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on International Finance |
| By: | Todd M. Hazelkorn; Tobias J. Moskowitz; Kaushik Vasudevan |
| Abstract: | Deviations from the law of one price between futures and spot prices, known as bases, reflect the difference between interest rates implied in futures prices and benchmark borrowing rates. These differences are driven by intermediaries’ cost of capital and the amount of leverage demand for an asset. Focusing on leverage demand, we find that bases negatively predict futures and spot market returns with the same sign in both global equities and currencies. This evidence is consistent with bases capturing uninformed leverage demand. We investigate the source of this demand in both markets using dealer and institutional positions data, securities lending fees, and foreign capital flows and find that the return predictability represents compensation to intermediaries for meeting liquidity and hedging demand. Our results have broader implications for understanding the interest rates embedded in derivatives prices. |
| JEL: | F3 F31 F65 G1 G13 G15 G2 G23 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26773&r=all |
| By: | Fan Fang; Waichung Chung; Carmine Ventre; Michail Basios; Leslie Kanthan; Lingbo Li; Fan Wu |
| Abstract: | The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars. |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2003.00803&r=all |
| By: | Oguzhan Cepni; Ibrahim Ethem Guney; Doruk Kucuksarac; Muhammed Hasan Yilmaz |
| Abstract: | This paper investigates the relation between yield curve and macroeconomic factors for ten emerging sovereign bond markets using the sample from January 2006 to April 2019. To this end, the diffusion indices obtained under four categories (global variables, inflation, domestic financial variables, and economic activity) are incorporated by estimating dynamic panel data regressions together with the yield curve factors. Besides, in order to capture dynamic interaction between yield curve and macroeconomic/financial factors, a panel VAR analysis based on the system GMM approach is utilized. Empirical results suggest that the level factor responds to shocks originated from inflation, domestic financial variables and global variables. Furthermore, the slope factor is affected by shocks in global variables, and the curvature factor appears to be influenced by domestic financial variables. We also show that macroeconomic/financial factors captures significant predictive information over yield curve factors by running individual country factor-augmented predictive regressions and variable selection algorithms such ridge regression, LASSO and Elastic Net. Our findings have important implications for policymakers and fund managers by explaining the underlying forces of movements in the yield curve and forecasting accurately dynamics of yield curve factors. |
| Keywords: | Yield curve, Macroeconomic factors, Nelson Siegel model, Panel VAR, Forecasting, Variable selection |
| JEL: | C1 C5 F2 F3 F4 G1 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:tcb:wpaper:2004&r=all |
| By: | Laser, Falk Hendrik; Weidner, Jan |
| Abstract: | During recent years, central banks have increased the levels of their international reserves at an unprecedented pace. In this paper, we introduce new country-specific reserve data and examine determinants of the composition of international reserves. Using a dataset of 36 countries (and the euro area) for the years from 1996 to 2016, we identify currency pegs and trade patterns as determinants of currency compositions. Our results emphasize the importance of transaction motives for the composition of currency reserves. The euro crisis appears to have been a setback for the euro, which temporarily seemed to challenge the US dollar as the most important international reserve currency and potentially impacted the determination of international reserve compositions. |
| Keywords: | International reserves,central banks,euro crisis |
| JEL: | E58 F31 G01 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:darddp:238&r=all |
| By: | Dräger, Lena; Nguyen, Duc Binh Benno; Prokopczuk, Marcel; Sibbertsen, Philipp |
| Abstract: | This paper examines long memory volatility in international stock markets. We show that long memory volatility is widespread in a panel dataset of eighty-two countries and that the degree of memory in the panel can be related to macroeconomic variables such as short- and long-run interest rates and unemployment. Moreover, we find that developed economies possess longer memory in volatility than emerging and frontier countries and that stock market jumps are negatively correlated with long memory of volatility. Overall, our results provide some evidence of a link between stock market uncertainty and macroeconomic conditions, which is prevalent across a large range of countries. |
| Keywords: | International; Long Memory; Volatility |
| JEL: | G15 C22 F30 F40 |
| Date: | 2020–02 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-667&r=all |