foreign exchange markets

A possible explanation for this behavior is that information arrival which leads to changes in asset prices are not evenly spaced but arrive in bunches. The price at which a stock sells at a particular time, for example, reflects or is the result of available information that flows within the market, whether positive or negative and these information in volatility clustering is known to arrive in bunches rather than been evenly spaced. The figure above depicts that the series used in the study show some sort of volatility clustering a point in time during the years under consideration. The returns can be seen to fluctuate around the constant but exhibit volatility clustering, that is, large changes in returns cluster together and small changes in returns also cluster together. This means that the series can be said to exhibit conditional heteroscedasticity. One significant timeline worthy of mention is the 2008 sub-prime crisis. African markets had their own share of the crisis, since they may be one way or another be related to other global indices. It can therefore be seen that all the series exhibit some spikes during this period of time. Also, the Egyptian stock and foreign exchange markets responded with some negative spikes in the stock market during 2011, the year of the revolution which led to the ousting of Hosni Mubarak from office. This may have been as a result of low foreign direct investment in the wake of the political unrest. However due to a low correlation of the Egyptian stock and currency with the other African countries, the effect of the revolution wasn’t felt in the other series has also shown in the correlation analysis below. To be able to identify the degree of linear association among the variables, which is a way to prove that there is a linear relationship between them and movements in the variables are to some extent related given by the correlation coefficient.

Drawing on previous work of one of the authors, the paper takes an asymmetric variant of Kirman’s ant model and combines it with an elementary asset pricing mechanism. The closed-form solution of the equilibrium probability distribution allows the specification of a tractable likelihood function for daily returns, which is then employed to estimate the model’s behavioural parameters for a large pool of Japanese stocks. By way of Monte Carlo simulations it is found that most of these markets belong to the same class, which is characterized by a dominance of the stylized noise traders. In contrast, the model assigns a number of major foreign exchange markets to a different class, where on average the majority of agents follows the fundamentalist trading rule. Implications for the tail index are also worked out.

Reference [16] proofed there are two types of traders in the foreign exchange markets: fundamentalists and chartists. This study showed that fundamentalists expect the exchange rate to revert to its fundamental value and chartists tend to extrapolate the trend for the recent period. References [19] and [20] showed that heterogeneous market participants are active in the yen/dollar market, whereas the stabilizing movement of the fundamentalists declines in times of misalignment. Recently, in the field of behavioral economics/finance, [21] showed that market participants first choose the optimal behavioral portfolio theory, overlook covariances among markets, and allocate funds across markets according to a rule to attain mean-variance efficiency or to minimize the loss. Reference [22] employed a stochastic chartist-fundamental model and showed that a stochastic delay-differential equation incurred market-like stock price dynamics that reflect the effects of time delay.

Due to the lack of appropriate data on official intervention in the foreign exchange markets, it is a difficult task for both academic researchers and policy makers to verify whether countries let their exchange rate adjust freely as they claimed. The controversy is apparent particularly for emerging market countries since it is believed in general that in order to prevent their competitiveness from deteriorating, emerging market countries tend to frequently and substantially intervene in foreign exchange markets to prevent or slow the appreciation of their currencies. However, simple descriptive statistics such as the rate of depreciation in real effective exchange rate or growth rate of foreign reserve accumulation may fail to capture the whole dynamic picture of official intervention.

This paper is related to the existing literature in several directions. First, it con- tributes to the literature on modeling the spot prices in foreign exchange markets. Shleifer and Vishny (1997) point out that liquidity in the foreign exchange markets is crucial for arbitrage trading and market efficiency. Periods when markets expect ground-breaking news may suffer liquidity shocks as fund managers tend to rather over-hedge and thus are more reluctant to trade. Therefore, a period with heavily interventions by central banks may imply temporal market inefficiencies. Boudt and Petitjean (2014) show the link between price jumps and liquidity for the equity markets. Further, Lyons (2001) documents that foreign exchange markets are in particular of limited transparency, their market participants are very heterogeneous with different scope and volume of operations, and trading under the decentralized dealership structure.

In general, regarding emerging markets around the East-Asia region not quite much research has been done on the interactive effects of the stock and foreign exchange markets regarding the view of the short- and long-run volatility components by the Component GARCH model. Some researches refer to the connections between the stock and foreign exchange markets through the spillovers of market volatility by GARCH-type models or via linear or even nonlinear co-integration analysis. Even though both the GARCH model (focused on market volatility spillovers) and the co-integration analysis (focused on the co-integration vector variation of the long-term correlation after exogenous chocks), these methods still cannot help to detect the impacts of information transmission on the permanent and transitory return volatility between the stock and foreign exchange markets. In light of such short- and long-run characteristics of return volatility, our study applies the Component GARCH model, modified from the traditional GARCH model by Engle and Lee (1999) that decomposes conditional variance into permanent and transitory components.

This table reports regression results for the risk exposures of the BMS portfolios of financial FX market end-users, that is, long-term demand-side investment managers (LT) and short-term demand-side investment managers (ST). The methodological framework a modified linear Fung- Hsieh (2002, 2004) model that also accounts for conditional equity market exposures by including various interaction terms. Besides the three conditioning variables (changes in VIX, TED spread, and T-bill rate) as used in the baseline results of Table VI, we consider two additional FX- specific conditioning variables: changes in interest rate di↵erentials as measured by the average forward discount (AFD) and foreign exchange market volatility (FXV). The table shows results for four parsimonious model specifications where the factors are selected according to the Schwarz criterion as outlined in the main text. Results for the other factors are not reported. We further report the estimated intercept ↵, the adjusted b R 2 , and the BIC computed for the two-equation system (Sys-BIC). Below the regression coefficients, t-statistics based on Newey-West standard errors are reported in brackets.

It is now more than two decades since the Plaza Agreement signed on September 22, 1985 and the Louvre Accord on February 22, 1987. 1 These agreements were signed in order to induce US dollar depreciation and promote stability in currency markets, respectively. Economists, policy makers and central bank analysts still lack conclusive evidence on the impact of CBIs on exchange returns and especially on volatility. The majority of the empirical literature suggests that unilateral, and even coordinated intervention of two central banks, does not affect exchange returns and has in most of the cases the opposite outcome on volatility from that expected (among others see Beine, 2004; Beine et al., 2002; Fatum, 2002; Humpage, 1999; Baillie and Osterberg, 1997; Bonser-Neal and Tanner, 1996; Catte et al., 1992). That is, interventions associated with the Louvre Accord appear to have been counterproductive since they led to an increase in volatility as opposed to the intended decrease.

The effect of publicly released news on market depth has been difficult to ascertain, primarily because of a lack of data. Lee, Mucklow, and Ready (1993) find th at the depths of specialist dealers in equity markets do fall around public news announcements, but thus far, little has been done in FX markets.11 The one exception to this is the study by Carlson and Lo (2004). They study the effect of an increase in German interest rates on spot DEM/USD (Deutsche-marks per dollar) depth. At 11:30 GMT on 9th October 1997, the German Bundesbank decided to increase the repo rate from 3% to 3.3%. However, this announcement was unscheduled and came as a complete surprise to market participants. Carlson and Lo (2004) show th at the announcement caused the exchange rate (mid-quote) to change and also caused depth to fall dramatically in the minute following the release. At 11:30 GMT the inside spread was 5 pips (the best bid in the market was 1.7530 DM per dollar and the best ask was 1.7535) and the spread for $10 million was 21 pips (0.0021 DM per dollar). In the following 35 seconds there was considerable trading with the transaction price increasing from 1.7530 to 1.7560 DM. At 11:30:35 the inside spread had increased to 39 pips and the spread for $10 million had increased to 52 pips. Even though an unexpected German interest rate rise should precipitate a fall in the price of the dollar, the US currency appreciated significantly in the initial 35 seconds post release. Carlson and Lo put this down to the existence of a large number of open short positions in the dollar, evidenced by a much deeper limit order schedule at the bid compared to the ask, i.e. there was greater buying support for the dollar at the time of the announcement. After the shock announcement of news, traders immediately tried to cover these short dollar positions by buying the currency (through market orders) which then led to its appreciation. However, in the following 25 seconds to 11:31:00, $86 million of liquidity at the bid were removed, $81 million of which were caused by seller initiated trades. Carlson and Lo suggest that traders did not remove their bid prices but simply let them be hit

Permanent repository link: http://openaccess.city.ac.uk/6972/ Link to published version: http://dx.doi.org/10.1016/j.finmar.2015.03.002 Copyright and reuse: City Research Online aims to [r]

Thus, as foreign capital restrictions are lifted in the Pacific Basin Stock markets and the link between the foreign exchange market and the stock market increases, there will also be an[r]

This chapter develops a novel discrete-time model for the asset return based on the high- frequency data and mixture of normal distributions of the latent volatility. We use ex- pectation maximization algorithm to avoid problems with direct maximization of mixing likelihood, which allows us to obtain robust estimates. Our model accurately replicates distributions of both returns and realized volatility under physical measure as we demon- strated in our simulation exercise. To compute option prices, we specify a Radon-Nikodym derivative, which includes both Gaussian and non-normal innovations, correspondingly. We identify one parameter of the pricing kernel from EMM condition, while obtain an- other parameter by fitting option prices for each week. Crucially, our approach avoids calibration of all model’s parameters. We price European Put options using Monte Carlo simulations and assess pricing performance of MN and nested Gaussian models during turbulent financial markets in 2008-2011 years.

Certainly there exist markets that bear at least an approximate resemblance to the Walrasian framework4. But there are many other financial markets where price-setting cannot be convincingly argued to fit in this picture. For example, in the foreign exchange markets and in the main trading periods o f most stock exchanges, trading takes place continuously and there are specific market partici­ pants playing roles far more active in price-setting than the Walrasian auctioneer does. The market maker or specialist in many exchanges do not have the lux­ ury o f going through the repetitive process o f submitting demand => announcing potential price => revising demand before setting the actual transaction prices. Also importantly, the insiders, such as those in Kyle (1985), might not even have the incentives to submit a fully informative demand and demand revision during the Walrasian auction process5. How are the ’’equilibrium prices” formed?6 The easiest way to demonstrate the information incorporation mechanism is by using Glosten and Milgrom (1985) framework. Assume there are informed and liquid­ ity traders in the market. The market maker sets the ask price at to the expected value o f an asset after seeing a trader wishing to buy (i.e. Et [V\Buy\. at depends on the conditional probability that V is either lower or higher than his prior belief Vo given that a trader wishes to buy. The bid price bt is defined similarly given

of 1975 as the Treasury had predicted. Over two days in the second week of May, the Bank used $313 million in support of the rate (TNA T 358/208, Note for the Record, 13 May 1975), and between April and June, the Bank‟s market intervention had come at a cost to the reserves of £641 million, with the end month total falling from £7,132 million to £6,491 million (TNA T 354/416, Reserves Objectives, 11 August 1975). During this period, sterling‟s dollar spot rate had slipped from $2.38 to $2.22 (Bank of England, 1975, Table 27), and whilst this was still short of the 10 per cent depreciation advocated within the Treasury, it began to argue that there was no end in sight to the market‟s scepticism about sterling resulting from the absence of a decisive counter inflation policy, even though it had made no decisive effort to halt the slide with substantial intervention in the foreign exchange markets.

Exchange rate is the price of one country's currency expressed in another country's currency. In other words, it is the rate at which one currency can be exchanged for another. It is the process of trading the currency of one country for the currency of another. This process is necessary for international trade to take place in a world of different currencies. Exchange rate is a national and international political, social and economic indicator. In developed countries, it reacts quickly to events like war, terrorism, and also to the changes in the political situation as well as to main economic indicators like unemployment and interest rate. From a macro perspective, foreign exchange rate has an effect on the country’s economy whereas from a micro perspective it affects the firms, [21]. The foreign exchange market is the largest and oldest financial market in the world. The value of one currency versus another is determined by the international exchange rate and, in most cases, is subject to fluctuation based on open trading of currency in foreign exchange markets, [14]. The market for foreign exchange involves the purchase and sale of national currencies. A foreign exchange market exists because economies employ national currencies. If the world economy used a single currency there would be no need for foreign exchange markets. In Europe for example, more than eleven economies have chosen to trade their individual currencies for a common currency. But the Euro will still trade against other world currencies. For now, the foreign exchange market is a fact of life [5].

Yet very few studies have been conducted on the interactions between the foreign exchange market and securities market in emerging and developing economies (see Bonga-Bonga and Hoveni, 2013 and Mishra et al., 2007), and those that do exist limit themselves to analysing the relationship between the level or change in exchange rate and capital markets. This study therefore focuses on the impacts of the volatility in the foreign exchange markets on the capital market, especially the returns on equity and bond yields markets in the BRICS (Brazil, Russia, India, China and South Africa) economies. Put differently, this paper assesses how foreign exchange risks are priced in equity and bond markets in the BRICS. The paper uncovers whether the relationship between exchange rate volatility and equity and bond returns is determined in the context of capital market line (CML), in that the expected risks counterbalances the expected returns or that exchange rate volatility creates a risky environment that compromises the returns in equity and bond markets. Another contribution of this paper will be to infer whether there exists evidence of equity and bond home bias in each of the BRICS.

Against this backdrop, the objective of this paper is to study the predictive ability of economic news releases and macroeconomic determinants on returns, volatility and jumps of the stock and foreign exchange markets of South Africa. The entire analysis is implemented in a monthly frequency. Volatility is estimated with the use of the median realized variance estimator building on the work of Andersen et al. (2012). Jumps are detected and estimated as in Duong and Swanson (2015). The impact of the announcements is assessed building on the work of Huang et al. (2015), by employing a vector autoregressive model with exogenous variables (VAR-X) in order to avoid endogeneity in the estimates. Our results reveal that the macroeconomic fundamentals can explain return, volatility and jumps series. Furthermore, macroeconomic announcements affect significantly both stock and foreign exchange markets. The robustness of results is ensured by examining the research questions in different sub-samples based on the global financial crisis of 2008. Such analysis has important implications for investment managers and policymakers. Our main target is to understand more efficiently the nature of jumpy (known as bad volatility) volatility during periods of macroeconomic adjustments in an emerging but quite promising market. Assuming that policymakers’ decisions causing macroeconomic adjustments can create jump-inducing turbulence in financial markets, it is economically vital to progress to an econometric understanding of financial time series behavior of bad and good volatility during such periods (see Todorov and Tauchen 2011). Furthermore, the insights provided in this study are important for the development of hedging strategies and the specification of risk premia. Basically, investors make decisions associated with risk management and designing an appropriate asset allocation strategy. Nevertheless, their decisions can be changed during

In particular, we ask the question: can economic fundamentals explain the exchange rate behaviour we observe in foreign exchange markets, or is the behaviour simply a reflection of ill-i[r]

4 During the capitalism evolution process with the continuous innovation and the creative disruption, the disruptive innovation is frequently generated by the means of the innovation breakthrough processes in Schumpeter (1911, 1939, 1947). The multiple discoveries of the innovative disruptive technological advancements in the information communication technologies in Shannon (1948), Ledenyov D O, Ledenyov V O (2012e) have been introduced in the Schumpeterian creative destruction age, resulting in the appearance of the technological and scientific advancements toward the ultra high frequency electronic trading in the foreign currencies exchange markets. In this empirical research article, we would like to discuss comprehensively the modern theories and practices toward the ultra high frequency electronic trading in the foreign exchange markets at an influence by the discrete information absorption processes in the diffusion – type financial systems with the induced nonlinearities, using the econometrical and econophysical principles, theories and perspectives in Schumpeter (1906, 1933), Bowley (1924), Box, Jenkins (1970), Grangel, Newbold (1977), Van Horne (1984), Taylor S (1986), Tong (1986, 1990), Judge, Hill, Griffiths, Lee, Lutkepol (1988), Hardle (1990), Grangel, Teräsvirta (1993), Pesaran, Potter (1993), Banerjee, Dolado, Galbraith, Hendry (1993), Hamilton (1994), Karatzas, Shreve (1995), Campbell, Lo, MacKinlay (1997), Rogers, Talay (1997), Hayashi (2000), Durbin, Koopman (2000, 2002, 2012), Ilinski (2001), Greene (2003), Koop (2003), Davidson, MacKinnon (2004), Campbell, Lo, MacKinlay (1996). In the conditions of the highly volatile dynamics of global financial system in Aliber (2002), we would like to focus specifically on the application of the probability theory in De Laplace (1812), Bunyakovsky (1846), Chebyshev (1846, 1867, 1891), Markov (1890, 1899, 1900, 1906, 1907, 1908, 1910, 1911, 1912, 1913), Kolmogorov (1938, 1985, 1986), Wiener (1949), Brush (1968, 1977), Shiryaev (1995), Ledenyov (2004) to analyze the nonlinear financial systems dynamics, aiming to predict the trends in the foreign currencies exchange rates at an influence by the discrete information absorption processes in the diffusion – type financial systems with the induced nonlinearities.

While most studies on volatility in South Africa and other foreign markets have mainly focused on returns linkages, information flow and volatility transmission of financial markets between countries for not more than three markets, this paper differs in that it considers four major markets in SA. Time series models provide an estimate of the variance of the relevant return series based on historical return data that are used to create volatility forecasts (Corredor and Santamaria, 2004) . Like most other papers, high frequency daily data is used, with a period of 2000/01/03 to 2009/08/05 because, this period covers times when South Africa experienced series of interest rate cut and changes, surprises of political and news announcement such as the stepping down of the finance minister Trevor Manuel and most importantly, includes a time of global financial crisis and recession, having an implication on South Africa ’s integration in to the world economies. Specifically used daily data because of the assumption that the markets react quickly to news, accordingly, therefore low frequency would fail to capture such dynamics. The study focuses on the stock, bond, money and foreign exchange markets because they comprise and contribute largely to financial and development status of a country.