tell, with widespread feedback that foreign exchange trading platforms stopped quoting Swiss francs for periods of time, while liquidity in the Swiss fixed income market was all but lost on the day and remained impaired for a few days. The performance of the US Treasury market was also materially affected on each of these days, such that much smaller trades than normal moved prices. (Hussain. 2016)


But in each of these cases there were stabilizing forces that meant volatility subsided and liquidity returned relatively quickly. In the case of the 15 October event, what had been a crowded trade was pushed so far from prices viewed as ‘justified’ by fundamentals that market participants were willing to provide support to the market. Similarly there was a reappraisal of the fundamentally justified level of the Swiss franc in the days that followed the removal of the peg, following public comments by the SNB. (Duffy, 2013) Volatility declined, if not all the way back to the pre news level, relatively quickly following these initial spikes. This is most true of the15 October episode: the implied volatility of 10 year interest rate swaps retraced about half of its upward move after two days, and was back to pre October 15th levels within just two weeks. This quick stabilization helped to limit contagion to other markets. (Fabozzi, 2015)

Neither the increase in baseline volatility, nor the recent episodes that have been described, in of themselves materially affected stability in the United Kingdom. Basically, the question becomes what lessons can be learnt? Why have volatility and liquidity evolved in this way? And is there an important fact about it? (Avellaneda, 2011)

Starting with the ‘Why?’ market intelligence suggests that uncertainty surrounding the global outlook has been one factor; itself in no small part a consequence of the unexpected rough halving in the price of oil since last summer. This uncertainty can be seen in the recent increase in the dispersion of economists’ forecasts for inflation in the US, UK and euro area during 2015. (Banks, 2013)

Central banks themselves have reacted to the changed global outlook and monetary policy makers have been active in recent months. Indeed, so far this year 24 central banks have cut their policy rates. Moreover, the decisions by the ECB and three other central banks since last summer to set negative policy rates have raised questions about where the lower bound for monetary policy exists. Clearly the answer is no longer as simple as just above zero for all central banks. And those decisions are raising questions about how markets work when negative rates persist several years down the yield curve. The combination of this macroeconomic news and central bank action means the assumed known of “low for long” (but positive) policy rates which market participants could cite as little as a year ago has been replaced by a less than certain landscape, helping to explain why ‘baseline’ volatility has picked up since last summer. (Riles, 2014)

But while this increase in macroeconomic uncertainty and central bank activity can explain some of the general increase in the level of volatility, it cannot explain the severity of events such as 15 October or 15 January. To explain these events we probably need to look elsewhere. The obvious place to turn to is market structure, given that FICC markets, which rely on intermediaries to make markets and warehouse risk, are going through considerable change. (Voit, 2015)

Here, two important  points can be highlighted which are contributing themes noted by market participants in the Fair and Effective Markets Review, both related to the provision of liquidity. First, market makers have become more reluctant to commit capital to warehousing risk. During the market intelligence conversations some have suggested that this reflects a combination of reduced risk tolerance since the financial crisis, and the impact of regulation designed to improve the resilience of the financial system. (Shiller, 2016)



This reduction in market making capacity has been associated with increased concentration in many FICC markets, as firms have been more discriminating about the markets which they make, or the clients they serve. This trend has gone hand in hand with a growth in assets under management by the buy side community. The combination has served to amplify the implications of reduced risk warehousing capacity of the intermediary sector relative to the provision of liquidity from market makers during times of market stress relative to the past.

That said, a comparison between the market liquidity of today and that of pre-crisis can be made. One does not need a long memory to recall the impact of under pricing liquidity risk on the highly leveraged market makers. Returning to such a situation would be a misplaced aspiration. (Schwartz, 2012)

Electronic platforms are now increasingly used across the various FICC markets. In some cases regulation has been the cause but in others, such as foreign exchange markets, firms have over a number of years increasingly embraced electronic forms of trading. This includes using ‘request for quote’ platforms to automate processes previously carried out by phone. Electronic platforms are effective in pooling liquidity in ‘normal’ times but may have the potential, at least as currently calibrated and given today’s level of competition, to contribute to discontinuous pricing in periods of stress if circuit breakers result in platforms shutting down. There has been much commentary about the temporary unavailability of a number of electronic trading platforms in the immediate aftermath of the removal of the Swiss franc peg. (Poon, 2015)

Financial markets have told the world what they think of the election of Donald Trump as US president – and it is not good. Global stocks, both the futures and in the physical market, started to weaken when the votes started hinting that Trump might get close. They tanked when it was clear Trump would probably win. (Do?pke, 2014)

There was extreme market volatility as the updated tally of votes were posted minute by minute but with an average fall of around 4% (at the time of writing), the value of global stocks has already dropped around US$3tn in value. US stock futures fell around 4.5%, throughout Europe and the UK stocks are down around 4% to 5%, while Japan is down over 5%. These numbers are fluid, but the verdict and direction are clear. (James, 2011)

This market reaction reflects the fear and uncertainty surrounding how president Trump will run the economy, frame the budget and operate on the international stage. As has been well analyzed, there are irreconcilable differences in the economic policy aims of Trump – lower taxes and a smaller deficit do not go together, as an example. (Kettell, 2012)

“Make America Great Again”, the slogan from the Trump campaign, involves the US raising barriers to international trade in an effort to protect US industry. If Trump follows through and works to restrict trade, especially with China where the US runs a huge trade deficit, there is a genuine threat that the global economy will stall, perhaps falling back into recession. The decades of productivity and income benefits from strong global trade risk coming to an end. Periods of weak global trade are inevitably associated with sluggish growth, stalled productivity and falling living standards. (Valdez, 2015)

To think that other countries will not retaliate against the US with their own trade restrictions is to ignore history. There seems little doubt that US multinationals and exporters have as much, if not more, to lose more than the protected industries in the US will gain. The world economy will be weaker as a result of Trump’s policy agenda. (Tan, 2015)


If the Chinese economy, which is the trading powerhouse of the world, suffers from a slump in global trade, the fallout will be far and wide. Commodity markets will falter, the services sector will weaken and countries that have significant economic links to China will obviously be under pressure. There will be a shock that will be felt around the world. (Duffy, 2013)

Financial markets know this, which accounts for the price action in stock markets in the wake of the election result. The other part of the market reaction has been in the bond market where yields have plummeted as they usually do when risk of recession increases. Investors have aggressively sold stocks and moved it into the relative security of the bond market. The US Federal Reserve, which was on the brink of hiking interest rates on the back of an improving economy, is now likely to sit until it sees the fallout from the election result – at least according to market pricing. Indeed, some smart money is pricing in a risk that the Fed will have to ease monetary policy into 2017 in reaction to a weaker economy. (Bebczuk, 2013)

Global interest rates and bond yields have followed this lead as the inevitable global economic slowdown is set to unfold. Expect central banks around the bulk of the world to implement easier monetary policy in the months ahead. If the financial markets are correct, a Trump presidency will be bad for economic growth and bad for global trade. The markets might be wrong. Sometimes they overreact to news events. As a result, there could be a market rebound when the dust of the election result settles and the policy reality confronts Trump, including dealing with the tensions within Congress, even though the Republicans will control both houses. Perhaps some of the policy proposals will not be delivered. Clearly it is too early to call. (Honeygold, 2012)



Avellaneda, M. (2011). Quantitative analysis in financial markets: Collected papers of the New York University Mathematical Finance Seminar, v.2. Singapore: World Scientific Publ.

Banks, F. E. (2013). Global finance and financial markets: A modern introduction. Singapore: World Scientific.

Bebczuk, R. N. (2013). Asymmetric information in financial markets: Introduction and applications. Cambridge : Cambridge Univ. Press.

Cohen, J. (2013). Financial Markets. Oxford: Butterworth-Heinemann.

Do?pke, J., & Pierdzioch, C. (2014). Financial market volatility and inflation uncertainty: An empirical investigation. Kiel: Kiel Institute of World Economics.

Duffy, D. J., & Germani, A. (2013). C? for financial markets. Chichester, West Sussex: John Wiley & Sons.

Fabozzi, F. J. (2015). Handbook of Finance: Financial Markets and Instruments. Hoboken: John Wiley & Sons.

Honeygold, D. (2012). International financial markets. New York: Nichols Pub. Co.

Hussain, A. (2016). Managing operational risk in financial markets. Oxford: Butterworth-Heinemann.

James, W. T. (2011). Financial market volatility: A symposium. Kansas City, Mo.: Federal Reserve Bank of Kansas City.

Kettell, B. (2012). Economics for financial markets. Oxford: Butterworth-Heinemann.

Poon, S.-H. (2015). A practical guide for forecasting financial market volatility. Chichester: Wiley.

Riles, A. (2014). Collateral knowledge: Legal reasoning in the global financial markets. Chicago: University of Chicago Press.

Schwartz, R. A. (2012). Volatility. New York: Springer.

Shiller, R. J. (2016). Market volatility. Cambridge, Mass: MIT Press.

Tan, C. H. (2015). Financial markets and institutions in Singapore. Singapore: Singapore University Press.

Valdez, S. (2015). Introduction to Global Financial Markets. Palgrave Macmillan.

Voit, J. (2015). The statistical mechanics of financial markets. Berlin: Springer.


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