{"id":49413,"date":"2018-06-15T20:10:00","date_gmt":"2018-06-16T00:10:00","guid":{"rendered":"https:\/\/prefblog.com\/?p=49413"},"modified":"2025-12-04T21:36:13","modified_gmt":"2025-12-05T02:36:13","slug":"june-15-2018","status":"publish","type":"post","link":"https:\/\/prefblog.com\/?p=49413","title":{"rendered":"June 15, 2018"},"content":{"rendered":"

The New York Fed has released<\/a> a paper by Andreas Fuster and James Vickery titled Regulation and Risk Shuffling in Bank Securities Portfolios<\/a>:<\/p>\n

\n

Bank capital requirements are based on a mix of market values and book values. We investigate the effects of a policy change that ties regulatory capital to the market value of the \u201cavailable-for-sale” investment securities portfolio for some banking organizations. Our analysis is based on security-level data on individual bank portfolios matched to bond characteristics. We find little clear evidence that banks respond by reducing the riskiness of their securities portfolios, although there is some evidence of a greater use of derivatives to hedge securities exposures. Instead, banks respond by reclassifying securities to mitigate the effects of the policy change. This shift is most pronounced for securities with high levels of interest rate risk.
…<\/b>
Quantitatively more importantly, we found evidence that treated banks respond by actively reshuffling their portfolios, and in particular classifying risky securities as “held to maturity” (HTM) rather than available for sale (AFS). The use of detailed security-level data allow us to control in a precise way for security characteristics – most finely by including both BHC fixed effects and a vector of CUSIP-by-calendar quarter fixed effects in our specifications. This is an important feature of our analysis, since it allows us to isolate the effects of the accounting classification decision for a given security from changes in the composition in investment securities portfolios which was occurring during this period. In these specifications, our preferred point estimates suggest that a security is 20 percentage points more likely to be classified as HTM rather than AFS if owned by a BHC subject to the AOCI rule (measured on a fully phased-in basis), or 38 percentage points measured on a weighted basis. For both agency MBS and Treasury securities, we find that these effects are concentrated among bonds with higher duration.<\/p>\n<\/blockquote>\n

It’s a common fallacy – you will often find people arguing vociferously that a five year GIC is less risky than a five year bond because the reported price doesn’t change and the holder doesn’t intend to sell it anyway. Crazy.<\/p>\n

In another paper released today<\/a>, Richard Crump, Domenico Giannone, and Sean Hundtofte claim that volatility has meaning in a paper titled Changing Risk-Return Profiles<\/a>:<\/p>\n

\n

We show that realized volatility, especially the realized volatility of financial sector stock returns, has strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time. Current realized volatility has the most information content on the uncertainty of future returns, whereas it has only limited content about the location of the future return distribution. When volatility is low, the predicted distribution of returns is less dispersed and probabilistic forecasts are sharper. Given this finding on the importance of financial sector volatility not just to financial equity return uncertainty but to the broader market, we test for changes in the realized volatility of banks over a $50 billion threshold associated with more stringent Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank) requirements. We find that the equity volatility of these large banks is differentially lower by 9 percentage points after Dodd-Frank compared to pre-crisis levels, controlling for changes over the same period for all banks and all large firms.
…<\/b>
Our paper is most closely related to Cenesizoglu and Timmermann (2008) who study whether economic and financial variables can help improve prediction of the quantiles of the return distribution. Cenesizoglu and Timmermann (2008) also find evidence of predictive power especially in the upper tail of the return distribution. Our paper is also related to Massacci (2015) who evaluates the accuracy of density forecasts but restricts the economic and financial variables to predict the location of the distribution only. Durham and Geweke (2014) predict higher-frequency, daily returns allowing for realized intraday volatility and option-implied volatility but restrict these variables to predict the scale of the distribution only.<\/p>\n

Our paper is also related to those papers that investigate interactions between financial crises (high periods of market volatility) and equity returns. For example, Baron and Xiong (2017) ex-amine changes in skew of bank equity returns in response to changes in economic leverage (bank credit\/GDP). Moreira and Muir (2017) look at the returns of a market portfolio managed by lagged volatility, finding investors are not compensated for risk in periods of lagged high volatility. Similarly, when we examine the distribution of aggregate returns conditional on realized volatility we find no predictability in the average return, but do find predictability in the risk to holding the
\nmarket portfolio (either in terms of variance at short horizon, or skew at longer horizons), indicating a breakdown in a risk-return tradeoff. Adrian et al. (2017b) document a strongly nonlinear dependence of stock and bond returns on past equity market volatility as measured by the VIX with expected stock returns increasing for stocks when volatility increases from moderate to high levels.<\/p>\n<\/blockquote>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
HIMIPref™ Preferred Indices
These values reflect the December 2008 revision of the HIMIPref™ Indices<\/strong>
Values are provisional and are finalized monthly<\/td>\n<\/tr>\n
Index<\/td>\nMean
Current
Yield
(at bid)<\/td>\n		Median
YTW<\/td>\n		Median
Average
Trading
Value<\/td>\n		Median
Mod Dur
(YTW)<\/td>\n		Issues<\/td>\nDay’s Perf.<\/td>\nIndex Value<\/td>\n<\/tr>\n
Ratchet<\/td>\n0.00 %<\/td>\n0.00 %<\/td>\n0<\/td>\n0.00<\/td>\n0<\/td>\n-0.1650 %<\/td>\n3,024.7<\/td>\n<\/tr>\n
FixedFloater<\/td>\n0.00 %<\/td>\n0.00 %<\/td>\n0<\/td>\n0.00<\/td>\n0<\/td>\n-0.1650 %<\/td>\n5,550.1<\/td>\n<\/tr>\n
Floater<\/td>\n3.33 %<\/td>\n3.52 %<\/td>\n69,860<\/td>\n18.49<\/td>\n4<\/td>\n-0.1650 %<\/td>\n3,198.6<\/td>\n<\/tr>\n
OpRet<\/td>\n0.00 %<\/td>\n0.00 %<\/td>\n0<\/td>\n0.00<\/td>\n0<\/td>\n-0.1038 %<\/td>\n3,162.0<\/td>\n<\/tr>\n
SplitShare<\/td>\n4.65 %<\/td>\n4.81 %<\/td>\n77,123<\/td>\n5.00<\/td>\n5<\/td>\n-0.1038 %<\/td>\n3,776.1<\/td>\n<\/tr>\n
Interest-Bearing<\/td>\n0.00 %<\/td>\n0.00 %<\/td>\n0<\/td>\n0.00<\/td>\n0<\/td>\n-0.1038 %<\/td>\n2,946.3<\/td>\n<\/tr>\n
Perpetual-Premium<\/td>\n5.62 %<\/td>\n-7.38 %<\/td>\n58,754<\/td>\n0.08<\/td>\n9<\/td>\n0.0131 %<\/td>\n2,877.2<\/td>\n<\/tr>\n
Perpetual-Discount<\/td>\n5.39 %<\/td>\n5.54 %<\/td>\n64,902<\/td>\n14.52<\/td>\n26<\/td>\n0.1779 %<\/td>\n2,958.0<\/td>\n<\/tr>\n
FixedReset<\/td>\n4.31 %<\/td>\n4.71 %<\/td>\n166,698<\/td>\n5.66<\/td>\n106<\/td>\n-0.1117 %<\/td>\n2,541.3<\/td>\n<\/tr>\n
Deemed-Retractible<\/td>\n5.19 %<\/td>\n5.78 %<\/td>\n70,434<\/td>\n5.56<\/td>\n27<\/td>\n0.0535 %<\/td>\n2,943.8<\/td>\n<\/tr>\n
FloatingReset<\/td>\n3.13 %<\/td>\n3.83 %<\/td>\n35,795<\/td>\n3.45<\/td>\n9<\/td>\n-0.0449 %<\/td>\n2,796.2<\/td>\n<\/tr>\n<\/table>\n\n\n\n\n\n\n\n
Performance Highlights<\/strong><\/td>\n<\/tr>\n
Issue<\/td>\nIndex<\/td>\nChange<\/td>\nNotes<\/td>\n<\/tr>\n
BAM.PR.X<\/td>\nFixedReset<\/td>\n-1.82 %<\/td>\nYTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 18.36
\nEvaluated at bid price : 18.36
\nBid-YTW : 5.02 %<\/td>\n<\/tr>\n
BAM.PF.B<\/td>\nFixedReset<\/td>\n-1.72 %<\/td>\nYTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 22.80
\nEvaluated at bid price : 23.42
\nBid-YTW : 5.04 %<\/td>\n<\/tr>\n
IFC.PR.A<\/td>\nFixedReset<\/td>\n-1.26 %<\/td>\nYTW SCENARIO
\nMaturity Type : Hard Maturity
\nMaturity Date\t: 2025-01-31
\nMaturity Price : 25.00
\nEvaluated at bid price : 19.55
\nBid-YTW : 7.79 %<\/td>\n<\/tr>\n
PWF.PR.T<\/td>\nFixedReset<\/td>\n-1.02 %<\/td>\nYTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 23.59
\nEvaluated at bid price : 24.26
\nBid-YTW : 4.65 %<\/td>\n<\/tr>\n<\/table>\n\n\n\n\n\n\n\n\n\n\n
Volume Highlights<\/strong><\/td>\n<\/tr>\n
Issue<\/td>\nIndex<\/td>\nShares
Traded<\/td>\n		Notes<\/td>\n<\/tr>\n
EMA.PR.H<\/td>\nFixedReset<\/td>\n97,100<\/td>\nYTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 23.19
\nEvaluated at bid price : 25.11
\nBid-YTW : 4.80 %<\/td>\n<\/tr>\n
NA.PR.G<\/td>\nFixedReset<\/td>\n65,575<\/td>\nYTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 23.13
\nEvaluated at bid price : 24.97
\nBid-YTW : 4.85 %<\/td>\n<\/tr>\n
TD.PF.I<\/td>\nFixedReset<\/td>\n55,300<\/td>\nYTW SCENARIO
\nMaturity Type : Call
\nMaturity Date\t: 2022-10-31
\nMaturity Price : 25.00
\nEvaluated at bid price : 25.20
\nBid-YTW : 4.46 %<\/td>\n<\/tr>\n
NA.PR.C<\/td>\nFixedReset<\/td>\n41,570<\/td>\nYTW SCENARIO
\nMaturity Type : Call
\nMaturity Date\t: 2022-11-15
\nMaturity Price : 25.00
\nEvaluated at bid price : 24.98
\nBid-YTW : 4.59 %<\/td>\n<\/tr>\n
BMO.PR.W<\/td>\nFixedReset<\/td>\n31,300<\/td>\nYTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 22.60
\nEvaluated at bid price : 23.02
\nBid-YTW : 4.70 %<\/td>\n<\/tr>\n
RY.PR.Z<\/td>\nFixedReset<\/td>\n30,177<\/td>\nYTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 22.93
\nEvaluated at bid price : 23.50
\nBid-YTW : 4.62 %<\/td>\n<\/tr>\n
There were 18 other index-included issues trading in excess of 10,000 shares.<\/td>\n<\/tr>\n<\/table>\n\n\n\n\n\n\n\n\n\n
Wide Spread Highlights<\/strong><\/td>\n<\/tr>\n
Issue<\/td>\nIndex<\/td>\nQuote Data and Yield Notes<\/td>\n<\/tr>\n
BAM.PF.G<\/td>\nFixedReset<\/td>\nQuote: 24.05 – 24.57
\nSpot Rate : 0.5200
\nAverage : 0.3215<\/p>\n

YTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 23.74
\nEvaluated at bid price : 24.05
\nBid-YTW : 5.12 %<\/td>\n<\/tr>\n

BAM.PF.E<\/td>\nFixedReset<\/td>\nQuote: 23.25 – 23.68
\nSpot Rate : 0.4300
\nAverage : 0.3025<\/p>\n

YTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 22.88
\nEvaluated at bid price : 23.25
\nBid-YTW : 5.00 %<\/td>\n<\/tr>\n

GWO.PR.Q<\/td>\nDeemed-Retractible<\/td>\nQuote: 23.60 – 23.97
\nSpot Rate : 0.3700
\nAverage : 0.2459<\/p>\n

YTW SCENARIO
\nMaturity Type : Hard Maturity
\nMaturity Date\t: 2025-01-31
\nMaturity Price : 25.00
\nEvaluated at bid price : 23.60
\nBid-YTW : 6.19 %<\/td>\n<\/tr>\n

IFC.PR.A<\/td>\nFixedReset<\/td>\nQuote: 19.55 – 19.85
\nSpot Rate : 0.3000
\nAverage : 0.1978<\/p>\n

YTW SCENARIO
\nMaturity Type : Hard Maturity
\nMaturity Date\t: 2025-01-31
\nMaturity Price : 25.00
\nEvaluated at bid price : 19.55
\nBid-YTW : 7.79 %<\/td>\n<\/tr>\n

IFC.PR.F<\/td>\nDeemed-Retractible<\/td>\nQuote: 24.80 – 25.22
\nSpot Rate : 0.4200
\nAverage : 0.3247<\/p>\n

YTW SCENARIO
\nMaturity Type : Hard Maturity
\nMaturity Date\t: 2025-01-31
\nMaturity Price : 25.00
\nEvaluated at bid price : 24.80
\nBid-YTW : 5.44 %<\/td>\n<\/tr>\n

BAM.PF.F<\/td>\nFixedReset<\/td>\nQuote: 24.41 – 24.80
\nSpot Rate : 0.3900
\nAverage : 0.2950<\/p>\n

YTW SCENARIO
\nMaturity Type : Limit Maturity
\nMaturity Date\t: 2048-06-15
\nMaturity Price : 24.00
\nEvaluated at bid price : 24.41
\nBid-YTW : 5.08 %<\/td>\n<\/tr>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

The New York Fed has released a paper by Andreas Fuster and James Vickery titled Regulation and Risk Shuffling in Bank Securities Portfolios: Bank capital requirements are based on a mix of market values and …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[],"class_list":["post-49413","post","type-post","status-publish","format-standard","hentry","category-market-action"],"_links":{"self":[{"href":"https:\/\/prefblog.com\/index.php?rest_route=\/wp\/v2\/posts\/49413","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prefblog.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prefblog.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prefblog.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prefblog.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=49413"}],"version-history":[{"count":4,"href":"https:\/\/prefblog.com\/index.php?rest_route=\/wp\/v2\/posts\/49413\/revisions"}],"predecessor-version":[{"id":51209,"href":"https:\/\/prefblog.com\/index.php?rest_route=\/wp\/v2\/posts\/49413\/revisions\/51209"}],"wp:attachment":[{"href":"https:\/\/prefblog.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=49413"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prefblog.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=49413"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prefblog.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=49413"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}