What Drives Currency Predictability?

Potì, Valerio; Siddique, Akhtar R.

doi:10.2139/ssrn.2158348

Cited by 3 publications

(6 citation statements)

References 66 publications

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“…This section asks if the predictability in the VAR-i.e., mean reverting impulse responses-is consistent with rational pricing and reasonable risk aversion. Potì and Siddique (2013) show that the product of the square of the coefficient of risk aversion and the variance of the market return must exceed the 2 from a predictive regression. 16…”

Section: Is the Estimated Predictability Consistent With Rational Primentioning

confidence: 92%

“…Although Kirby (1997) was important in connecting asset return predictability to risk aversion, this appendix essentially condenses work from the appendices to Potì and Siddique (2013), who draw on arguments in Cochrane (1999) and Potì and Wang (2010). Their arguments are applied in Levich and Potì (2015).…”

Section: Appendix B: the Bound On R 2 S In Asset Return Equationsmentioning

confidence: 99%

“…Kirby (1998) first formalized the intuition that the willingness to substitute consumption across states of nature must bound the 2 from a predictive regression of asset returns. Appendix B summarizes the implications ofPotì and Siddique's (2013) work for the present paper.17 The reader might wonder if rational bounds should apply in the post-crisis financial environment of 2008:11-2011:10. Standard measures of market stress indicate that the market was definitely more volatile than average but still functioning within normal bounds.…”

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confidence: 95%

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How Persistent Are Unconventional Monetary Policy Effects?

Neely

2014

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Event studies show that the Federal Reserve's announcements of forward guidance and largescale asset purchases had large and desired effects on asset prices but these studies do not tell us how long such effects last. Wright (2012) used a structural vector autoregression (SVAR) to argue that unconventional policies have very transient effects on bond yields, with half-lives of 3 to 6 months. The present paper shows, however, that this inference is unsupported for several reasons. First, accounting for model uncertainty greatly lengthens the estimated persistence. Second, and more seriously, the inference is unreliable because the SVAR is structurally unstable and forecasts very poorly. Finally, the implied in-sample return predictability from the SVAR greatly exceeds a level consistent with rational asset pricing and reasonable risk aversion. Restricted models that respect more plausible asset return predictability are more stable and imply that unconventional monetary policy shocks were fairly persistent. Estimates of the dynamic effects of shocks should respect the limited predictability in asset prices.

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Section: Is the Estimated Predictability Consistent With Rational Primentioning

confidence: 92%

Section: Appendix B: the Bound On R 2 S In Asset Return Equationsmentioning

confidence: 99%

mentioning

confidence: 95%

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How Persistent Are Unconventional Monetary Policy Effects?

Neely

2014

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“…To determine the extent to which the VAR might overfit the data, Table 3 reports the insample 𝑅𝑅 2 , the OOS 𝑅𝑅 2 , and the Potì and Wang (2010) and Potì and Siddique (2013) bounds on the R 2 s implied the bond return regressions (equation ( 7)) from the bias-adjusted VAR. The OOS forecast statistics are constructed with expanding samples, updating the VAR coefficients every 20 business days.…”

Section: Sensitivity Analysismentioning

confidence: 99%

How persistent are unconventional monetary policy effects?

Neely

2022

Journal of International Money and Finance

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“…The financial market therefore becomes less efficient and it is possible to predict the stock market to some extent. For example, Poti et al [107] discover there exists predictability in the foreign exchange market (FOREX) in their experiments.…”

Section: Adaptive Market Hypothesismentioning

confidence: 99%

Towards better prediction and content detection through online social media mining

Chen¹

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This thesis would not be possible without many people who have helped me and changed my life deeply during my study at Nanyang Technological University (NTU). First and foremost, I would like to express my sincerest gratitude to my supervisors, Associate Professors Lau Chiew Tong and Yeo Chai Kiat for giving me all the support and freedom to carry out the research which I am interested in. In addition, I would also like to extend my appreciation to Associate Professor Lee Bu Sung who has given me many useful suggestions for my research. Their precious and warm help in my research and studies, infectious enthusiasm and kindness, unlimited patience and inspiring guidance have been the major driving force during my candidature at NTU. Under their supervision, I have developed skills in critical thinking, research methodologies and integrity, technical writing, communication and leadership, which are invaluable for my future career. Moreover, I would like to thank the students and staff of Computer Networks and Communications Graduate Lab (CNCL) in the School of Computer Science and Engineering (SCSE) at NTU. I would like to thank my seniors, Yang Yiqun, Pham Thi Ngoc Diep; my lab peers, Zhang Yan and Chen Zhaomin; my junior, Yean SeangLidet and my good friends, Liang Yuhan, Chu Zhaowei and Li Qiye. They have not only provided me with their valuable suggestions for my studies and research, but also enriched my life at NTU with unforgettable experiences. Last but not least, I cannot end without giving my special thanks to my parents for their continuous support and endless love. I am also grateful to Cao Pi and Furuya Rei whom I always admire and give me all the courage to overcome the difficulties I have met. This thesis cannot be finished without them and is dedicated to them.

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What Drives Currency Predictability?

Cited by 3 publications

References 66 publications

How Persistent Are Unconventional Monetary Policy Effects?

How Persistent Are Unconventional Monetary Policy Effects?

How persistent are unconventional monetary policy effects?

Towards better prediction and content detection through online social media mining

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